Free Case Study On Review Of GIS In Housing And The Effect Of Educational Level

Abstract
Shelter is one of the basic human necessities. Provision of decent-quality and affordable housing is one challenge that various authorities have been faced with, and Sheffield is not left behind. Though she has surpassed her average annual housing needs, Sheffield is still struggling to mitigate housing problems. This research project reviews the use of Geographic Information System (GIS) in Sheffield’s housing and how education level affects the adoption of GIS in affordable housing initiatives. In order to achieve this objective, the current state of Sheffield’s housing is exhaustively considered. Using the boundary data from EDINA and the current housing state, ArcGIS 10 has been used to generate maps that relate different housing variables. Questionnaires were also administered to the local councils in UK to aid in realizing the above aims. The analysis of the maps depict that, indeed the potentials of GIS can be optimized in Sheffield’s housing initiatives. The Statistical analysis also reveal the possibility of adopting GIS softwares in housing as most of the council employees have the required knowledge and experience in these softwares.

Chapter One
1.0 INTRODUCTION
1.1 Background of Study
Housing, in the human context, refers to the provision of a human being or beings with a physical structure which is constructed with the primary purpose of providing the occupant with security from the vagaries of weather, scum of the earth and other organisms; and with the secondary purpose of providing comfort and privacy to the resident. The quality of housing is thus determined by these factors. Some housing units, like shanties, provide barely the most basic of these while others, like palaces, provide all these. And in all known human societies, housing along with food is considered a primary need. With complexity of the current human society, much has changed. But much has equally remained the same.
In today’s complex human societies, the need to provide decent housing to populations is a challenge faced by governments and other concerned authorities the world over. The ostensibly disparate political, social and economic factors at play make the task even more daunting. For though pure political and social considerations demand that all social classes and geographic regions be provided with housing of more or less similar costs and quality, economic and geographic considerations negate this. Yet, like in any venture, the concerned authorities cannot adopt an “either-or” attitude. The authorities must take the arguments from all sides into account and arrive at a compromise that is socially sound and economically prudent. More importantly, their decisions must always be based on scientifically collected data and facts, not on emotions and populist sentiments.
Though she has surpassed her average yearly housing needs of 1,425 net additional houses over the period extending from 2008 to 2026 (DCA 2007), Sheffield, like many other administrative units in the world, faces the problem of housing. Though official figures suggest that well above half the population report adequate satisfaction with their housing and that the number of those considered as homeless is about 10 in a population of more than half a million (DCA 2007), the quality of some of these houses, especially the ones in private hands, is questionable. Additionally, the cost of the housing units are exorbitant and above the reach of most Sheffield residents. It is of note that Sheffield may be unique in at least one way. On the economic front, it recorded a decline in the part of the 1970s, the 1980s and part of the early 1990s. This explains the decline in Sheffield’s population from 570 000 in 1974 to 512 242 in 2002 as out-migration became inevitable to most people during this recession. It is also significant to note that Sheffield’s population has been on the rise since 2002 and is projected to reach the 560 000 mark by 2029 according to Dr. Roland Lovatt in a report titled Developments in the Sheffield Population. A survey carried out in 2007 further reveals that the net in-migration to Sheffield stood at 2419 putting even greater pressure on the housing demands of Sheffield. It is also of note that the number of households in Sheffield has been on the rise since 1981. Since this rise in the number of households corresponds to a period when the net Sheffield population was decreasing implies that households have been having fewer members since 1981; that is, there has been smaller households since 1981. With such and other figures – some contradictory on face value – it is clear that housing initiatives in Sheffield cannot be based on simplistic methods but on comprehensive multi-faceted approach taking into account the geography, economy and demography of Sheffield.
As already intimated, good housing initiatives should take scientifically collected facts and figures into account. One tool that is used extensively in planning – from urban planning to healthcare to infrastructure development – is the Geographical Information System (GIS). GIS is such powerful a tool due to its ability to capture real world data and figures, even natural phenomena and do simulation using computer programmes in such a way that analysis and review is made much easier.
Given the immense success of GIS in many areas, this study primarily intends to find out whether these vast potentials of GIS can be used to better the outcome of housing initiatives, specifically in Sheffield. The research questions, thus, are;
1. Is it possible to adopt GIS technology in Sheffield housing initiatives?
2. Does the required expertise to implement the adoption of GIS in Sheffield housing initiative exist and how does education level affect it?
The first question is answered by reviewing the relevant literature on the applications of GIS in related fields and by considering what other scholars have said. In addition, by entering the required parameters in ARCGIS 10 and doing simulation (in addition to administering questionnaires on policy makers), we will be able to observe the strengths and weaknesses of GIS in this specific task. The other question will be answered primarily by the questionnaire which will be sent to at least 325 of the 433 local councils in the United Kingdom.
1.2 The Aims and Objectives
Based on the research questions, it follows that the three objectives of this study are:
1. To determine how GIS can be adopted in affordable housing initiatives
2. To examine and consider if there is enough expertise in the UK 433 local council in the adoption of GIS in affordable housing initiatives
3. To analyze how the level of education encourage the adoption of GIS in housing department in the affordable housing.
1.3 Geographical Information System (GIS)
Decision making based on geography is very basic to every human thinking. Understanding geography and how we relate to locations is very important especially in making informed decisions about how we live on earth. A geographic information system (GIS) is an important technological tool in comprehending geography and in making such intelligent decisions.
GIS is a tool that simulates real world data and figures (including physical ones) for easy analysis by the end user. It is a highly powerful, successful tool and is used extensively in planning. Of interest in this study is the spatial modeling capability of GIS. This capability makes it possible to relate the demographic changes and variations with the unique housing needs of people in different locations. Since localities are often associated with certain social classes, ethnic groups and age groups, the simulation generated by GIS software can be of great importance to the authorities concerned with the issue of housing. In Sheffield, for example, an impressive 86% of residents say they are either very satisfied or fairly satisfied with the conditions of their houses. If taken on face value, this figure can be grossly misleading since it does not cut across all regions of Sheffield, neither does it apply equally to both privately and council owned houses. By feeding all the determining factors into a GIS software, this study intends to show that a much more accurate and representative picture can be obtained; a picture more appropriate for housing planning and initiatives.
1.4 Introduction to GIS and Housing
Geographic Information System is mainly considered as a mapping tool which can delineate land parcels. Maps alone can only provide the contextual information or project the possible results of a sustained analysis e.g. the demand pressure; however, in the GIS context, maps are basically a display device: the database assumes the information storage function of maps while a body of spatial analysis software replaces the map’s traditional role of supporting measurement.
A database, in itself, is tremendously useful and may contain the information referring to a particular property within a local authority ownership; however, the use of GIS adds value to the database in that it shows the actual location of the property and allows the interrogation of the map in order to reveal the underlying database records. In a situation where several attributes of the individual property are to be exhibited at the same time, the main requirement is a wide and richer range of cartographic options. In this manner, there is the possibility of showing, say, the location of the properties whose occupants are in receipt of Housing Benefit. Through the use of co-ordinates, GIS can be used in determining the properties that fall within a given potential area for spatial treatment. Such operations can be repeated at a higher level of geographic areas instead of individual properties. A map can be used in illustrating the variation in pressure of demand for the social housing from place to place.
GIS possibilities are endless, and this gives it the strengths and weaknesses. It is important that we not only be realistic about the capacity and capability of any software package, but also identify from the wide range of all the possibilities an appropriate and most effective approach to analyzing the housing needs.
It is worth noting that in other areas, for example healthcare and general urban planning, where GIS has been used, it has proved highly successful. It has led to building of schools and hospitals and other social amenities where they really ought to be and not where partisan and, sometimes misguided, political interests want them to be. This, obviously, lowers the risk of white elephants projects and thus better accountability for public money which should be the goal of any public office (or officer) anyway. This research intends to find out whether GIS can be adopted to help in housing planning for our time and the future.
1.5 Limitation of the Study
This study will be limited to housing in Sheffield but for the questionnaire which will be administered to at least 325 local councils across the UK. It seeks to find out how GIS can be adopted in Sheffield’s housing initiatives and determine whether there is the required expertise for its adoption.
1.6 Problems envisaged in this research
Like is always with human endeavors, challenges are inevitable in research projects. Some are spontaneous while others can be foreseen and remedies sought where they prove unavoidable. In investigating the research questions and meeting the research objectives, the problems encountered in the study include:
1. In some cases, there was difficulty in obtaining the requisite literature and data. Some of the information were considered by the authorities as highly sensitive and could not be revealed even for the purpose of learning.
2. Official data from the authorities tend to exaggerate the strong points while ignoring the weak ones. This may have a negative impact on the research findings of this study since official government data informed a major part of this paper.
3. As is the case with questionnaires, cases of reluctance, even outright refusal, to give answers were encountered.
4. The issue of logistics, especially with regard to administration of questionnaires was a challenging and expensive one.
Certain mitigating actions were taken to offset the impacts of these problems. For example, caution was taken when dealing with data from the authorities. Where the authorities made the bold claim that Sheffield’s population would be 561 300 by 2029, the study took into consideration the fact that both the economic and social factors that may engender the attainment of such a figure tend to change radically and that such a change has the impact of affecting this projected figure significantly.
In addition, though attempts were made to get as much requisite data as possible where crucial data was unavailable, this report acknowledges as much. Moreover, despite the logistic challenges encountered as well as that of reluctance by some respondents to answer the questionnaires, adequate amount of data was collected for this study. With the required data, it was possible to investigate the research objectives and answer the research questions.

