Master Thesis
submitted within the UNIGIS MSc programme Interfaculty Department of Geoinformatics - Z_GIS
University of Salzburg
Spatial Variation of Household Income and Unemployment in the Municipality of Niagara Falls Area in Southern Ontario of Canada
by
Napoleon Kurantin
up40099
A thesis submitted in partial fulfilment of the requirements of the degree of
Master of Science (Geographical Information Science & Systems) – MSc (GISc)
Advisor:
Prof Josef Strobl
Austria, Salzburg April, 29th 2018
i
Science Pledge
By my signature below, I certify that my thesis is entirely the result of my own work. I have cited all sources I have used in my thesis and I have always indicated their origin.
Austria, Salzburg April, 29, 2018
(Place, Date) Signature
ii
ABSTRACT
The broad goal of this study is to employ certain spatial and non-spatial data analysis techniques (spatial statistical techniques, modelling – interpolation and analysis) to investigate and map spatial variations of aggregated household income and unemployment data-sets in Canada using 2006 as a reference (temporal) year with particular emphasis on the Metropolitan region of Niagara in southern Ontario. Sources of data to be used for this inquiry is mainly secondary datasets that include socio-economic datasets primarily obtained from the Government of Canada (Statistics Canada) and, GIS shapefile (boundary files) from Natural Resources Canada.
By employing spatial (Moran’s Index – Moran’s I) and non-spatial statistical techniques (Multiple regression) the study models and analyse spatial-temporal hierarchy of the mechanisms and, factors influencing the spatial distribution of unemployment and the average income of household within the processes of regionalism. The results obtained will contribute and thus, enhance policy making geared towards the reduction of employment and the spatial income inequality within the Niagara Falls region. Therefore, due to the geographical location and socio-economic diversity of Canada, this study maintains that regional inequality in Canada is sensitive to spatial scale, and that multi-mechanisms of regional inequality have a spatial- temporal hierarchical structure, which influences the patterns of regional inequality.
iii
PREFACE/FORWARD
The work presented in this thesis has been to a large degree funded by myself (loan acquisition) and from my long-time friend Catherine O’Brien. The project originally consisted two parts, namely the acquisition of the required data-sets and the formulation, modelling and writing the entire work. It has taken many years to complete as the candidate worked under very challenging conditions. For instance, there was four years of continuous electricity challenges.
Rather than dampen the spirit, it spurred on the will to complete the thesis. It is hoped the results will contribute to the growth of the discipline of spatial econometrics and regional science.
iv
ACKNOWLEDGEMENT
This project work in Master of Science in Geographic Information Science and Systems MSc (GISc) has been accomplished through the grace and guidance of the Almighty God. I subscribe to a belief in the need to appreciate people and share values, experiences, and knowledge across national, religious and ‘racial’ boundaries.
I am motivated by and, make my way through life’s complex opportunities with this belief as a solid framework. As such, a combination of personal values and formal and informed life experiences acquired over the years shaped by decision and resolve in accomplishment of this study. Hence, this study does not represent a singular effort but the efforts and unflinching support of many known and unknown individuals. It’s been a privilege to be associated with and offered such an opportunity to undertake a research of this nature under the auspices of UNIGIS at the University of Salzburg. I am greatly indebted to all the lecturers and administrators at the University of Salzburg, Department of Geo-informatics – Z_GIS.
The research for this study has been carried out under extremely difficult conditions. Because of the difficult conditions, the study had to take a longer time to be completed. I had to combine working around the clock to earn the meager means which enabled me to maintain myself with confronting the odds of the research, in terms of amount of work involved. It must be confessed that, doing the two things simultaneously was really a hard task. In fact, one of the rare wonders I have ever had has been the fact that, in spite of grave challenges which confronted me in the course of the research project, I could preserve in the accomplishment of a task which appeared
v almost insurmountable. I am forever convinced that the ingenuity of man reveals itself to the maximum, when any person concentrates his/her efforts towards overcoming adversities.
vi
Table of Contents
Science Pledge... i Abstract ... ii Preface/Foreword ... iii Acknowledgement... iv - v Contents... vi - viii List of Figures ...ix List of Tables ... x List of Abbreviations ...xi
CHAPTER ONE: INTRODUCTION
1.0 Introduction ... 1 1.1 Problem Statement... 2 1.2 Research Questions ... 3 1.3 Broad Goal and Objectives of the Study ... 4-6 1.4 Study Approach ... 7 1.5. Significance of the study ... 8 1.6 Chapter Disposition: Scope and Organisation of the Study ... 8
CHAPTER TWO: LITERATURE REVIEW
2.0 Introduction ... 10 2.0.1 Housing and Income Variables ………..….…. 10-12 2.0.2 Economic Variables - Unemployment ... 13-15
CHAPTER THREE: THEOETICAL FOUNDATION, RESEARCH DESIGN, METHODOLOGY AND DATA REQUIREMENTS
3.0 Introduction ... 16
vii 3.1 Research Design and Methodology ... 17-18 3.2 Study Relocation ... 18-19 3.3 Data Requirement ... 19 3.4 Theoretical Foundation ... 20 -24
CHAPTER FOUR: PROJECT DESCRIPTION
4.0 Introduction ... 25 4.1 Background of the Study ... 25 4.2 Research Design: Processing Steps ... 25-28
CHAPTER FIVE: RESULTS
5.0 Introduction ... 29 5.1 Spatial Distribution of Unemployment and Household Income ... 29 5.2 Spatial Distribution of Settlement ... 40 5.3 Geographically Weighted Regression (Local Model) _ Spatial Variation Between Unemployment, Income and Settlement ... 45 5.4 Mean Centers of Unemployment and Household Incomes ... 48
CHAPTER SIX: ANALYSIS OF THE RESULTS
6.0 Introduction ... 50 6.1 Spatial Distribution of Unemployment Rate and Household Incomes ... 50 - 52 6.2 Spatial Distribution of Settlement ... 52 6.3 Mean Center: Unemployment Rate and Average Household Incomes ... 52
CHAPTER SEVEN: SUMMARY, DISCUSSION, RECOMMENDATIONS AND FUTURE WORK
7.0 Summary of Findings and Conclusions ... 54 - 56 7.1 Personal and Professional Retrospect ... 56 7.2 Recommendation for Future Research ... 56 - 57
viii References ... 58 - 64
ix
LIST OF FIGURES
Figure 1: Five/ Three Regional Model of Canada ... 4
Figure 2: Southern Ontario, Canada ... 5
Figure 3: Location in Southern Ontario ... 5
Figure 4: Regional Municipality of Niagara Falls ... 6
Figure 5: Conceptual Framework ... 7
