Measuring Urban Form and Examining Its Impact on Traffic Congestion in Florida

Transcription

1 Measuring Urban Form and Examining Its Impact on Traffic Congestion in Florida By Vince Wang Chair: Ruth Steiner Co-Chair: Andres Blanco March 04, 2011 Department of Urban and Regional Planning

2 Overview 1. Introduction 2. Methodology 3. Findings and Results 4. Discussions 5. Conclusions 2

3 1. Intro. Problem Statement Congestion is getting worse nationwide, especially in Florida Nation's Congestion Cost (billions of 2009 dollars) Florida's Congestion Cost (billions of 2007 dollars) Source: 2010 Urban Mobility Report Source: Blanco et al. (2010) 3

4 1. Intro. Problem Statement Congestion is getting worse nationwide, especially in Florida Florida has undergone fast population and job growth in recent years resulting in changes in urban form Relationship between urban form and congestion contradictory: o o Compact development less car travel less congestion (Downs, 1992; Gillham, 2002) Compact development more localized congestion (Downs, 2004) 4

5 1. Intro. Research Question & Objectives Question: What is the relationship between urban form and traffic congestion in Florida? Objectives: Adapt the multi-dimensional approach to measure urban form at sub-metropolitan level; Make a valuable contribution towards the understanding of urban sprawl in the context of Florida; Provide further insight of the impacts that the built environment poses on traffic congestion. 5

6 2. Methodology Study Area County level urban areas (UAs) Characteristics: Clustered in central and coastal areas Differ in size Single vs. multiple UAs in one county 6

7 2. Methodology Conceptual Model Urban Form Population Size Density Continuity Concentration Centrality Mixed-use Proximity Population Size Congestion Housing Unit Density Job Density Roadway Congestion Index Continuity Housing Unit Concentration Job Concentration Delay per Capita Housing Unit Centrality Job Centrality Mix of Job/Housing Mix of Housing/Job Travel Time Index Housing Unit Proximity Pearson Job Proximity Independent Variables Correlation Cross-sectional Dependent Study: Variables

8 2. Methodology Conceptual Model 8

9 2. Methodology Settings Areal unit system Land Superimposed types on GIS layers Developed to calculate urban form indices Developable Modifiable Areal Unit Problem (MAUP) Undevelopable o Size 2.5*2.5 sq mile cell o Location Base West South Southwest 9

10 2. Methodology Data Sources Name Source Year Land types FGDL Parcel Data 2009 Urban Form Number of housing FGDL Parcel Data 2007 units Number of U.S. Census - LED 2007 employed residents Location of Jobs U.S. Census - LED 2007 Congestion VMT, Roadway Type, Lane-mile, No. of Lanes, Ave. Daily Travel FDOT rcitop

11 County name Housing unit concentration ratio Job concentration ratio Job/ housing mixed-use ratio Housing/job mixed-use ratio Continuity ratio Housing unit centrality ratio Job centrality ratio Housing unit ratio Job ratio 3. Results Areal Unit Effect Table 3-1. Ratios of the maximum to the minimum values calculated in four scenarios Bradford Desoto Hendry Pinellas Taylor Average Note: High value of the ratio indicates strong influence of areal unit location on urban form outcomes. 11

12 3. Results Descriptive Statistics 12 Table 3-2. Descriptive statistics of urban form indices Name N Minimum Maximum Mean Standard deviation Population size Housing unit density Bradford Job density Miami-Dade Housing unit concentrationª Job concentrationª Mix of job/housingª Mix of housing/jobª Continuityª Housing unit centralityª Job centralityª Housing unit ª Job ª ªMean value of four scenarios for each county level UA. Table 3-3. Descriptive statistics of traffic congestion indices Name N Minimum Maximum Mean Standard deviation RCI Taylor Delay per capita TTI

13 HU density Job density HU Concen Job concen Job/ housing Housing/ job Continuity HU centrality Job centrality HU Job 3. Results Correlation: Urban Form Table 3-4. Bivariate correlation between urban form indices Size.751 **.859 ** **.775 **.363 * HU density.882 ** *.812 **.739 **.476 ** * Job density **.857 **.482 ** HU Concen..557 ** Job Concen Job/housing.964 **.442 ** Housing/job.403 ** Continuity HU centrality.685 **.555 **.721 ** Job centrality.295 *.822 ** HU.702 ** Note: * Significant at the 0.05 level (2-tailed); ** Significant at the 0.01 level (2-tailed). Table 3-5. Bivariate correlation between congestion indices Delay per Capita TTI RCI.702 **.927 ** Delay per Capita.644 ** **. Significant at the 0.01 level (2-tailed). 13