Chapter Two
2.0 LITERATURE REVIEW
2.1 Overview
This chapter explores and revises the existing literature on Geographic Information System and housing. It tends to investigate how GIS can be adopted in affordable housing initiatives.
2.2 Affordable Housing
A decent-quality and affordable housing (defined as a housing that consumes less than 30% of the family’s total income) enables the family to enjoy improved life outcomes on such dimensions as family stability, household wealth, labor market participation, neighborhood quality, mental and physical health, and educational achievements (Rohe, McCarthy and Van Zandt 2001). In addition, decent-quality and affordable housing contributes to the improvement of physical, environmental, economic, and social health, which defines the sustainability of communities. This is especially important for the lower-income households and the underserved populations.
Where an individual stays (home) is a gateway to services, educational, and employment opportunities, health, and social services. Home is a reflection of who the occupants are, and is an influence on who they can become. Making a spatial decision for affordable housing concern where middle and low families live in the city and what they can facilitate to get from the city. Affordable housing policy in UK has undergone a development in the recent past.
According to the UK government, Social housing is needed to provide affordable homes in terms of rent with security of tenure for the families on low incomes, people with severe disabilities, frail older people, and for other people for whom home ownership is unlikely to be the right option (CLG 2007). It is the role of the government to assist the vulnerable and to ensure that everyone has access to a decent home at a price they can afford (King 2008). Social housing is not for everyone, rather to assist those who cannot afford to own decent homes. Policy-makers have never intended to make social housing available for all households or for a majority, as in the case with health and education in the UK (King 2008).
2.3 How Geographic Information System (GIS) can be adopted in Affordable Housing Initiatives
A GIS is a form of information system that combines geographically (spatially) referenced data, and non-spatial attribute data. It is capable of storing and managing the political boundaries of regions, in addition to the attribute information. This enables one to identify and view each region on the surface of the earth. In addition, GIS analysis functionality allows for neighboring regions to be identified through boundary evaluation. This is the major contrasting feature of GIS, because geographic extent is an explicit and important component of all information being stored, managed, and processed. Thus, GIS may be considered a hybrid information system that structures data and summarizes features based on the inherent characteristic of the data being managed (Church and Murray 2009).
Many researchers have identified the significance of GIS in urban policy initiatives that have a spatial component, like the housing initiatives, informal settlement upgrade, selection of appropriate site for affordable housing, and neighbourhood revitalization among others. These initiatives are currently being implemented by policy makers and governments the world over by exploiting the excellent capabilities of geographic information system in the form of spatial decision support system of which the main engine is GIS (Beer and Baker 2000; Batey and brown 2007). In affordable housing initiatives, GIS can vastly be used in site selection and in policy formulation, as discussed hereunder.
2.3.1 Site Selection
Housing is fixed in a geographic space, and therefore, the importance of the environment is beyond any reasonable doubt. The location of a house (geographic location) is a major determinant of such activities as shopping, recreation, and access to employment, proximity to environmental amenities, and the level and quality of public services. The household residential satisfaction and the patterns of household mobility are majorly determined by the geographic location. The location choice results in the geographic segmentation of housing stock along such dimensions as quality, price, type, and ownership as well as household characteristics like income, ethnicity, race, and lifestyle (Rohe, McCarthy and Van Zandt 2001). GIS can therefore be used in identifying the low-income sites for affordable housing initiatives.
In many business practices, the importance of geographic location is inherent concerning housing supply, financing and marketing. For the real estate practitioners, location is of utmost importance as it determines the premium which households are willing and able to pay for the comparable properties. The locational factors and the past trends in sales are used to determine the market value of properties. For the mortgage lenders and insurers, the geographic location of the property for such purposes as loan security is a major determinant of the credit risk exposure. For the policy makers, the main interests are the programs to neighborhoods where desirable outcomes can be derived from the housing investments.
For the planners to perform their tasks effectively, it calls for the integration of the socioeconomic characteristics of sites and the constraints of physical layout, available area, and land suitability. In housing and urban planning, one main advantage of GIS, especially in rapidly growing areas, is that the combination of digital map and database information allows for great flexibility in assessing alternative scenarios, making GIS an important tool in selecting the low-income sites for affordable housing initiatives. Unfortunately, compiling an urban GIS takes a major resource commitment in time and funding.
Zhang and Li (2009) explored the vast capabilities of GIS in affordable housing initiatives in Thailand- through the model approached using spatial decision support systems to analysed geographic data towards aiding decision making. For any affordable housing project, low-income site is the initial consideration since housing is any structure attached to land, and low income people are those whom the conventional housing market marginalized. In locating the low income site in the area of interest, (Zhang and Li 2009) proposed that the low income sites values be collected to form a database in the area of interest. In addition, other criteria such as accessibility are to be analysed against the low income sites in order to arrive at decisions that meet the objectives of low income housing. In selecting the low income site for housing, Zhang and Li (2009) considered the different site values and size of land in square metres, amongst other criteria, and created the polygons of these data in GIS which is the basis for spatial analysis towards decision making on the best affordable size.
Site selection also employs the use of spatial overlay technique, which is one of the principal functions of GIS. The technique tries to extract useful information from geographic data distributed across space by joining and viewing together separate dataset that share all or part of the same characteristics on a digital map in a stack of transparent layer. Overlaying each thematic map against one another to determine between the best matches on the map gives an insight on the best alternative sites for the affordable housing (Demers 2005).Spatial overlay tries to study the spatial relationship that exist between geographic data so that it can be understood and predicted.
Another technique used in site selection is the Digital Elevation model (DEM), which is basically a topographic map in digital form obtained from various sources such as from remote sensing, ground surveys, and photogrametry among others. DEM shows the surface parameters of the landform in terms of height, slope, curvature, gradient, aspect and contours (Podobniker 2009). It is possible to model the DEM in order to reflect the varying land values of a given location or neighbourhood using a variety of analytical means such as visualization and statistical analysis to enhance decision. Visualization is a powerful tool and has been the traditional role of GIS; its weakness lies in the fact that it is qualitative in nature and can neglect some hidden fact unlike the statistical method, which is more objective and quantitative. Be that as it may, it can be adopted to enhance decision making process in housing initiatives.
Land values in the area of interest can be obtained through varieties of spatial analysis techniques. Policy makers would be happy to commit resources to the best sites for affordable housing that meets the requirements in terms of the topographical parameters and land value, and housing DEMs can be useful in this area of analysis to aid decision making. In this regard, 2-Dimensional visualization or 3-Dimensional visualization can be of great help. Low income land/housing DEM maps show the relationships between spatial locations and land/housing values enabled by the powerful capability of visualization, and guides the policy makers on where to identify the relevant locations against the topographic parameters and how to calculate what to pay (Li et al 2009). This approach has been adopted successfully by the housing authority in china in providing affordable housing for the low income population in the urban areas.
Remotely sensed data are been increasingly used for low income site selection process through the analysis of land cover. Satellite image data are been adopted because of their relative cost and timesaving and the broad range of land cover information from which other land use information can be extracted for decision making purposes. Remote sensing enables data to be captured and gathered without being in direct contact with the object (Lilesand, kiefer and Chapman 2008).
Factors Influencing Site Selection
In selecting the appropriate sites for affordable housing, the factors that are considered include the location, the physical environment, and the accessibility characteristics. These factors may result in positive or negative externalities on residents. Of these factors, accessibility plays the major role. Physical environment deals with the physical characteristics of the house and the proximity to the environmental hazards. The neighborhoods differ in types, levels, and qualities of public service offered, and are stratified on the grounds of social, economic, and demographic features.
1. Location
Neighborhood plays a very important role in social and economic outcomes for individuals, the associated housing market, and the institutional behavior. According to Goodman (1989, 53), this is a major research area in analysis of housing initiatives. Housing has two unique qualities that link housing purchase and residential satisfaction to the geographical location of the housing. The two qualities are special fixity and durability. In addition to these physical characteristics of housing, neighborhood characteristics enter into housing bundle due to geographic location of the house.
The role of geographic location can be looked at in two ways in terms of the individual and market level behavior and outcomes. One form involves the localized externalities associated with the location or site of the house. These externalities form the adjacency effects as they capture the spatial spillover effects. As an example, a dump site is a source of negative externality to the adjacent properties. The situation or the overall neighborhood characteristics like accessibility and socioeconomic context, among others, greatly affect the decision making and the resulting market outcomes.
A neighborhood can be defined as a discrete special entity or a physical area that contains the households and the housing structures that have similar characteristics. Typically, households within neighborhoods exhibit similar characteristics (social, economic, demographic, among other characteristics). The similarities in the housing structures are observed in the tenure type i.e. owner-occupied or rent-occupied; the ownership i.e. private or public; the type of structure i.e. single-family or multifamily; and the design i.e. town house, rambler, or colonial; together with the general quality of the stock. The extent of the similarity, i.e. the spatial continuity among the households and the housing units, varies across the neighborhoods resulting in the homogenous difference among the neighborhoods.
2. The Accessibility Characteristics
The geographic position of a neighborhood or a house determines access to opportunities like employment and transportation. Zhu, Lui and Yeow (2005) stated that accessibility play a significant role in housing planning as it determines the opportunity that the occupant is exposed to in a given location, such as access to communal facility and services like hospital, post office, school, etc. The microeconomic theories of the land use, the residential location, and the resulting house prices or rent are based on the differential access to workplaces (Alonso 1964; Muth 1969). This explains the major spatial regularities that are observed in the allocation of the urban residential land e.g. near the city centre, there is the high-rise, high-density development, while in the peripherals, and there is the low-rise, low-density development. According to Straszheim (1975), differential access to workplaces, as the foundation of the economic theories of land use, is insufficient in investigating the spatial detail in housing characteristics at different locations. Straszheim postulates that the limitation given above is the major motivating factor for econometric investigations of the housing markets.
The limitations in the accessibility to the employment opportunities in the suburban from the inner-city neighborhoods have serious economic impacts to the low-income earners and the disadvantaged households. However, neighborhoods with easier access to the public transportation are very much advantaged. Other than easy access to jobs, the location of neighborhood determines the general access to amenities. Easy access to facilities like parks, shopping malls, medical centers, public libraries, and recreational facilities has positive impact on the residents.
In relation to housing initiatives for low-income households, accessibility is viewed from the perspective of travel cost over time, which the low-income households would be able to maximize if they lived closer to public transport routes, and travel in a less amount of time. In considering affordable housing initiatives, accessibility is one of the major factors as it directly influences house affordability in terms of rent.
3. The Physical Environment
Depending on the historical patterns of development, there is a substantial variation in the physical characteristics of the residential stock across the urban landscape. Neighborhoods are differentiated according to type, density, the architectural style, and the landscape’s physical characteristics. The construction technology, the prevailing economies of scale, the ease and cost of land assembly, and the customer preference determine the degree of spatial clustering in the production of housing through housing type. Majority of the new housing are constructed on urban periphery and there is a substantial decrease in the dwelling age in concentric style moving outwards from the centre of the city. With the depreciation of the quality of the housing stock due to aging process, there emerges the importance of rebuilding and revitalization in altering spatial distribution of physical stock. The topography and physical characteristics of certain neighborhoods is a source of negative externalities and low demand for housing. The proximity to sites like airports, toxic waste sites, highways, and subsidized housing projects are all determined by the house location. The landscape’s physical patterns and the accessibility of houses, the housing production, and the redevelopment are the key sources of social and economic variations in neighborhoods within the urban areas.
Regardless of the impeccable importance of geographic location for policy, business, and regulatory policies, its integration into housing initiative research has been limited. There are few studies which have applied the special analytical tools in examining the location effects on housing prices (Pace and Gilley 1997; Can and Megbolugbe 1997; Can 1990, 1992b; Dubin, 1992), in mortgage market outcomes (Anselin and Can 1995) and in population density models (Griffith and Can, 1995). Lack of the appropriate software tools, inadequate availability of comprehensive and accurate information on housing and locations, and lack of research environments for the computation and facilitation of geoprocessing needs of data have greatly formed the major hindrances to the housing initiatives.
With the GIS technology, there is significant contribution towards overcoming the operational impediments that forms the major hindrances stated above. GIS capabilities extend far much beyond facilitating the organization and management of geographic data to enabling the researchers in taking full advantage of the locational information contained in the databases. The GIS research infrastructure in conjunction with the recent advances in special research offers vast opportunities for investigating the mortgage market research and the housing initiative. Depending on the research application purposes, the commercial analytical functionality of GIS should be complemented by the external analytical tools (Anselin and Getis 1992; Anselin, Dodson, and Hudak 1993). External Software tools like SPSS have therefore been developed and are either interfaced with or embedded in the existing GIS software environments (Can 1992a, 1996).
2.3.2 Housing Policy Formulation
The Struggle for a decent home and the desire for the shelter, comfort, privacy and independence that a house provides, are familiar the worldwide. Nonetheless, not everyone can obtain the housing at the market prices. The local governments thus supply the affordable houses to those with the inability to obtain access to housing at market prices (Brian 2006). Many countries provided affordable housing in different ways to middle or low classes, e.g. council housing in U.K., section 8 housing in U.S., and public housing in Singapore.
The UK housing policy has two dominant themes: the choice and the affordability. Households should be able to make choices about where they live, and this should apply regardless of tenure: social tenants should be able to choose just as much as owner-occupiers should. In the same way individuals chose from a wide range of products in the supermarkets, housing should not be an exception (DETR 2000). Everyone should also have access to a decent home at a price they can afford (CLG 2007).
Access to affordable housing is a concern for policy makers in many developed countries (Barton et al. 2005). Most of the housing policies are formulated based on the accessibility analysis, especially the housing planning and the facilities and service planning. Soles (2003) used the accessibility as a measure to evaluate the housing needs in North Saskatchewan, Canada. In this evaluation, emphasis was put on accessibility to transportation and community facilities, and appropriate policies developed. Lee et al (2002) in the case study in Portland, Oregon, explored the concept of policy formulation based on accessibility in the context of access to healthcare services. Guy (1983), on the other hand, considered the policy formulation in terms of accessibility to shopping opportunities from the supply-side and demand-side perspectives. Another important study is Halden et al (2002) where accessibility was used as a criterion to measure the level of service provision in the rural areas of Scotland. In this study, they examined the travel-time and analysed the patterns of accessibility to urban centres, shopping opportunities and to regional health care facilities. This study resulted in a policy review in Scottish Executive to prioritize service provision. The services included employment, public and community transport, health and social care, information and advice services, education and training, shops, banks, post offices, libraries, community halls, sports and leisure facilities, childcare centres, chemists, and local government offices.
According to Johnson (2006), housing initiative and sustainable development is divided into three: the descriptive research, the prescriptive research, and the decision support system. The descriptive research is where the results are used to support the specific strategies or policy initiatives; the prescriptive research is where the results, based on the most preferred set of alternatives, are assumed to be determinative; and the decision support system generates policy recommendations based on information technology applications that uses both descriptive and prescriptive research. The use of GIS falls under decision support system, which I undertake to explore.
Geographic Information Systems technologies and Spatial Decision Support Systems have made essential contributions to site selection and local and regional planning. Population flows can be used in formulating multiple partial differential equations and the transient solutions of the equations used in evaluating different public housing policies and model validation against the real-world geographical data (Nikolopoulos and Tzanetis 2003). According to Caulkins et al. (2005a,b), a single-state model of housing mobility can be formulated in a stylized metropolitan area. In such a model, the level of the middle-class families forms the state while the level of the poor families introduced from the high-poverty neighborhoods as part of a policy initiative forms the control variable. The solutions to the optimal control model where there is maximization of the discounted net present value subject to the differential equation describing the system dynamics allows the identification of both stable and unstable long-term equilibrium associated with housing policies.
In some of the prescriptive housing models, the motivation directly comes from the specific policy initiatives in the specific geographic regions; however, the specific spatial characteristics of the study area are of less importance, or even unimportant in some cases. According to Frech and Thyagarajan (1975), formula-based allocations and gravity models can be used to derive the proposed allocations of the affordable housing to sections of metropolitan statistical area. Households can also be located to zones so that the housing costs and the commuting costs can be minimized (Kim 1979). This helps in generating potential allocations of housing (low-income housing) that satisfies the requirements of the fair housing policy. Alternative potential allocations of households can also be done through the use of rental vouchers to Census tracts across a city or a country to optimize jointly the measures of net social benefit and equity (Johnson and Hurter, 2000; Johnson, 2003). A multi-objective model can be used for the location of project-based subsidized rental housing so that the social efficiency and equity measures are optimized (Johnson 2006c). Johnson (2006b) proposes a general model for affordable housing that can be developed by either a non-governmental organization or a public housing authority. Spatial concerns can also be ignored (Tiwari, Parikh and Sharma 1996) in construction process for affordable housing. The level of production activities should be chosen in order to minimize the total costs subject to the constraints on both input and output levels, construction technology requirements, and the environmental impacts.
Another team of researchers has developed regional planning strategies using representation of programs and planning units. Programs for urban renewal that assign the specific building types, the levels, and the prices to all the land parcels in the study area , have been developed by Atkins and Krokosky (1971) using a simulation model, and then choosing the solution that optimizes user-defined measures of net social benefit. According to Gabriel, Faria and Moglen (2006), a planning problem for smart growth can be solved using actual, non-uniform land packages and multiple objectives that reflect the opinions and or perspectives of the government planner, the environmentalist, the land developer, and the conservationist. Production scheduling programs can also be formulated so that the design policies that minimize the total development time are arrived at (Kaplan, 1986; Kaplan and Amir, 1987; and Kaplan and Berman, 1988).
It is important to note that the operational models for real estate are relatively rare. Kaplan (1987) has used the queuing theory to evaluate the effects of both race-based and non-race based policies in tenant assignment of public housing on the levels of racial segregation and the waiting times for the available units.