Figure 6: Flow Chart _ Work Process/Steps ... 27
Figure 7: Twelve Local Municipalities Areas in Niagara Falls ... 28
Figure 8: Spatial Distribution of Unemployment ... 29
Figure 9: Geographically Weighted Regression _ Dependent Variable _ Unemployment ... 30
Figure 10: Scatter Plot Matrix_-Unemployment ... 32
Figure 11: Autocorrelation Report __Unemployment ... 33
Figure 12: Spatial Distribution of Household Incomes ... 35
Figure 13: Geographically Weighted Regression _ Dependent Variable _ Average Household Incomes . 36 Figure 14: Scatter Plot Matrix _ Average Household Income ... 37
Figure 15: Autocorrelation Report__ Average Household Income ... 38
Figure 16: Settlements: Total Census Families ... 40
Figure 17: Geographically Weighted Regression _Dependent Variable_ Settlements ... 41
Figure 18: Scatter Plot Matrix __ Settlements ... 42
Figure 19: Autocorrelation Report__ Settlements ... 43
Figure 20: Geographically Weighted Regression _ Settlements, Unemployment and Average Household Income ... 45
Figure 21: Mean Center __ Unemployment Rate ... 48
Figure 22: Mean Center and Standard Distance _ Average Household Income ... 49
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LIST OF TABLES
Table 1: Results of Geographically Weighted Regression _ Unemployment ... 31
Table 2: Moran’s I __Unemployment ... 34
Table 3: Results of Geographically Weighted Regression _ Average Household Income ... 37
Table 4: Moran’s I __ Average Household Income ... 39
Table 5: Results of Geographically Weighted Regression _ Settlements ... 42
Table 6: Moran’s I __ Settlements ... 44
Table 7: Results of Geographically Weighted Regression _ Settlements, Unemployment and Average Household Income ... 46
Table 8: Excel Results of Geographically Weighted Regression _ Settlement, Unemployment and Average Household Income ... 47
xi
LIST OF ABBREVIATIONS
GIScience: Geographic Information Science and Systems ... 1
ICT: Information, Communication and Technology ... 3
GWR: Geographically Weighted Regression ... 8
GDP: Gross Domestic Product ... 25
OECD: Organisation for Economic Cooperation and Development ... 25
ESRI: Environmental Systems Research Institute ... 26
1
CHAPTHER ONE INTRODUCTION
1.0 Introduction
Relatively, among the Organisation for Economic Cooperation and Development (OECD) Canada, has been experiencing persistent economic growth rates over the last decade.
For instance, as at the end of the fiscal year 2012, Gross Domestic Product (GDP) for the United Kingdom (UK) was 2.435 trillion USD, Canada on the other hand recorded 15.68 trillion USD for the same year; representing 2.94 percent of the world economy (World Bank Group, cited in Trading Economics 2013). The impressive growth rate has equally led to an uneven process of economic growth and development among Canada’s ten provinces and three territories. Thus, it is important to investigate and examine the space-time dimension for analysing Canada’s processes of development. Between the years (1997 to 2007) the period with the fastest-growing incomes in Canada, according to Yalnizyan (2010), the richest 1 percent of Canadians took almost a third of all incomes gains.
Historically, interdisciplinary research and investigation have struggled with an objective and scientific inquiry as well as analysis of the processes of development especially those involving aggregated data and information collated at the micro and meso scale of analysis __
rural, urban and regional context. Theoretically, the proposed study is informed and motivated by the interplay of disciplines and their underpinning philosophies ranging from geography, spatial econometrics (spatial interaction of heterogeneity based on geo-referenced data-sets);
Geographic Information Science and Systems (GIScience); and regional science. According to Paelink and Kaassen (1979) and Anselin (1988a) spatial econometrics as subfield of the discipline of econometrics focus on spatial interaction (spatial autocorrelation) and, spatial structure (spatial heterogeneity), and regression modelling involving either cross-sectional or panel data-sets. The field of GIScience deals with the general principles underpinning the acquisition, storage, management, processing, visualisation and analysis of geographic data; GIS
2 involves computer software systems formulated and designed to undertake such activities (Goodchild 1992a).
Interestingly, there is no general accepted definition of what constitutes a region.
Christaller (1953) and Losch (1954) respectively delineated regions as hierarchical systems of central places or cities. The city in question is determined by its diversity of goods produced and relatively by its size and demarcated market areas for different goods. On the other hand, Hoover and Giarratani (1995) present and define regions in terms of spatially interdependent, nodal and labour market centers integrated internally relatively to the flow of labour, capital or commodity within the region compared to other places.
This study intends to investigate and re-exam the spatial-temporal dimensions and variables of regional household income, settlement and unemployment data-sets to map inherent spatial variations in the Metropolitan region of Niagara in southern Ontario in Canada using 2006 as a reference year. Henceforth, an evaluation of macro-economic policies is undertaken to access the sensitive and response to spatial variations within the Canadian federation.
1.1 Problem Statement
The Royal Commission on Canada's Economic Prospects (the Gordon Commission) of 1957 explicitly recognized the existence of regional disparity in Canada. It gradually became evident that differences existed not only in fiscal capacities but also in opportunities for growth.
The Gordon Commission defined the regional problem as a difference or gap between a province's level of income, or unemployment, or other such key variable, and that of another province or relative to the national average. These economic differences between provinces became known as disparity gaps. It also became evident at this time as it is today that, regardless of the national level of prosperity, the gaps did not disappear. They might narrow or widen in accord with changes in the national rate of economic growth but the relative differences between the rich and poorer provinces seemed to persist over time. In the words of the Gordon Commission (1957), if attempts to eliminate regional disparities were to be eliminated, it could no longer be considered sufficient for the less developed parts of the country to grow at the same rate as the more prosperous regions of the country; thus, they would have to grow at much faster rates. Furthermore, it maintained and concludes that its analysis of the problem was to have
3 profound effect on subsequent thinking about regional policies relative to the processes of economic growth and development with its positive and/or negative impact on employment generation and income (welfare) levels.