Chapter 3
3.0 Sheffield City
3.1 Introduction
This chapter takes a digestive look of the Sheffield City. Essentially, the context of the city is looked into with respect to its location, coverage area, level of population and types of economic activities carried out. In addition, parameters like type of education the city provides and population pattern have been explored to help understand the city better. In regard to the housing in Sheffield, the main reference material is the DCA’s “Strategic Housing Market Assessment Report of 2007”. This report brings into focus the local housing market, and explores and evaluates the housing situation in Sheffield. Another document of importance, which Sheffield’s housing literature has also been sourced from, is the “State of Sheffield 2010.” Because housing and population are inseparable, it is of great importance to consider the Sheffield’s population and the “Developments in the Sheffield Population report” has greatly helped. A good number of resurgent literatures have also been explored to help understand the context of the city.
3.2 Location and coverage
Sheffield (Figure 1) is a city of England, UK. It is located within the South Yorkshire sub region. Its set up is urban in nature, but with scanty rural settlements to the North and West of the city. Even though its local authority stretches out within the Peak District National Park, the mass of the population resides within the more urban areas. It is located North and North of the equator respectively, famously known as South Yorkshire and the climatic condition is mostly temperate. The city covers a total of 36,800 hectares. It is surrounded by Rotherham to the East and Barnsley to the North. Derbyshire Dales, High Peak, and North East Derbyshire also surrounds Sheffield.

Figure 1: Sheffield City: Location and Wards

3.3 Population
Sheffield is England’s fourth largest local authority in terms of population (Winkler 2007). According to Dr. Roland Lovatt (2007) in the report “Developments in the Sheffield Population,” Sheffield’s population started increasing in 2002 and in 2005, the population was 520,700. By the year 2029, the population is projected to rise to 561,300. Based upon the ONS (2004) population change estimates between 2004 and 2029, the Sheffield’s population is ageing. The population between the age 0 and 14 was projected to shrink until 2011, which has been the case; it is projected to rise from 2014. In the next 20 years, the population size between the ages 65 and 79 is projected to increase by 20 percent while that of 80 years and over is projected to increase by 40 percent. It is also worth noting that the size of the Sheffield’s student population is in the rise. In 2005, the city had 40,000 students compared to 32000 students in 1996. Generally, ONS projections propose that in the next 20 to 30 years, Sheffield’s population size shall have an overall increase. The projected increase in population necessitates an increase in housing needs.
The city’s in-migrants majorly come from Yorkshire. The Sheffield’s in-flow of households from Rotherham contributes to 6.3%, and the in-flow of households from Barnsley contributes to 4.3%. Considering the surrounding influence, the 2003 Rotherham Housing Needs Survey discloses that 28% of the households moved to Rotherham from Sheffield and some to Doncaster and Barnsley. In addition, the survey reflects that 9% of the existing households were planning to move from Rotherham to Sheffield. 15% of the concealed households also had a similar movement plan.
3.4 Major Economic Activity
Manufacturing is the major economic activity of Sheffield city. The city gained recognition in the 19th century for its manufacturing and production of steel. The city has since embraced the innovations in the steel industry. Besides iron and steel industries, coal mining is also taking center stage, especially in the outlying areas. The intensity of economic activity at Sheffield of 74.5% is to some extent lower than the Yorkshire and Humber region at 78.1% and the national average of 78.4%. The Annual Population Survey indicates that the level of employment in Sheffield is at 92.6%. This has however increased over the past 10 years as indicated by other literature.
3.5 Sheffield’s Housing
After experiencing a long period of decline in population figures (1974 – 2002), Sheffield’s population has been on the rise since 2002. While the decline was attributed to out-migration occasioned by Sheffield’s, then, dwindling economic fortunes, the rise is attributed to in-migration due to Sheffield’s promising economic prospects. With a rising population that is expected to rival the 1974 figure of 570 000 by 2029, there is need to have proper housing policies in Sheffield. This is important more so because the population is rising with a corresponding disproportionate increase in number of households. This lack of proportion is clear from the fact that though the population was in decline in the period between 1981 and 2002, the number of households has been on the increase since 1981. This implies that the number of people per household has been equally reducing since 1981. It is thus a cinch that any increase in Sheffield’s population – as is being witnessed now – will put immense pressure on Sheffield’s housing sector. As at March 2007, the total number of Sheffield’s household was 231,562 as compared to the 2001’s 217,622. This constitutes an increase of 6.4% of the households (an increase of 14,140 households) thus the increasing need to put up more housing.
From various research surveys, the household types in Sheffield keep on changing and this has implications for housing demands. From 1991 to 2001, there has been a remarkable decrease in the number of couples without children. Within the same ten-year period, there was a significant increase in the level of lone parent households and single person households. The lone parent households increased by 9.4% and single person households increased by 1.5%.
Sheffield intends to ease the pressure by having at least 1425 net additional dwellings put up every year for the period between 2008 and 2029. So far, this target is being met – even surpassed – with 2000 net additional dwellings put up yearly. Yet, though Sheffield has exceeded its own target, it still records an annual housing shortfall of 729 dwellings per year. This can be explained in several ways. One is the issue of affordability. Most of 6the houses are beyond the rich of ordinary Sheffield residents. In a Council where vast majority of houses are in private hands, there is need to check on housing costs if Sheffield is to be secure in housing matters. And the issue of affordability is so serious that more and more are applying for social housing. Official figures reveal that more than 90 000 households have already registered for social housing. This just confirms the already repeated phrase that new, efficient, cheaper technologies are required for fruitful housing initiatives in the city.
Housing prices have, of course, decreased ever since 2008 when the credit crunch took its toll. But since the credit crunch affected individuals as well as corporations, the reduced housing prices did little as far as affordability of housing is concerned since individual’s pockets were already dented. In short, the crunch affected all sectors of the economy thus restricting individuals’ access to finance facilities such as mortgages and loans. One of the objectives of this study is to determine if GIS technology can help come up with cheaper ways of providing housing at the right locations for the right people. In a council as diverse as Sheffield, such a consideration is important. The disparate economic, social, ethnic and age groups that comprise Sheffield have different housing needs. This study aims to find if these varying needs can be met satisfactorily using GIS.
The quality or hazard level is also a major factor in Sheffield. Reports indicate that houses in private hands are (or are perceived to be) of lower quality than government owned houses. This despite the fact that an impressive 86% of Sheffield residents recorded satisfaction with the quality of their houses. Whether these houses meet the Decent Homes Standard of being weatherproof, warm and having reasonable facilities is another issue altogether. What is certain is that 92% of government housing meets this standard. This is impressive put against the fact that only 63% of private housing meet this standard and that only 55% of privately rented housing meet the standard already described above.
3.5.1 Sheffield’s Forecast Change in Households (2004-2026)
The Sub-Regional Household Projections for England forecasts that there shall be a remarkable change in Sheffield’s household. The area population and the future changes in household size are seen as the major factors that shall dictate Sheffield’s future developments in housing. The longer life expectancy, the higher labor mobility, the reduction in the inter-generational households, and the increased social aspirations are seen as the major contributors to the decline in the average household size. Table 1 shows the forecast by the CLG.
3.5.2 The Economic Drivers of Sheffield’s Housing
According to Goodman (1989), housing demand depends majorly on the economic developments of the area in the same way as it depends on the population changes. Sheffield city is not an exception. It is approximated that the number of jobs within Sheffield is 255,700. Since 1995, Sheffield’s number of jobs has increased by 20.1%. Considering the regional and national averages, Sheffield has a higher unemployment rate of 7.4%. This necessitates the provision of affordable housing. With the increase in employment levels in Sheffield, there is a high likelihood that the mobile households relocate to the region, putting pressure on the housing stock. Reports also indicate that 27.2% of heads of households are retired and this figure is projected to increase further. The housing needs of the older people therefore calls for a strategic approach.
3.5.3 Sheffield’s Housing Strategy: focused on neighborhoods
The city council of Sheffield has applied neighborhood-based approach in local housing renewal. As the council’s housing stock decays, the national government has substantially funded the council through Decent Homes and Housing Market Renewal Programmes (Sheffield Homes, 2007; Winkler 2007). Through the Housing Market Renewal (HMR) Programme of 2002, Sheffield got substantial funding for the improvement of her most deprived neighborhood’s housing. This was part of the central government’s policy to lobby work of core cities group to improve the housing markets in the northern cities of England which have experienced low levels of market demand.
The programme attempts to either upgrade or clear low demand property. One of Sheffield’s major problems has been an oversupply of social housing coupled with a mismatch between local people’s housing aspirations and the type of housing available. Over one third of Sheffield’s housing (63,000 homes) is classed as in need of ‘market renewal’ and is therefore included in the programme area. Over a 15-year period, this housing will receive part of the £2.5 billion of investment designated for the South Yorkshire sub-region (Sheffield City Council 2004, p 19). By 2006, the HMR programme had already invested £100m in Sheffield (EKOS, 2006, p 7).
The Council has developed three Area Development Frameworks (for North, East and South Sheffield), and Masterplans for the nine housing areas included in Sheffield’s HMR programme, as a way of ensuring continuity in spite of the short-term nature of government funding. All these plans were opened to community consultation before being implemented. The Masterplans include action on a wide range of factors affecting neighbourhoods such as housing, education, transport, and local green spaces.
3.6 The characteristics and structure of the housing supply in Sheffield
Highlighted hereunder are facts on the housing supply in Sheffield as given in the Strategic Housing Market Assessment Report of 2007.
In 2001, the number of the housing stock was 217,622 units, and by 2007, the number increased to 231,562. The owner occupation level in Sheffield’s housing is at 59.6% lower than the country’s regional and international standards. According to the 2001 census, 30 percent of the city’s stock is social rented housing as compared to 19.3 percent nationally and 21.1percent across the Yorkshire and Humber. However, the figure decreases and by 2006, it stood at 23.1% for Sheffield. 8,301 residents in Sheffield in the year 2001 lived in 292 communal establishments; however, the figure decreases. It has been noted that in the Council rented sector and in the HA rented sector, there is over occupation as compared to the owner occupied sector. 6.3 percent and 4.5 percent of the households are over occupied in HA rented homes and Council rented homes respectively. Most of the residents consider their houses as adequate for their needs. This is shown by 86.7%. Only 13.3% consider their houses as inadequate.
Technology is dramatically changing the way public affairs are managed and Sheffield should not be left behind in this regard. For though Sheffield records a relatively low, and reducing number of the homeless people (only ten in 2009), what the future portends for Sheffield’s economy is difficult to determine – especially with the unpredictability of the world economy in our time. This coupled with the fact that the recent global financial crunch affected Sheffield in more ways than one means that the affordability of houses in Sheffield is at a low. Indeed, most residents of Sheffield “are now struggling with their mortgage repayments and are in danger of repossession” according to the report State of Sheffield 2010. Good planning will thus be required for the benefit of posterity.