On behalf of the Commission on Growth and Development and the World Bank, Kim (2008), cautioned that spatial inequalities in socio-economic variables are not only a challenge and concern for the governments of developing nations but equally relevant of the developed nations. Although scare; a relative growing body of work has brought to the fore the existence and emergency of spatial variations in many dimensions of socio-economic variables in the developing and the developed world. Among the noted authors Kanbur and Venables (2005a), Kanbur and Wan (2006) espoused that increasing rate of economic growth and development has led policy makers to be worried about the uneven regional and urban development associated with the processes of development particularly due to advancement in Information, Communication and Technology (ICT) as inherent within the process of economic globalization.
The processes of globalization and spatial inequality could be beneficial or disadvantageous to regions and therefore, local communities. On one hand, spatial inequality may be advantageous when it stems from regional specialization based on comparative advantage and/or returns to scale in terms of productivity; on the other hand, according to King and Clark (1978); Kim (1999), the existence and/or of emergency of relatively high unemployment rates in certain regional locations could be attributed to external economies that are yet to be internalized hence, the spatial inequality may be closer to optimal levels. Therefore, academics and policy-makers alike concerned with inequality due to spatial socio-economic variations will not only monitor but evaluate relevant questions associated with rapid growth and development within the processes of economic globalization.
1.2 Research Questions
The phenomenon of spatial inequality and variation relative to the processes of globalisation, regionalism, economic growth and development raises many more interrelated questions than answers. However, based on the general issues raised in the research problem
4 statement above, the study sought to answer the following key questions posed in order to achieve its stated goal and objectives:
i). What is the spatial distribution of unemployment rates relative to household incomes?
ii). What is the spatial dimension of settlement patterns? and,
iii). What is the mean centre’s for socio-economic variables such as household income and unemployment rates at the census tract level within the selected geographical location within this study?
1.3 Broad Goal and Objectives of the Study
The broad goal of this study is to employ certain spatial and non-spatial data analysis techniques (spatial statistical techniques, modelling – interpolation and analysis) to investigate and map spatial variations of aggregated household income and unemployment data-sets in Canada using 2006 as a reference (temporal) year with particular emphasis on the Metropolitan region of Niagara in southern Ontario __ see Figure 1 – 4 respectively.
Figure 1: Five/three Regional Model of Canada
Source: Government of Canada, (2013).
5 Figure 2: Southern Ontario, Canada
██ Core area ██ Extended area
Coordinates: 44°00′N 80°00′W
Source: https://en.wikipedia.org/wiki/Southern_Ontario (2018).
Figure 3: Location in Southern Ontario
Coordinates: 43°03′36″N 79°06′24″W
Source: https://en.wikipedia.org/wiki/Niagara_Falls,_Ontario
6 Figure 4: Regional Municipality of Niagara Falls
Coordinates: 43° 5' 46.371" N 79° 2' 15.86" W
Source: https://en.wikipedia.org/wiki/Niagara_Falls,_Ontario (2018).
https://www.gps-latitude-longitude.com/gps-coordinates-of-niagara-falls (2018).
The maps presented in figures 1 to 4 pictorially depict spatio-temporal hierarchy of Canada.
Therefore, due to the geographical location and socio-economic diversity of Canada, this study maintains that regional variation and inequality in Canada, is sensitive to spatial scale, and that multi-mechanisms of regionalism have a spatial-temporal hierarchical structure, which influences the patterns of regional settlement, growth and development. Henceforth, this study sought to:
i). Investigate and identify the spatial distribution of unemployment rates and household incomes;
ii). Identify and map the spatial dimension of differential settlement patterns; and,
iii). Calculate and analyse the mean centres of socio-economic variables including household income and unemployment rates at the census tract level.
7 With the aid of Mindjet 2012, the broad goal of this study which is to employ certain spatial and non-spatial data analysis techniques (spatial statistical techniques, modelling – interpolation and analysis) to investigate and map spatial variations of aggregated household income and unemployment data-sets in Canada using 2006 as a reference (temporal) year with a focus on the Metropolitan region of Niagara in southern Ontario is depicted in figure 5 below.
Figure 5: Conceptual Framework
Source: Authors Compilation, (2018).
A central concern and focus of any empirical work is the employment of statistical data-sets that corresponds the assembled theoretical variables in the model and/or study. Henceforth, the variable of income and unemployment relative to settlement is selected as it allows for the investigation, identification, testing and examination of spatio-temporal variations amongst these variables. Furthermore, these variables are part of the core of constructed indices in the theoretical foundation of regional science and/or economics, location theory and spatial econometrics.
1.4 Study Approach
Although the proposed study is weighted more towards quantitative methodological approach, it will apply and make use of a mixed methods approach (aspects of quantitative and qualitative) with specific reference to data gathering, mapping, calculations, analysis and interpretation of the results. The quantitative aspect is grounded within a post-structuralist philosophy that enables the use of statistical, analytical, and modelling methods that incorporates
8 the sensitivity of local context, complexity, dynamism. Hence, within this approach, the study intends to employ Excel (version 2016), GIS techniques and analysis (ArcGIS version 10.3.1_
geo-spatial techniques such as Geographically Weighted Regression [GWR]) involving thematic classification and query of feature attributes to examine spatial patterns and relationships between the distribution of household income and unemployment in the Metropolitan region of Niagara in Southern Ontario in Canada. The qualitative approach applies the method of case study with the Niagara region as the focus of its geographical location within the field of regional science, economics and GIScience.
1.5 Significance of the Study
Theoretically, the study’s outcome and discussions contribute to the respective discipline and fields of regional science, economics, development and GIScience. Moreover, it is hoped it will bring to the fore a deeper comprehension of the spatial-temporal hierarchy of the mechanism within the above noted disciplines. Therefore, due to the geographical location and socio- economic diversity of Canada, this study will shed light on the interrelationship between attempts at forging a modern nation-state and the building of vibrant regional economic centers that spurs economic growth and development.