Chapter Four
4.0 METHODOLOGY
4.1 Introduction
The primary aim of this chapter is to provide comprehensive information on the methods of data collection, research design, the data sources and methods of analyzing the data. The statistical techniques used herein are also highlighted. The data obtained are presented on maps using GIS software and analysed through statistical methods in the preceding chapter.
4.2 Research Design
This research was carried out as a case study. The study area was Sheffield City. For the statistical analysis, the entire UK was taken as the study area and questionnaires administered to all the local councils in UK. The case study approach enabled an in-depth and contextual analysis that was deemed fit for the investigation of the study problems.
4.3 Target Population
The target population refers to the group of people or study subjects who are similar in one or more ways and which forms the subject of the study in a particular study. The study targeted a total population of 433 local councils in the UK. This population was aimed at identifying whether there is required expertise in the councils to adopt the use of the GIS as a technology in housing initiatives and how this is affected by the level of education.
4.4 Data Collection Instrument
This study employed the use of both primary and secondary data. The main primary data collection tool was the questionnaires. The questionnaires contained both open ended and closed ended questions. Questionnaires were used to gain the general picture of an investigation of whether there is the required expertise to implement the adoption of the vast potentials of GIS as a technology in affordable housing initiatives in Sheffield, and how education level affects it. The questionnaire contained questions derived from the objectives of the study. The secondary data was obtained from CASWEB, UKBORDERS, and the ordnance survey website of EDINA.
4.5 Methods of Analysis
The study employed two types of analysis in order to realize the objective of the study.
The questionnaires were administered to the 433 local councils in the UK through emails. The councils were informed earlier through phone calls before sending the questionnaires. This was to help the council authorities identify the most relevant individuals in the position to complete the questionnaires with the most relevant information. Some of the information, as the researcher was later made to know, were highly sensitive and could not be revealed, even for the purpose of learning.
GIS analysis was undertaken alongside statistical analysis. The software package ArcGIS 10 was used to run the GIS analysis by mapping the different categories of data that represented different attributes, among which were shared and unshared ownership houses, the Council rented houses and other Social rented rouses. The data categories also represented the housing type such as bungalows, detached houses, semi-detached houses, terrace houses, flat apartments, and tenement houses. The data was worked out as percentages in spreadsheet file for all the wards in the Sheffield city. These figures were then translated into maps in ArchGIS 10 simply by linking the spreadsheet files to the base map. This was done to help identify the distribution of these houses across the wards of Sheffield, thus their availability and accessibility.
A perfect approximation of the actual housing situation in Sheffield was sourced from the Census data, which mainly included the population and distribution of social houses across Sheffield. These houses are provided by the council, Housing Association (HA) and other private developers. The census data was of utmost importance as it gave useful information about population distribution in Sheffield. Another important data that has been used in this study is the boundary data. The data was downloaded from UKBORDERS and the ward map extracted from Edina. A road network data was sourced from the ordnance survey website of EDINA.
Statistical analysis was carried out using SPSS 12.
4.6 The procedure
As stated above, the study employed two types of analysis: the GIS analysis, and the Statistical analysis. The GIS analysis procedure started by defining the geographical space of interest. To achieve this, the files that included the geographical boundaries and spatial features of interest in the format required by the software were sourced. These files are called “shapefiles” in ArcView and ArcGIS platforms; however, they are called differently for different platforms e.g. “data layers” in MapInfo and “coverages” in ArcInfo. Shapefiles contain different attributes, i.e. they may include point features, such as households, schools, or factories; linear features, such as streets, railway tracks or streams; and polygonal features, like county and state boundaries or census blocks, zip codes, and parcels.
The focus of this study was Sheffield and the shapefiles defining the boundary was used in generating the map of Sheffield. The population map was then generated by plotting the population of each ward on the map. Linear features of the shapefiles were used to display the roads within Sheffield. The Sheffield’s map together with the road network was used as the base map for the subsequent mapping. The points denoting all shared and unshared ownership houses/dwellings, the Council Rented Houses, and Other Social Rented Houses were plotted on the base map for the analysis. Also plotted for the analysis was the housing type such as Bungalows, Detached Property/Houses, Semi-Detached Property/Houses, Terrace Houses, Flat Apartments, and Tenement Housing. This gave rise to the maps discussed and analysed in the subsequent chapter.
The statistical analysis was based on the questionnaires. Questionnaires were administered to the 433 local councils in UK and feedback received from 331 councils. Based on the nature of the data, the variables considered and their effects in housing initiatives included level of education, hierarchy within the organization, GIS softwares used by the organizations, level of experience for the GIS softwares, use of remotely sensed imagery in spatial data analysis for housing initiatives, among other variables. With these variables, a crosstab analysis on the level of dependence and correlation of the variables was performed using SPSS 12. This analysis is provided in the following chapter.
The questionnaire and the feedback from the questionnaires are provided in the appendix section of the report. This primary data was very crucial in analyzing the possibilities of implementing GIS technology in Sheffield housing initiatives based on the needed expertise, and how it is affected by education level.

Chapter Five
5.0 ANALYSIS
5.1 Introduction
This chapter presents an in depth and broad analysis of the data. Essentially, the analysis herein takes into account the aim and objective of the research—to establish whether the vast potentials of GIS as a technology can be made use of in housing initiatives. The focal point of the analysis is housing accessibility and affordability. Besides, analysis looks at all social rented, all shared ownership, all council rented, all other social rented, all unshared dwelling among other housing attributes in Sheffield city. Data set analysis categorically employs the GIS spatial procedures like the location base analysis, which has been used to identify the location of houses and their different categories. Different maps have been presented to show the population distribution, all social rent, all shared ownership, all council rented, all other social rented, all unshared dwelling among other housing attributes.
5.2 GIS in Housing and the Effects of Educational Levels
As mentioned earlier, this paper examines the use of GIS in Sheffield’s housing initiatives, and how the education level affects it. The succeeding sections investigate these objectives. First, the potentials of GIS in housing are highlighted based on mapping. This employs the use of GIS software to analyze the accessibility and affordability of Sheffield’s houses. Finally, the last section examines the available know-how on GIS softwares and how this is affected by the individual’s level of education.
5.3 Implementation of GIS in Housing
From the data, various maps were generated, which in turn were used for better understanding of the housing initiatives in Sheffield wards. Each map provides a description of the various attributes that housing is dependent on; the effect of which determines the result of the housing initiatives. The attributes presented in these maps describes the population and the different types of house ownership. Social housing in Sheffield is provided by the council, Housing Association (HA) and other private developers.
5.3.1 The Population
The population information herein, according to the Census, is the population of usual residents. A usual resident is one who spends most of his/her time at a specific address. The population includes those who usually at a particular address and were temporarily away on the census day, those who work away from home on part time basis, and students during the term-time. The information does not include any other person who was present on the counting day, whose address is usual. GIS has been used to give a clear visualization of the population in the map discussed hereunder.

Figure 2: Population of Sheffield City

The map above presents the population distribution in the various wards of the Sheffield city. From the map it is evident that population of Sheffield is high to the north. The map also shows that a smaller part of the city to the south has high population. It can as well be observed that the city is somehow highly populated to the centre of output areas. This has been represented by the the dark brown coloured areas in the map. The city has however low population mostly to the south as shown by the the yellow and light brown colourations in the map. From the map, it is clear that the population of Sheffield city is unevenly distributed. This should thus be one of the major attributes that need consideration when establishing housing initiatives. This is because the different housing initiatives should be such that each initiative is evenely employed as per the population densities in various wards and locations. Determination of population size will thus help solve unrealistic housing initiatives from ward to ward. From the population distribution of every ward, it can be easily established whether housing ownership should be shared or unshared; whether ownership should be detached share ownership; or whether share ownership in an unshared dwelling should be adopted.
5.3.2 Dwellings
The accommodation of a household or the household space can be defined to be a shared dwelling if the accommodation type “part of a converted or shared house,” and not all the rooms, including toilet and bathroom, if any, are all behind a door which only that household can use. There should also be at least one other such household space within the same address, which can be combined to the former household space to form an unshared dwelling. If these conditions are not met, then the household space forms an unshared dwelling. A dwelling can thus consist of two or more household spaces (referred to as shared dwelling) or one and only one household space (referred to as unshared dwelling).
Household Space is defined as the accommodation occupied by only one household (an individual household) or, if unoccupied, then it must be available for only an individual household.
a) All shared ownership houses/dwellings

Figure 3: All shared ownership houses/dwellings

The above map shows the distribution of all shared dwellings in Sheffield. From the map, it can clearly be observed that the concentration of the shared houses is highest at the centre of the output. The concentration however decreases as one move away from the centre towards the peripheries. Generally, shared dwellings are more on southern part compared to the northern part. The western part also has relative lower number of the shared dwellings compared to the eastern part. The northwestern regions of the map has the least number of shared houses.
When analysed against the road networks, the number of shared houses relatively depend on the road network. In the northwestern parts of the map with the least concentration of the shared houses, most of the houses are situated along the major roads. Regions far away from the roads have no houses or very few houses, if any. The main determinant here is accessibility. The nearer the road, the more accessible the house is, also, the further away the house is from the major road, the less accessible it is. The demand for such houses is definitely low and this is the reason for their limited supply in such areas.
Considering the central region of the map where various roads converge, the concentration of the shared houses is highest. The eastern and some southern parts of the map with relatively more road networks have higher concentration of the shared houses. These houses are easily accessible and are common amongst the vast majority. This is the reason for their high demand and thus, high supply in these regions.

b) All Unshared Dwellings
Figure 4: All Unshared Dwellings

Presented in the above map is the distribution of All Unshared Dwellings per ward in the local authority of Sheffield. This is shown by the green dots. From the map, it is clearly seen that the concentration of the unshared dwellings is higher in the southern part as compared to the northern part. The concentration also high in the eastern regions compared to the western regions. This concentration is highest at the central region of the map where various road networks converge. The south eastern part of the map has higher unshared dwellings concentration compared to the south western part. From the centre of the map northwards, the number of unshared dwellings decrease. The north western part of the map has the lowest distribution of the unshared dwellings.
A conclusion is therefore drawn that at the central, eastern, and southern parts of the output, unshared dwellings are readily available. These dwellings are concentrated at the centre of the output where most roads converge. As one moves further away from the roads, the number of the unshared dwellings decrease. This is probably due to accessibility problems. Most parts of the north western part of the output are far away from roads, which brings forth the problems of accessibility thus limiting the number of the unshared dwellings in these areas.

5.3.3 Council Rented Houses
Figure 5: Population in Council Rented Houses

The map shows the population in council rented houses in the wards of Sheffield city. Green colors on the map indicate all council rented houses. It is observed from the map that all council rented houses are concentrated to the eastern region of the map. Also, there is low concentration of all council rented houses to the north and to the western parts of the maps. Concentration of council rented houses tends to be higher as well at the points where the motor roads merge. It can clearly be observed from the map that there is unequal distribution of council rented houses in Sheffield city (North West regions). In these regions, people cannot easily access the council rented houses. The central output of Sheffield city shows that people can easily access council rented houses. This is not the case with the regions at the outer output regions. The maps created by the GIS analysis signify a discrepancy in council rented houses in the various parts of Sheffield city. It is also evident from the map that the independent variables employed in citing council rented houses make these houses unequally distributed across the wards of Sheffield city. This is because most of the council rented houses are crowded in the central output and in the southern part of the city. This study therefore offers an avenue for further research on how council rented houses can be evenly distributed in different wards and regions of Sheffield city.
5.3.4 Other Social Rented Houses

Figure 6: Population in Other Social Rented Houses

The above map shows the population in other social rented houses and the network of the major roads. At the central region of the output where most roads converge, the population of social rented houses is highest. A small part of the south eastern region also has high population in the social rented houses. Far northern region of the output has the lowest population in the social rented houses. South western regions of the map also have low population in these houses. Vast majority of the north western regions have even distribution of population in these houses.
It can be concluded that the highest population in the social rented houses at the central regions of the map is basically due to high concentration of the road network since this is the region where most roads converge. Definitely, these regions are easily accessible. Away from the road network, the population decreases. It is also important to note that this population does not solely depend on the road network. An exception is the south-eastern region, which has only one major road through it, and has a relatively higher population in these houses.
5.3.5: All social housing distribution in Sheffield

Figure 7: Total Social Housing Distribution

Presented in the above map are all the social houses in the local authority of Sheffield. Social housing providers include the council, the housing association, and other social housing providers in Sheffield. As can be seen in the map, social houses are highly crowded to the eastern part of the map (dark-green coloration). They are majorly found in the wards to the eastern side of Sheffield city. They are also concentrated in the central output of the map. However, it is evident that social houses are sparse to the north as well as to some parts of the southern region of the map (light-green coloration). Generally, the south western region of the map has low number of social houses. The western centre of the output presents very low social houses as shown in the map. In the central and southern regions of the map, social houses are dense in number while its counterparts in the north and western region of the map have less social houses.
It is thus clear from the map that social houses are mainly found in the various wards in the central and southern regions of Sheffield city. In addition, those wards, which are in the north and western regions of Sheffield, are noticed to be having low number of social houses. All the maps generated by the GIS software show that social houses are mainly dominant in the central and southern part of Sheffield city. The concentration of this type of housing to the north of Sheffield city is, however, low. On the whole, social housing is not evenly accessed by the population of Sheffield city. GIS technology has thus shown that in offering social housing to the population across various wards of Sheffield city, the population distribution is of importance. This is due to the fact that it assists to comprehend how the demand and supply of such houses can be met.
5.3.6 Bungalows
Figure 8: Sheffield’s Population in Bungalows

The above map shows Sheffield’s population in bungalows and their distribution across the wards. Map A describes the population distribution and other social rented bungalows while map B describes the distribution of shared ownership in bungalows and council rented bungalows.
a) Population distribution and other Social Rented Bungalow (A)
The concentration of the social rented bungalows is highest at the central regions of the map. As one moves from the centre towards the peripheries, the concentration of these houses reduce. The southeastern parts of the map, however, have higher concentration of these houses more than any region other than the central region. It can also be stated that the eastern part of the map has more social rented bungalows as compared to the western regions. The northern regions, specifically the North West regions, have the lowest distribution of the social rented bungalows. It is also observed that the central region with the highest concentration of social rented bungalows, have the lowest population in the bungalows. The southwestern region has the highest population and also high concentration of the bungalows. The northwest region has relatively high and evenly distributed population in the few available social rented bungalows. The uneven distribution of these houses depict that other than population, the availability of these houses depend on a couple of factors. From the previous maps with road networks, we saw that the central region of the map has the highest concentration of road network since it is the point of convergence of most major roads. The eastern regions of the map have good road networks compared to both the western and the northern regions. The northwestern part, however, is the poorest in terms of road network. It can therefore be stated without any contradiction that the distribution of the social rented bungalows majorly depend on the road network. This determines their accessibility, which in turn dictates the demand and supply of such houses.
b) Shared Ownership in Bungalow and Council Rented Bungalow (B)
The map clearly shows that the distribution of the council rented bungalows is higher on the eastern parts of the map compared to the western parts. The vast majority of these houses are found within the central regions of the map, some distance southwards and northwards. The western regions, especially the northwest, has the least distribution of the bungalows rented from the council.
The central region of the map with the highest concentration of the council rented bungalows northwards, has the least distribution of the share owned bungalows. Vast majority of the northern regions of the map have low but even distribution of both the share owned bungalows and the council rented bungalows.
5.3.7 Detached Property/Houses
Figure 9: Sheffield’s Population in Detached Houses

The above map is a representation of detached houses. It is divided into two maps, A and B. Map A shows the population distribution in detached houses including other social rented detached houses while B shows the population distribution in detached houses rented from the council and those under share ownership.
a) Population Distribution in Detached property and in other social rented detached Property (A)
From map A of the output, it is clear that the concentration of the detached houses is highest at the centre of the map. The eastern part of the output generally contains more detached houses compared to the western part. In addition, the southeastern part of the map has a higher concentration of the detached houses compared to the southwestern part. As one moves from the centre westwards, the number of the detached and other social rented detached houses decreases sharply.
The population of the detached houses at the central region of the output is relatively small as compared to other regions of the output. However, this is the region with the highest concentration of the detached houses. The population of the detached houses is highest in the southeastern part of the map. The detached houses in the western part of the output have relatively high population.
b) Population Distribution in Detached Houses Rented from the Council and Under Share Ownership (B)
The distribution of the detached dwellings rented from the council and those under share ownership is highest at the central region of the map eastwards. This distribution reduces as one move from the centre towards the peripheries. There is an average distribution of these houses in the southern parts of the output. The western part of the map has the lowest concentration of the detached rented houses from the Council and Detached Share owned houses; however, their distribution is even.
From the map, it can be seen that the population of these houses is directly proportional to their concentration. The higher the population, the higher the concentration of the houses. Central region has the highest population in the detached houses and also the highest concentration of the detached houses, either rented from the council or share owned. The population in the detached houses is lowest from the centre of the map southwest wards. The concentration of these houses is also lowest in these regions. Northern part of the map, the southeastern part, and some far south parts, have relatively high population. These regions have an averagely concentrated distribution of the detached houses.
5.3.8 Semi-Detached Property/Houses
Figure 10: Sheffield’s Population in Semi-Detached Houses

The above map shows the semi-detached houses. It is divided into two maps, A and B. A shows the population distribution in semi detached houses including other social rented semi detached houses while B shows the population distribution in semi detached houses rented from the council and the semi detached houses under share ownership.
a) Population Distribution in Semi-Detached property and in other social rented semi detached Property (A)
The concentration of semi-detached houses is highest at the centre of the map as reflected in A. The eastern part of the map generally contains more semi-detached houses compared to the western part. Moreover, south eastern part of the output has a higher concentration of the semi detached houses compared to the southwestern part.
Despite the central region having the highest concentration of the semi-detached houses, the population in the houses is relatively small compared to other regions of the output.
b) Population Distribution in Detached Houses Rented from the Council and Under Share Ownership (B)
Semi-detached houses rented from the council and those under share ownership are mostly concentrated at the eastern. As one moves from east westwards, there is a reduction in the concentration of these houses. There is an even distribution of these houses in the southern parts of the output. The north western part of the map has the lowest concentration of the semi detached houses rented from the Council and under share ownership.