1.6 Chapter Disposition: Scope and Organisation of the Study
The study is succinctly organized into five interrelated clear chapters. The first chapter comprises the introduction to the study, background of the study, research problem statement, research questions posed, the broad goal and objectives of the study, the study approach adopted, and the significance of the study. The second chapter covers the review of the relevant literature relating to the subject matter of the study from scholarly sources. Chapter three dwells on the theoretical foundation, research design, methodology and requirements. The fourth chapter focuses on the description of the study’s project. Chapter five on the other hand emphasizes the results obtained from the research and its calibrations of variables with the application of GIS techniques and analysis (ArcGIS version 10.3.1_ geo-spatial techniques such as geographically weighted regression) involving thematic classification and query of feature attributes to examine spatial patterns and relationships between the distribution of household income and
9 unemployment in the Metropolitan region of Niagara in Southern Ontario in Canada. Chapter six involves the analysis of results as brought forth in chapter five. The last chapter focuses on the summary of findings, conclusions and recommendations.
10
CHAPTHER TWO LITERATURE REVIEW
2.0 Introduction
In the words of Fry (2018), a literature review is an objective, critical summary of published research literature relevant to a topic under consideration for research with its main goal being to create familiarity with current thinking on a particular topic. Moreover, Crow and Semmens (2007), maintains that a review of existing relevant data, information and literature is the most fundamental and important aspect of any research exercise as it not only to inform but equally relevant as it enlightens the researcher on his/her chosen field of inquiry. Therefore, the related literature incorporates the final section of the studies that compares and contrast the results and analysis of categories or themes that emerge from such an inquiry (Creswell 2012).
2.0.1 Housing and Income Variables:
Without delving into all of the literature on the subject matter under review, this chapter will focus on selected and hence, relevant literature pertaining to spatial variations of socio-economic variables of income and unemployment relative to settlement patterns within the processes of regional development. As such relevant information will be sought from secondary sources such as books, journals, reports, and online government websites and publications.
According to the National Research Council (2001) as cited in Wu and Gopinath (2016) increasing rate of employment losses, crimes and urban sprawl with its associated urban decay have left some inner cities become distressed if not depraved communities in the United States of America. On the hand, depressing economies and declining health services have left some once- viable rural communities in the United States of America turn into ghost towns. The processes of economic globalization and technological advancement which has led to increasing demand for leisure has contributed to changes within rural economies. In their study, it is noted that location decisions by firms and households are affected by natural amenities, human and physical capital as well as economic geography. Thus, amenities, infrastructure and economic
11 geography in its entirety affect the level of income, employment, housing prices and land development. By 2000 the median household income varied from 18, 000.00 to more than 91, 000.00 United States Dollars across counties in the United States of America; variation of median housing prices was even larger, hovering between $10,000.00 to over $640, 000.00 (Wu and Gopinath 2016). Henceforth, to a large degree, investments in infrastructure resulted in more job opportunities, higher income, appreciating housing prices and land development.
However, this scenario is not equally effective everywhere as investments in infrastructure in rural regions are more effective in the promotion of land development rather than in raising income and employment levels.
The literature on economic geography brings to the fore the importance of mobility, transport costs and travels as variables contributing to the growth and origin (location and settlement) of municipalities. In an European study, Bruyne and Hove (2006), divided Belgian into two study regions made up of the northern Flanders and southern Wallonia municipalities for their analysis of variations in housing prices with the adoption of an economic geography approach. The two regions differ in terms of their political, geographical and economic contexts. Citing Berg (2002), Bruyne and Hove (2006) argued that socio-economic variables particularly, general economic performance of a region affects the equilibrium of price on housing markets.
Furthermore, apart from income, employment opportunities are equally an important variable for municipalities which spurs local housing prices and economic development. Additionally, population growth and migration affect housing prices; negative impact from emigration and a positive impact from immigration. Similarly, differentials in government regulations and planning policies impact housing market outcomes (Gollard and Boelhouwer 2002).
Differentials in municipal tax rates could contribute to the attraction or otherwise of potential house buyers (Bardhan et al. 2003). Moreover, investments in the transport system and networks affect the commuting time and travel distance and hence, house market prices. Empirical evidence shows that the construction of the Toronto subway network in Canada contributed to increasing rate of house prices in the down-town region within the municipality (Bajic 1983).
Based on the work by DeSalvo (1985), Bruyne and Hove (2006) derived the following equation to model income and housing prices in their study of the two municipalities: northern Flander C and southern Wallonia P in the Belguim with the assumption that residents and/or workers in the
12 core could equally work in the core. However, residents and workers in the periphery could work in the periphery (P) municipality and or choose to work in the core (C). Workers in the periphery earn income (δ) and a section earns income (1- δ) in the core. Income in this scenario is the wage (w) times the number of hours worked (W), minus the costs of communing (T).
Thus, worker in the periphery working in the periphery is wpWp; workers of the periphery working in the core therefore have an income of wcWc – T. Henceforth, the average income in the periphery is δwpWp + (1 - δ) (wcWc – T). The number of hours worked W is equal to the total number of hours at a person’s disposal M, minus the hours devoted to leisure L, excluding the hours devoted to commuting C. Thus, re-writing the average income in the periphery is:
Where:
P = leisure and work in the periphery
C = lives in the periphery and works in the core
T = Difference between the two leisure times is the commuting time
Results from their study shows that in generally, income level has significantly positive impact on housing market price. A 1 percent increase rate in income per household increases approximately 0.3 percent in house market price levels; municipalities with high unemployment rates are less attractive to development. Municipalities with increasing population growth rates tend to have higher housing prices resulting from increased demand (Bruyne and Hove 2006).
Whilst the average income for Belgium is 6838.00 with a variance of 940721.00 Euros, that of Flanders in the north is 7079.00 with a variance of 775294.00; that of Wallonia in the south is 6504.00 with a variance of 796222.00 Euros respectively (Bruyne and Hove 2006).
The study confirms the empirical evidence that a higher unemployment rate in a municipality presents a negative impact on the housing price in both research locations. Spatially, the impact in Wallonia in the south is almost four times as high as the impact in Flanders in the north.
Moreover, the variance in the unemployment rate in Wallonia is much higher than in Flanders;
Wallonia significantly depends on the capital (Brussels) for employment opportunities and hence, distance and travel time are contributing variables on housing prices (Bruyne and Hove 2006).
13 2.0.2 Economic Variables = Unemployment:
Scholars’ including Metcalf (1975), Marston (1985), Partridge and Rickman (1995 and 1997), Malizia and Shanzi Ke (1993) and Molho (1995) are among authorities who have attempted to investigate and explained in detail regional unemployment rates. Apart from Molho (1995) who employed spatial econometrics all the other authors adopted standard regression techniques.