5.3.9 Terrace Houses
Figure 11: Sheffield Population in Terrace Houses

The map above shows Sheffield’s population in terrace houses. Map A shows the population distribution in terrace housing and in other social rented terrace houses while map B shows the shared ownership in terrace housing rented from the council.
a) Population Distribution in Terrace Property and in other Social Rented Terrace Property
It can clearly be observed that a better number of Sheffield’s population in map A is in terrace housing and other social rented terraced housing. The map depicts that terraced houses and other social rented terraced houses are concentrated to the central region (the dark brown regions). It is evident that the local authority of Sheffield is packed with terrace houses, as shown at the centre of output areas. The city has however low number terrace houses and other social rented terrace houses in its northern regions as shown in the map by light brown colorations. As well, there is smaller number of this type of housing in some parts of the southern region of the map. it can be concluded that Sheffield city has high concentration of the terrace houses at the central regions basically because of the high population in these regions. In addition, these are the regions with good road network thus easy accessibility. This should thus be one of the major attributes that need consideration when establishing housing initiatives for the Sheffield population. Areas with high population and good road network should therefore have more terreced houses and other social rented terraced houses.
b) Shared ownership in terrace housing rented from the council
Map B shows the population of shared ownership in terrace housing rented from the councils. The black dots on the map represent shared ownership. As can be observed from the map, it is clear that the concentration of terraced housing of shared ownership; but rented from the councils are high in the southern and central part of the map (dark brown regions). The concentration of these houses in the northern regions of the map is however very low (light brown regions). It is thus clear that terraced housing of shared ownership; and rented from the councils is highly concentrated in the central output regions of Sheffield city. Maps generated by GIS basically show that dominance of this housing type is mainly in the central and southern part of Sheffield city; while the regions to the north and some southern parts of the city have insignificant number of terraced houses rented from council, under shared ownership. This is as a result of high population that exists in these regions and better road network that make the demand for these houses higher. Terraced houses, under shared ownership, rented from the council thus remains a consideration that should be made as a major attribute of housing initiatives for the Sheffield population. These houses should be made available in regions of high population and of better road network. Higher population demands more of this housing as opposed to a smaller size population.
5.3.10: Flat Apartments
Figure 12: Sheffield’s Population in Flat Apartment

The map above shows Sheffield’s population in flat apartments. The green dots in map A show population in other social rented flats while those in map B show population in flat shared ownership but rented from the councils.
a) Population Distribution in Flat Apartment and in other Social Rented Flats (Map A)
Map A shows the total number of population in flat apartment and in other social rented flat. As pointed out earlier, the green dots represent the population in other social rented flat. It is clearly evident from the map that the population in social rented flat is high in the central and in the south eastern regions of the map; while to the northwest areas and some regions to the southwest of the map, there is low population in social rented flat apartment. Sheffield’s population to the northwest have little accessibility of social rented flat apartment. The map also signifies that there is unequal distribution of social rented flat apartments across the various parts of output areas of Sheffield city, with central region of the city having more of this housing initiative. To the far northwest end of the city, accessibility of social rented flat apartments is very limited. This is because of low level of population in this area and the poor road network. This is the reason for low number of people in social rented flat apartments in these areas. The map generated by GIS software points out that this housing initiative needs consideration in areas with uneven population distribution. The regions in Sheffield city with high population should have easy access to social rented flat apartments and hence should be provided.
b) Flat Rented from Council and Shared Ownership in Flat (Map B)
Map B shows number of population residing in flats rented from the councils and whose ownership is shared. The green color dots in the map show the number of flat shared ownership. It is clear from the map that flat share ownership housing initiative is concentrated to the central regions of the map as well as to the south eastern parts of the map. Concentration of this housing is however scant to the north western region of the map as well as to the middle north of the map. This implies that in central output areas of Sheffield city, the number of population in who reside in flat share ownership housing initiative is high as can be clearly observed from the map generated by the GIS. However, the same is not applicable to the north western region as well as to the middle north of Sheffield city.
5.3.11: Tenement Housing
Figure 13: Sheffield’s population in tenement Housing

The above figure shows Sheffield’s population in tenement housing. Map A shows the number of people in tenement housing and in other social rented tenement while map B shows the shared ownership tenement housing rented from council.
a) Population Distribution in Tenement and other Social Rented Tenement Housing (Map A)
As mentioned above, map A presents number of people in tenement and in other social rented flat or tenement. The green dots show other social rented tenement housing across the various wards of Sheffield. It is clearly observed from the map that the number of people in tenement housing concentrates to the central and southern regions of the map. There is also dominance of this type of housing in the central output regions of Sheffield city. However, this type of housing is scarce to the northern part of the map as is evident by lesser number of green dots in these regions and also by a reduction in number of tenement houses. It is evident from the map that this housing initiative tends to be dominant in areas of good road network.
b) Shared ownership in tenement housing from the council (Map B)
Map B on the other hand presents shared ownership in tenement housing rented from the council. The green dots on the map represent the population in housing rented from the council. It can be observed that number of shared ownership in tenement rented from council are high in the central and in the south eastern regions of the map (dark-purple colored area).On the other hand, this housing initiative is not dominant to the northwest output areas and some regions to the southwest of the map (light-purple colored areas). The population to the northwest have limited accessibility to the shared ownership tenement housing rented from council. From the map, it is evident that there is uneven distribution of shared ownership tenement rented from council across the different parts of Sheffield city. It is a housing initiative that needs consideration. Areas with high population should have even distribution of shared ownership tenement rented from councils. This will help reduce the problems associated with inaccessibility of this type of housing in areas where population is increasing at a faster rate and hence planning is essential.
5.4 Statistical Analysis
As indicated above, the study area is Sheffield; however, the analysis herein is for the entire UK. 331 of the 433 (76.4%) local councils have been considered.
5.4.1 Methods of GIS Use
Appendix B shows the feedback from the questionnaires, which majorly contains the methods of GIS use, and the familiarity of GIS within organizations.
The duration that the respondents have worked in their respective organizations is shown in table Q1. It is evident that averagely, respondents have worked for 2 – 5 years and thus well conversant with the organization’s operations. The type of GIS implemented for spatial data analysis in every organization is shown in table Q2. They include: Free-standing spatial analysis systems, Loose coupling with stand alone spatial analysis with GIS, Complete integration of spatial analysis with GIS, Close coupling between GIS and spatial data analysis extension, and Embbedding GIS functionality in statistical softwares.
Freestanding spatial analysis does not require a specific GIS system, however it adheres to ESRI’s shapefiles as the standard for storing the information, for spatial data access, for mapping and querying. Loose coupling approaches involve developing data links between commercial GIS software packages and commercial spatial analysis or statistical software packages. The main weakness of loose coupling is clumsiness in data transfer procedures. Its greatest strengths, however, are that they involve a minimum level of programming, combine the functionality of multiple software products, and allow the researcher to use the most appropriate software for the task at hand. Close coupling strategies are based on the loose coupling structure, but involve efficient links and often elaborate user interfaces so that the user may not even realize they are operating within multiple stand-alone software packages.
As shown in the table, most organizations make use of complete integration of spatial analysis with GIS as this account for 33.5% of all the responses. Use of freestanding spatial analysis systems is also common as it account for 10.3% of the total responses on GIS implemented for spatial data analysis. However, the least used GIS analysis system was a combination of both freestanding spatial analysis systems and a close coupling between GIS and spatial data analysis extension. This only accounted for 0.3% valid response of the 331 responses.
Table Q3 illustrates the familiarity of the respondent with GIS or other I.T software. 99.4% of the total response was familiar with the GIS or any other I.T software. This signifies respondents’ massive knowledge on the existence of GIS as only 0.3% was not certain with the software.
The Job description of the respondents in their respective organizations is shown in table Q4. It is noticed that 37.8% of response work as spatial analysts in their respective organizations. This is closely followed by computer technologists that account for 33.2% of total response. Other job roles like development policy research, meteorologists, and regional developers among others only account for 0.3% of the 331 responses. It is significant therefore that most organizations have employees who work as spatial analysts.
Various GIS softwares are currently in use. This study investigated the use of ArcGIS, MapInfo, Envi, MapTime, CAD, among others. ArcGIS is basically a system for working with geographic information and maps. It creates maps, compiles geographic data, analyzes mapped information, shares and discovers geographic information, uses the geographic information and maps in various applications, and manages the geographic information in a database. The Windows desktop softwares under ArcGIS are ArcReader, ArcView, ArcEditor, and ArcInfo.
Of all the 331 responses, 46.5% confirmed their familiarity with ArcGIS software while only 1.2% recognized the ArcGIS, MapInfo, and Geomedia or IDRISI (Table Q5). This signifies ArcGIS familiarity with the respondents. Respondents are also familiar with MapInfo GIS software as it accounts for 34.4% of the valid responses. The level of experience with the GIS softwares is shown in Table Q6. 57.1% of responses had advanced experience for their respective stated software while only 11.5% had basic knowledge. 31.4% had intermediate experience. Generally, it is evident that respondents had advanced experience. The level of experience in using MS Excel is illustrated in table Q8. This was categorized as advanced, basic or intermediate. 59.2% of the total responses had advanced experience, 36.3% intermediate experience and only 4.5% had basic experience. Overall, it is significant that respondents had advanced experience in using MS Excel. Besides, 100% of the respondents use Microsoft office packages (Table Q7). The respondents’ level of experience in using MS Word
is shown in table Q9, where majority of the respondents have advanced experience, accounting for 75.2% of the responses. Those with intermediate experience accounted for 20.5%. Basic experience was however minimal as it accounted for only 4.2% of the total valid responses.