Early contributions to the field of spatial econometrics includes Cliff and Ord (1973) involves the technique of autocorrelation; positive autocorrelation refers to occurrence of similar values for a variable turn to clustered together in space as compared to negative autocorrelation when dissimilar values are clustered in space. Furthermore, Lesage (1997/99) notes that spatial autocorrelation means the absence of independence among observations within a cross-sectional data. Geographical units such as cities and rural regions may have different shapes, densities and sizes which could lead to measurement errors resulting in heteroscedasticity and in the case of spatial econometrics, spatial heterogeneity (Trendle 2002). Magalhaes et al. (2000) suggests that it’s not easy to differentiate between spatial autocorrelation and spatial heterogeneity. To overcome these noted measurement errors, it is suggested that there is an explicit inclusion of space in the original estimated equation.
Webber and Pacheco (2009) empirically, based on area-specific census data sourced from Statistics New Zealand, investigated and analyzed the determinants of spatial variations in employment rates in Auckland the capital of New Zealand, between 1996 and 2006. Employing spatial regression model (spatial error model and spatial lag model) to investigate the influence of spatial relationships they specified as:
y = Xβ+
=p u+ε
Where:
y = Dependent variable X = Independent variables β = Regression parameters µ = Error term
14 ρ = Spatial lag parameter
W = Weights matrix of area’s neighborhood Wij = Influence of area I on its neighbours j.
Source: Webber and Pacheco (2009).
Alternatively, the spatial lagged dependent variable model is specified as:
y = λWy + Xβ + u
Where:
Wy = Spatially- weighted average of the dependent variable: neighbouring locations λ = Spatially lag parameter to be estimated
Source: Webber and Pacheco (2009).
Results from the study shows that Moran’s I value reject the null hypothesis that there is no spatial clustering with figures of 0.468, 0.509 and 0.350; statistically significant at the 99 percent confidence level. Areas with high unemployment were continguous to regions and/or areas with high unemployment rates. Breusch- Pagan (BP) test for independence as part of the regression model resulted in a score of 668.818 thus, rejecting the null hypothesis of no correlation between the error terms of independence and the predictor variables. Henceforth, heteroskedasticity is present if variance of the errors from a regression model is dependent on the noted values of the dependent variables (Breusch and Pagan 1979). Referring to Patacchini and Zenou (2007), Webber and Pacheco (2009) cites the study of 297 travel-to-work areas in the United Kingdom that revealed that there exists spatial correlation between unemployment rates of different regions. The findings are relevant as it showed that despite empirical studies, others ignore the spatial correlation between regional unemployment rates and the importance of regional socio- economic development.
Based on disaggregated unemployment data-set at the Census ward level (CAS 2003 classification) of the Office of the National Statistics of the United Kingdom, Manning and Petrongolo (2015), adopted the urn-ball simple log function to measure employment search relative to geographical distance. Their investigation spatially revealed that the unemployed preferred to fill job vacancies with the nearest 5 kmilometers and this is estimated to be same for
when measured using commuting time and/or commuting costs.
15 Topia and Conley (2002), espouse that the unemployed as those who are neither at work or searching for work during the last four weeks prior to the reference week between 1980 and 1990 when 866 Census tracts from the Bureau of the Census in the city of Chicago in the United States of America (USA) were combined into 75 community areas for an inquiry and examination of spatial clustering patterns of unemployment rates. Adopting a spatial econometric model (i as located at a point si in a Euclidean space) in which the dependence distance between observations correspond to their random economic distance; when observations I and j are close, then their corresponding random variables of Xsi and Xs could be highly correlated. The findings show that there is a strong-positive auto-correlation of unemployment at distances close to zero however; there is decrease in spatial correlation roughly monotonically with distance.
16 CHAPTER THREE
THEOETICAL FOUNDATION, RESEARCH DESIGN, METHODOLOGY AND DATA REQUIREMENTS
3.0 Introduction
The chapter discusses the processes and procedure of data gathering and data analysis in this study. Henceforth, the chapter provides details of the methodology and design employed to achieve the broad goal and specific objectives of the study __ see chapter one. The last few decades have seen increasing attempts to foster collaborative approaches to spatial planning and optimal decision-making at different levels of abstraction and policy making. Geo-spatial analysis as a sub field of Geo-informatics applies statistical techniques to datasets which have geographical or geo-spatial underpinnings. Its applications include military and intelligence use, disaster and emergency management, public health, economic, regional and urban planning, forestry and climate science. The inter-relationship between human as a species and the environment is a complex process that requires knowledge, skills and information to make decisions. The application of GIScience enables us to build such model of the environment that makes it much simpler and easier to understand and hence, make informed decisions (Aronoff 1995).
Although the study is weighted more towards quantitative methodological approach, it will apply and make use of a mixed methods approach (aspects of quantitative and qualitative) with specific reference to data gathering, mapping, calculations, analysis and interpretation of the results (Denzin and Lincoln 1994). Moreover, the study is premised as a case study within the philosophical tool box of qualitative research approach and/or ontology. Such an approach allows for qualitative techniques for instance, a case study based on meaning, representation, and explanation of the systematic detection of underlying concepts and patterns in texts used as evidence (Miles and Huberman; Strauss, cited in Pavlovskaya 2005). The quantitative aspect is grounded within a post-structuralist philosophy that enables the use of statistical, analytical, and modelling methods that incorporates the sensitivity of local context, complexity, dynamism, network analysis and openness of sociological processes (Fortheringham; Poon, cited in Pavloyskaya 2005, p2005). Hence, within this approach, the study intends to employ Excel (version 2016), GIS techniques and analysis (ArcGIS version 10.3.1_ geo-spatial techniques
17 such as geographically weighted regression) involving thematic classification and query of feature attributes to examine spatial patterns and relationships between the distribution of household income and unemployment in the Metropolitan region of Niagara in southern Ontario in Canada. They are intended to supplement each other as both types of methods bear elements of rationality and non-rational influences (Pavlovskaya 2006).
3.1 Research Design and Methodology
The study adopts a mixed methods approach, utilizing quantitative and qualitative techniques.
The study will make use of a mixed method (aspects of quantitative and qualitative) with specific reference to data gathering, analysis and interpretation (Denzin and Lincoln 1994). Moreover, the study is premised as a case study within the philosophical of qualitative research approach and/or ontology. Such an approach allows for qualitative techniques for instance, a case study and content examination and analysis based on meaning, representation, and explanation of the systematic detection of underlying concepts and patterns in texts used as evidence (Miles and Huberman; Strauss, cited in Pavlovskaya 2005).
Case study as a method within qualitative research approach is considered robust as its enables a holistic investigation into community and spatial related based variables such as unemployment, income and poverty (Gulsecen and Kubat 2006; Grassel and Schirmer 2006). Thus, it’s the noted concern and limitations of quantitative research approach and associated statistical methods in providing an in-depth and holistic explanations that gave one reason for application of case study method within qualitative approach. Including case study as method help explain quantitative and qualitative data in a process that illuminates the outcome of a phenomenon under investigation (Tellis 1997; Johnson 2006).
On the one hand, quantitative research is the systematic empirical investigation of observable phenomena via statistical, mathematical or computational techniques (Given 2008). The purpose of this quantitative research was to use statistical tools in analysing identified and/or relevant data-sets. Moreover, the quantitative approach assisted the researcher to determine and compare association by using descriptive statistics such as percentage analysis, measure of central tendency and Geographically Weighted Regression (GWR) results. Geographically Weighted Regression a feature within ArcGIS geo-statistical analyst tool allows the separate generation of
18 regression equation for features analysed within sample data-set as a way of addressing spatial variation. Thus, allowing for the quantification and dissection of spatial related variables cross region and/or study area (Fotheringham, Charlton and Brunsdon 1997; Charlton and Fotheringham 2003; Legg and Bowe 2009; Fotheringham 2017).
A number of proponents for mixed methods (Jones 2004; Lyncy 1991; Yu 2004) have equally argued that triangulation between quantitative and qualitative methods produces better result and outcomes. Yu (2004) thus, builds on the arguments of Philips and Burbules (2000) that ‘pro- observation’ does not always lead to realism stance and that, quantitative research methodology is not always objective. He is in support of the use of variety of methods and the use of triangulation when necessary. The application of mixed methods to this study is believed to add up to the strength of the approach and moreover, help to thoroughly, and appropriately deal with the problem being investigated.
3.2 Study Location
This section of the study presents the location of the Municipality of Niagara Falls in southern Ontario of Canada which forms part of the census metropolitan area of St. Catharines - Niagara.
With a land area of 209.73 square kilometres (km2), the city of Niagara Falls with an elevation 184m above sea level is geographically located in the horse-shoe region of southern Ontario of Canada at a Latitude of 43°06′00″ N and Longitude of 79°03′58″ W (Natural Resources 2017).
At the end of the last census in 2016, the city of Niagara Falls had a population of 88,071 representing 1.0 percent increase from 2006 with a population density of 395.8 persons per square kilometre. Its land area size relative to the provincial land area of 908,607.67kms registering a population density of 14.1 persons per square kilometre. The Municipality recorded a total of 33,380 dwellings with usual residents; a change of 2.7 percent from 2006. Sixty-seven (67%) percent of private households resided in single-detached houses; 24.4 percent of the households were made up of couples who had children aged 24 and under at home. Within the same time period, the number of private dwellings occupied by usual residents increased by 7.1 percent (Statistics Canada 2018).
The labour force characteristically, includes seasonal adjusted migrations figures. At the end of February 2018, the total labour force within the municipality stood at 21, 200; employment stood
19 at 20,000 and unemployed figure is at 11,000 respectively. Whilst participation rate is 60.5 percent; employment rate is 57.4 percent and unemployment rate 5.2 percent (Statistics Canada 2018).
There are 23,912 business outlets in the municipality with approximately 16 percent of these businesses registered with the recognized nine chambers of commerce in Niagara Falls (Canadian Co-operative Association 2017). Niagara Falls locational competitive advantages include manufacturing (employment = 21, 200), agribusiness (employment = 2,100) tourism (employment = 15,000), transportation and logistics; thus, small, medium multi-generational sized businesses form the basis of the region’s economy. The economy of the region is spurred on by the development of large-scale hydroelectric power thus, providing relatively cheap electricity for industrial development. Apart from the three large-scale hydroelectric power plants built in the 1950 and 1960s, in 2013, another power plant was completed resulting in additional 1.6 terawatt-hours of electricity (Government of Ontario 2017). Taking advantage of its geographical location and space near the United States of America (USA), over 25 percent of all bi-national trade between Canada and USA pass through the Niagara border crossings.
Overall, the region stands out as multi-modal freight and interconnection alternatives involving deep water, rail, road and air transport buttressed by first class infrastructure to support such transport network and economic development (Niagara Region 2012).
3.3 Data Requirement
There is increasing collaborative approaches to spatial planning, evaluation and effect decision- making at different levels of abstraction, policy formulation, making and implementation. The field of geo-spatial analysis within the discipline of Geo-informatics adopt and apply statistical techniques to datasets which have geographical or geo-spatial dimensions. Its inherent applications involve disaster and emergency management, public health, military and intelligence usage, forestry, space and climate science as well as regional and urban planning (Aronof 1995). Data-sets and/or geographic information are at the core of GIS. Thus, the emphasis on geographical information is verified from the many definitions of GIS. For instance, Dueker and Kjerne (1989) defines GIS as a system of hardware, software, data, people, organisations and institutional arrangements for collecting, storing, analysing and disseminating
20 information about areas of the earth. To meet the goal and achieve the objectives of this study, the research employed secondary source data-sets and information. Fundamentally, secondary data is originally collected for a different purpose and re-used for another research (Hox and Hennie 2005). The use of secondary data that was collected by someone else for another primary purpose provides alternative and viable option for researchers with limited time and resources at their disposal (Johnston 2014). Data capture in GIS account for 85 percent of the total cost (Longley et al. 2001). Secondary sources involving panel data-sets, census data and shapefiles1 were obtained from Statistics Canada, GeoGratis,2 the University of British Columbia3 and the open-source DIVA-GIS.4
3.4 Theoretical Foundation
This section of the study illuminates the theoretical frameworks that guides and support the thesis. Grant and Osanloo (2014) posits that theoretical framework provides the structure to define and how one will philosophically, epistemologically, methodologically, and analytically approach a dissertation as a whole. On the other hand, Eisenhart (1991) in Grant and Osanloo (2014), defined a theoretical framework as a structure that guides research by relying on a formal theory constructed by using an established, coherent explanation of certain phenomena and relationships.