The respondents’ use of remotely sensed imagery in spatial data analysis for housing initiatives is shown in table Q10. As evident in the table, 73.1% of the respondents acknowledged the use of remotely sensed imagery in spatial data analysis for housing initiatives. 10.3% of total responses however did not use remotely sensed imagery in housing. The number of respondents that sometimes make use of remotely sensed imagery in spatial data for housing initiative accounted for 16.6%. Generally, it is significant that most respondents use remotely sensed imagery in spatial data analysis for housing initiatives.
In table Q11, the respondents’ job hierarchy within their respective organizations is shown. 46.8% of total response worked as middle level managers while 39.6% accounted for operational staff. Only 12.4% of total response worked in senior management position. It is significant that the majority of the responses actively take part in strategic operations of the organizations hence well conversant with GIS software usage.
The GIS softwares that support Participatory GIS (PGIS) in the respective organizations is shown in table Q12. PGIS is the integration of local knowledge and stakeholders’ perspectives in the GIS. For the realization of this, the GIS databases and products should be made accessible to stakeholders, who should in turn be able to apply the GIS and GIS products to resource management, development planning, and advocacy. As indicated in the table, Geodata was noticed to be the general GIS software product that supported PGIS in organizations as it accounted for 57.7%. Arcview, as a GIS software accounted for 37.2% while other GIS softwares made up only 1.2%. Geodata is thus identified as the general GIS product that supports PGIS in organizations.
The study also identified the web technologies that support PGIS visualization. The examined ones were 2Dimension, 3 Dimension, and CAD. Nowadays, digital information is used in GIS technologies where various methods of digitized data creation are adopted. Digitazation is the common method of creating data in which a survey plan or a hard copy map is transferred to a digital medium through various technologies like CAD (Computer Aided Design) program together with geo-referencing capabilities. Both 2-dimensional visualization and 3-dimensional visualization are commonly used. Table Q13 shows the responses on the various web technologies that support PGIS visualization. It is evident that 3D technology is the preferred web technology that supports PGIS visualization as it accounted for 55% of all responses. This was followed by use of 2D web technology which had 8.2% of total response. However, open source web technologies were identified and they only account for 0.9%. It is significant that 3D web technology is what is believed to be supporting PGIS visualization.
5.4.2 The Level of Education and familiarity with GIS softwares
Education level is one of the major factors that affect the familiarity and use of GIS softwares, as shown in the table 2.
It can clearly be seen from the table that GIS softwares are most popular among the degree holders. Of those who are familiar with GIS softwares, 58.9% are degree holders, 14.2% are diploma holders, 10.9% are master holders, 10.0% are postgraduates and only 6.0% have A- level. Most of the degree holders are familiar with ArcGIS (26.0% of the total) and MapInfo (22.0% 0f the total). However, only a few degree holders are familiar with both ArcGIS and MapInfo. This is represented by 2.4% of the total count. Considering the totals, it is also evident that ArcGIS is the most popular software with 47.1% followed by MapInfo with 34.4%, then Envi (12.7%) and finally MapTime (2.1%)
Based on these results, it is clear that GIS can easily be used by various councils across UK. Most of the degree holders, who form the bulk of the workforce, are familiar with the GIS softwares. This would make the use of GIS in housing initiative easy and cheaper.
5.4.3 Familiarity with GIS softwares and use of remotely sensed imagery in spatial data analysis for housing initiatives
Table 3 shows how familiarity with GIS softwares affect the use of remotely sensed imagery in housing initiatives.

Use of remotely sensed imagery in spatial data analysis for housing initiatives highly depends on the familiarity with the GIS software. Of all those who are familiar with GIS softwares, 73.1% always use remotely sensed imagery in housing initiatives. Those who sometimes use remotely sensed images and are familiar with GIS softwares form 16.6% of the total count while those who do not use remotely sensed imagery in housing initiative despite their familiarity with the GIS softwares form only 10.3%. when considering the individual GIS softwares, it is seen that the majority of those who use remotely sensed imagery in housing initiative are familiar with ArcGIS, then MapInfo. The table also depicts that none of those who are familiar with both Envi and MapTime do not use remotely sensed imagery in housing initiatives.
5.4.4 How education level affects the level of hierarchy within organizations
Table 4 shows how education level affects an individual’s hierarchy within the organization.
Education level is one of the major factors that affect an individual’s hierarchy within the organization. Of all the 46.8% individuals in middle management position, 26.9% are degree holders, 10.0% are diploma holders, 4.5% are post graduates and 3% are master holders. None of the A-level employees are in senior management position.
Generally, the degree holders occupy vast majority in the management positions. It is also important to note that this group is well familiar with GIS softwares. Majority of this group uses remotely sensed imagery in housing initiatives. This would therefore make it easy and less costly to optimize the potentials of GIS in housing initiatives.

Chapter Six
6.0 CONCLUSION
The purpose of this chapter is to infer a conclusion to the research, highlight the areas of major findings and suggest a recommendation to effective housing initiatives in Sheffield. The findings presented in chapter five are further summarized here so that the specific findings can be obtained clearly with respect to the research objectives. The conclusion is thus drawn based on the research findings in order to help answer the research questions.
GIS technology offers varied applications that are not merely map generations, but that help to collect, display, manipulate and analyze spatial data to help improve and support the decision making process by the policy makers. With such a technology, proper and sound decisions can easily be made at all levels of government and other organizations regarding the planning, distribution, and forecasting of housing initiatives. This research clearly shows that in housing initiatives, GIS can be used majorly in site selection and in policy formulation. In site selection, various factors like accessibility and location can easily be investigated through GIS. The ability of GIS to constantly examine and analyze any likelihood of variation and at the same time look out for emerging events or occurrences, can be used to determine the outcomes before undertaking financial commitment, prioritizing the applicability of available resources and change management. The technology also monitors the trend towards enabling decision making. This results into sound judgments as errors are minimized as possible.
Through the application of GIS technology, the research herein visually shows that the population of Sheffield city is high in the north central areas. It is also revealed that the population is low to the south of the city and to the neighboring areas of Sheffield city as has been shown in figure 2. It is as well realized from the research that, generally most of the housing in Sheffield local authority are centrally located in the city making the population at the central part and to the southern region of the city to have a wide list of housing to choose from. However, the research noticed that the population residing in the north western regions of the city have limited access to these housing. Based on this premise, therefore, it can be concluded that those residing at the peripheral areas constitute the majority of the population in Sheffield with limited access to affordable housing. These housing are more at the city centre where most roads are interconnected. This is the main reason for high population in these areas and consequently the reason why more affordable houses are within the region.
Based on UK’s work environment considering the employees’ level of education, their knowledge of GIS, and the level of hierarchy, it is evident that there is the necessary expertise for the adoption of GIS technology in Sheffield’s housing initiative. This, in the long run, can prove to be very easy and cheap since majority has the required know-how and not much will be spent in training.

6.1 Recommendation
From the research it was noticed that there are varied considerations that influence the choice of housing initiative. The city centre of Sheffield is observed as a major factor that determines the type of housing initiative and its location. This is because the demand of housing is high at the city centre as opposed to the surrounding areas. Therefore, in enhancing accessibility of these housing initiatives to Sheffield’s general population, it is recommended that a way out should be employed to ensure that people in the surrounding areas of the city have better access to all the housing initiatives.
The researcher therefore recommends that housing planning be undertaken with respect to the population size and distribution that have been made available by this research and data, mainly from GIS. As in the city centre, all the housing initiatives should be equally employed in the whole Sheffield city. The researcher proposes that all shared ownership, all unshared, council rented, social rented, bungalows and detached among other housing initiatives should be equally distributed throughout the Sheffield city. This will therefore eliminate the existence of housing initiative discrepancy.
Besides, housing initiatives should not be considered with respect to the road networks as seem to be the case revealed by the GIS technology. The researcher recommends that the initiative should be dependent on population size and demand. This will solve a scenario where the less privileged from the surrounding of the city centre do not access to the available houses simply because the houses are unavailable. This should however be correlated to the economic well being of the population. Lastly, an event of equal distribution of the needed housing initiatives with respect to the population size in all regions of Sheffield city will solve the problem of inaccessibility. This signifies that the general population of Sheffield city will be able to reside in the house of their choice.

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