Among the theoretical frameworks that attempts to answer and address the following questions:
why the variations between settlements, housing, employment, unemployment, growth and social welfare across regions includes (i) Regional science, (ii) Location theory, and (iii) Spatial econometrics. Individually and collectively, these three theoretical frameworks recognize that the processes behind innovation, national economic growth and development are fundamentally spatial in nature thus, in short in the words of Dawkins (2003) space “matters.”
1 Natural Resource Canada: North American Datum 1993 (NAD83) _ In 1990, Canada officially made a change in its National Topographic System (NTS) by officially adopting NAD83 as its new Geodetic Reference System 1980 (GRS80) and/or the Canadian Spatial Reference System (CSRS): http://www.nrcan.gc.ca/earth-
sciences/geography/topographic-information/maps/9791
2 GeoGratis – maintained by Natural Resources Canada: http://geogratis.gc.ca/geogratis/en/search;
3 University of British Columbia: http://gis.ubc.ca/data-sources-2/canada/;
4 DIVA – GIS: http://www.diva-gis.org/gdata
21 Regional science attempts at providing an objective and scientific analysis of settlement, industrial location and urban development (Isard 1975). The field of regional science emerged due to a degree, the inability of the disciplines of economics that favour abstraction and generalisations; whilst geographers and planners preferred descriptions, synthesis and specificity (Garretson and Martin 20011). In the nutshell, economics ignored the consequences of space, geography and planning failed to rigorous deal with space. Henceforth, regional science noted and integrated the essences of space, time (temporal) and location within the processes of economic growth and development. Within the processes of economic growth and development, national development and distribution of welfare does not take place at the same time everywhere; it’s uneven and unbalanced in space and time. The processes of change and expansions in economic growth involving economic activities, settlement, employment and income generation are embedded spatially and temporally in a complex web of local and regional configurations (Martin and Sunley 1996; Martin 1999; Garretson and Martin 2010). The theoretical framework of regional science has spanned six overlapping areas of research and analysis. These include input-output relationships, demographic studies, environmental studies, location analysis, transport networks and urban studies. In recent years, regional science has been influenced if not substantially impacted by advances made in spatial econometrics dwelling and/or emphasizing regional features such as settlement patterns, economic activities, employment and unemployment, and income gradients (Rey and Montouri 1999; Fingleton and Lopez-Bazo 2006). Regional science theory therefore, focuses on spatial aspects of economic growth and hence, the territorial distribution of income (North 1955; Isard 1956).
Location theory was developed to include space as ignored by traditional economics inquiry and analysis. Location theory is said to provide the scientific-disciplinary bedrock of regional science and economics. Temporal and space are two vital variable considerations in the theory of location. Originators of the theory of location considered distance as social friction that needed to be overcome due to regularities in variations of costs and prices over space; these noted regularities are attributed primarily to transport cost which is a function of distance (Isard 1956). At its core is the microeconomic underpinning dealing with the location decision and choice of firms and respective households relative to the investigation and interpretation of spatial disequilibria and hierarchies (Capello 2011). Locational choice involves a trade-off between access to a variety of other locations and that of the cost of real estate at a given
22 location. This raises the issue of land use particularly as it relates to access; the more accessible a location is to the positive elements in the environment, the higher the valuable it will be. Apart from firm and businesses seeking accessing to productive labour, and markets, households on the other hand, seek access not only employment but access to amenities. In essence, time and convenience are the most important factors of access (Jordaan, Drost and Makgata 2004).
Balchin, Bull and Kieve (1995), maintains that in terms of urban land use planning, housing constitutes fifty percent in certain cities and twenty-five percent of personal expenditure. These raises a peculiar relationship between settlement (residence), place of employment and income;
leading to recognizable neighborhoods’ (Blair 1995). Access is provided to certain social services and amenities that residents may not presently need but may be in demand in the future __ known as option demand (Hirsch 1973). Five delineated criteria normally used in the evaluation of residential locations involves the physical characteristics of housing structures within a neighborhood; socio-economic characteristics of residents living in a particular neighborhood; public service and amenities informing quality of schools, roads and recreation;
environmental qualities featuring the topography and weather; and finally, accessibility to transport networks (Segal 1979). The last criterion as espoused by Von Thunen (1826) focuses on the distance – cost relationship relative to land values.
Until few decades ago, space was not a major or permanent feature of mainstream economic thought and analysis. Such questions involving proximity, location, spatial competition, spatial interaction, hierarchies, and the role of regions were neglected or causally treated. Economists almost viewed the economy within temporal dimension in which economic agents calculated economic activities, transformation by innovation, production of resources to induce growth, incur recessions without space (Derycke and Huriot 1998).
Since the framework and practice of regional science heavily depends on sample data that is collected with reference to location measured as points in space, spatial econometrics is distinguished from traditional econometrics by the spatial dependence between observations and, spatial heterogeneity in the relationships in a model (LeSage 1999). Bayesain methods greatly underpin spatial econometrics as some of its ideas are to be found in regional science modeling including a decay of sample data influence with distance; similarity of observations to neighbouring observations; a hierarchy of place and/or regions; and, a systematic change in
23 parameters with movement through space (Anselin 1988a). The availability of GIS with its associated user-friendly interfaces has enabled the spatial visualisation of variables in a region using maps. Apart from the visual perception of the spatial distribution of a phenomenon, GIS has the capability of presenting a map that allows the visualization of the spatial pattern of the noted phenomenon and thus, allows for the translation of the existing patterns into objective and/or measurable considerations (Camara, Monteiro, Fucks and Carvalho 2004).
Spatial dependence in a collection of sample data means that observations at location i depend on other observations at locations j ≠ i. According to LeSage (1998), this is stated as follows:
yi = f(yi), i = 1,…, n j ≠ i.
Where:
i can take on any value from i = 1,…, n.
The concept of spatial dependence is well encapsulated by Tobler’s (1970) first law of geography which states that everything is related to everything else, but near things are more related than distant things. In the same vein, Cressie (1991), Cressie and Wikle (2011) states that spatial dependency is present in every direction and get weaker the more the dispersion in the data localization increases.
Reason behind why sample data observed at one point in space and time depends on other values observed at other locations might reflect measurement error. For instance, unemployed labour are mobile and could move across different locations hence, unemployed rates measured on the basis of where people live (location) could exhibit spatial dependence (Anselin and Rey 1991).
Another important term in spatial econometrics is spatial heterogeneity which refers to variatio n in relationships over space. This is written as:
yi = Xiβi + Ɛi
Where:
i = observations collected at i = 1,…, n points in space
Xi = 1 x k vector of explanatory variables relative to a set of parameters βi
yi = dependent variable at location i
24
Ɛi = depicts a stochastic disturbance within a linear relationship Source: (Kelley and Gilley 1997; LeSage1998).
Spatial autocorrelation is another method and technique in spatial econometrics applied in Exploratory Data Analysis (ESDA) that allows for a better comprehension of the spatial distribution, structure and the detection of spatial dependence. According to Griffith (2003) spatial autocorrelation is the correlation of the values of a single variable to proximity values within a geographical space and time. The most common and relevant spatial autocorrelation indicator is that of Moran I which tests for global spatial autocorrelation for continuous data (Moran 1948/1950). Moran I varies from -1 to +1. Moran I weighting scheme is based on the deviations from the mean which is calculated for n observations on a variable x at locations i with j presented as:
2 0
( )( )
( )
ij i j
i j
i i
w x x x x I n
S x x
Where:
x == mmeeaann ooff tthhee x
wij == eelleemmeennttss ooff tthhee wweeiigghhtt mmaattrriixx
S0 == ssuumm ooff tthhee eelleemmeennttss ooff tthhee wweeiigghhtt mmaattrriixx;; 0 ij
i j
S
wSource: (Cliff and Ord 1973/1981; Kalogirou 2003).
Before the above spatial statistical methods are calculated, it advisable to bring to the fore, the calculated mean centre of the phenomenon under investigation and analysis. The mean centre which is constructed from the average x and y values for the input feature centroid identifies the geographic centre of point observations (Esri 2012).
25 CHAPTER FOUR
PROJECT DESCRIPTION
4.0 Introduction
This chapter provides an integrated description of the study under the following areas:
background of the study; the processing steps (including a flow chart); and the expected result.
4.1 Background of the Study
The extend, structure and spatial dimension of regional settlement, income levels relative to unemployment is very important from the theoretical perspectives and equally relevant from the policy designation, formulation and implementation stage. As pointed out in chapter two, regional and spatial disparities of settlement, income, unemployment relative in the European Union has brought to the fore, the importance of policy cohesion within the last few decades.
Ezcurra (2005) investigated and examined the distribution of inequality among the regions of the European Union from 1993 to 2000. As part of his findings, it is reported that there existed high level positive spatial dependence in the distribution of income. The result highlighted the relevant roles indicated by national component, unemployment rates, Gross Domestic Product (GDP) per capita, and especially, the weight of agricultural sector in total employment accounting for extend and degree of income dispersion across the continent (Ezcurra 2005).
Relatively, among the Organisation for Economic Cooperation and Development (OECD) Canada, has been experiencing persistent economic growth rates over the last decade. For instance, as at the end of the fiscal year 2012, Gross Domestic Product (GDP) for the United Kingdom (UK) was 2.435 trillion USD, Canada on the other hand recorded 15.68 trillion USD for the same year; representing 2.94 percent of the world economy (World Bank Group, cited in Trading Economics 2013). The impressive growth rate has equally led to an uneven process of economic growth and development among Canada’s ten provinces and three territories. Thus, it is important to investigate and examine the space-time dimension for analysing Canada’s inequality. Between the years (1997 to 2007) the period with the fastest-growing incomes in Canada, according to Yalnizyan (2010), the richest 1 percent of Canadians took almost a third of
26 all incomes gains. Henceforth, the main goal of this study is to assess the dynamics of spatial and non-spatial data analysis techniques (spatial statistical techniques, modelling – interpolation and analysis) to investigate and map spatial variations of aggregated household income and unemployment relative to settlement pattern data-sets in Canada using 2006 as a reference (temporal) year with particular emphasis on the Metropolitan region of Niagara in southern Ontario.
4.2 Research Design: Processing Steps
Research design and process and/or steps are a series of integrated actions that results in an outcome. These involves the thesis management process focusing on the description and organization of the work of this study and, the product-oriented process which specify the creation of the study result in a form of a flow chart (Project Management Institute 1996).
In this thesis, the results obtained from the research and its calibrations of variables was undertaken with the application of Excel 2016 (regression equation), and GIS techniques and analysis involving Environmental Systems Research Institute’s (ESRI) software (ArcGIS version 10.3.1_ geo-spatial techniques such as geographically weighted regression) involving thematic classification and query of feature attributes to examine spatial patterns and relationships between the distribution of settlement, household income and unemployment in the Metropolitan region of Niagara in Southern Ontario in Canada. Therefore, the study’s process and/or steps involve the following:
1. Download and merged the geography made up of the country (Canada), province (Ontario), census division (regional municipality of Niagara) and, census tract (St. Catherines – Niagara) into one shapefile __ Metropolitan Niagara region shapefile;
2. Joined the census tract settlement, income and unemployment data for the metropolitan area to the larger Metropolitan Niagara region shapefile;
3. Converted feature class to point class involving the centroids for the census tract in the Metropolitan areas; and,
4. The point class files were used in the calculation of mean centers by weighting the noted respective variables.
27 Below is a flow chart that pictorially brings to the fore the work undertaken in obtaining the results of this study.
Figure 6: Flow Chart__Work Process/Steps
Source: Authors Compilation, (2018).
The above diagrame (Figure 6: Flow Chart __ Work Process/Steps) depicts the integrated work flow chart portraying the research design process in achieving the results as obtained in the next chapter. Figure 6 is calibrated from the twelve local area municipalities in Niagara region __ see figure 7 below.
Merge Joins Fort Erie
Grimsby
Lincoln
Niagara Falls Niagara-on-the Lake
Pelham
Port Colborn
St. Catharines
Thorold
Wainfleet Welland West Lincoln
Census tracts: Settlements, income and unemployment for all twelve area
municipalities
Shapefiles of Metro Areas
Complete Files of Metropolitan Areas
Feature to Points: Point Class File
Mean Center: Output _ mean center
28 Figure 7: Twelve Local Municipalities Areas in Niagara Falls Region
Source: https://www.niagararegion.ca/government/municipalities/default.aspx, (2018).
The twelve municipalities of Niagara region also known as the “Regional Niagara” has its seat of government in Thorold. It’s located in the southern end of the Golden Horse-shore which is the largest megalopolis within the Canadian federation. The results of the study are presented in chapter five.