14 HU density Job density HU Concen Job concen Job/ housing Housing/ job Continuity HU centrality Job centrality HU Job 3. Results Correlation: Urban Form Table 3-4. Bivariate correlation between urban form indices Size.751 **.859 ** **.775 **.363 * HU density.882 ** *.812 **.739 **.476 ** * Job density **.857 **.482 ** HU Concen..557 ** Job Concen Job/housing.964 **.442 ** Housing/job.403 ** Continuity HU centrality.685 **.555 **.721 ** Job centrality.295 *.822 ** HU.702 ** Note: * Significant at the 0.05 level (2-tailed); ** Significant at the 0.01 level (2-tailed). Table 3-5. Bivariate correlation between congestion indices Delay per Capita TTI RCI.702 **.927 ** Delay per Capita.644 ** **. Significant at the 0.01 level (2-tailed). 14

15 HU density Job density HU Concen Job concen Job/ housing Housing/ job Continuit y HU centrality Job centrality HU Job 3. Results Correlation: Urban Form Table 3-4. Bivariate correlation between urban form indices Size.751 **.859 ** **.775 **.363 * HU density.882 ** *.812 **.739 **.476 ** * Job density **.857 **.482 ** HU Concen..557 ** Job Concen Job/housing.964 **.442 ** Housing/job.403 ** Continuity HU centrality.685 **.555 **.721 ** Job centrality.295 *.822 ** HU.702 ** Note: * Significant at the 0.05 level (2-tailed); ** Significant at the 0.01 level (2-tailed). Table 3-5. Bivariate correlation between congestion indices Delay per Capita TTI RCI.702 **.927 ** Delay per Capita.644 ** **. Significant at the 0.01 level (2-tailed). 15

16 3. Results Factor Analysis of Urban Form Table 3-6. Rotated component matrix describing five urban form s Component Centrality/ Urban form index Size/density/ mixed-use job Concentration Housing Continuity Size HU density Job density HU concen Job concen Job/housing Housing/job Continuity HU centrality Job centrality HU Job Note: High index loadings are emboldened. 16

17 3. Results Ranking on Urban Form Factors Table 3-7. UA rankings on urban form s Size/density/ mixed-use Centrality/job Concentration Housing Continuity Ranking 1 Miami-Dade Desoto Bay Desoto Seminole 2 Broward Bradford Leon Nassau Monroe 3 Pinellas Putnam Escambia Flagler Walton 4 Hillsborough Leon St. Lucie Escambia Broward 5 Alachua Martin Sumter Taylor Okaloosa 41 Okeechobee Santa Rosa Pinellas Santa Rosa Bay 42 Santa Rosa Seminole Lake Pasco Desoto 43 Taylor Hendry Broward Hendry Charlotte 44 Citrus Hernando Highlands Bay Osceola 45 Bradford Sumter Taylor Monroe Highlands Note: Higher ranks indicate lesser sprawl. 17

18 3. Results Ranking on Urban Form Factors Table 3-7. UA rankings on urban form s Size/density/ mixed-use Centrality/job Concentration Housing Continuity Ranking 1 Miami-Dade Desoto Bay Desoto Seminole 2 Broward Bradford Leon Nassau Monroe 3 Pinellas Putnam Escambia Flagler Walton 4 Hillsborough Leon St. Lucie Escambia Broward 5 Alachua Martin Sumter Taylor Okaloosa 41 Okeechobee Santa Rosa Pinellas Santa Rosa Bay 42 Santa Rosa Seminole Lake Pasco Desoto 43 Taylor Hendry Broward Hendry Charlotte 44 Citrus Hernando Highlands Bay Osceola 45 Bradford Sumter Taylor Monroe Highlands Note: Higher ranks indicate lesser sprawl. 18

19 3. Results Ranking on Urban Form Factors Table 3-7. UA rankings on urban form s Size/density/ mixed-use Centrality/job Concentration Housing Continuity Ranking 1 Miami-Dade Desoto Bay Desoto Seminole 2 Broward Bradford Leon Nassau Monroe 3 Pinellas Putnam Escambia Flagler Walton 4 Hillsborough Leon St. Lucie Escambia Broward 5 Alachua Martin Sumter Taylor Okaloosa 41 Okeechobee Santa Rosa Pinellas Santa Rosa Bay 42 Santa Rosa Seminole Lake Pasco Desoto 43 Taylor Hendry Broward Hendry Charlotte 44 Citrus Hernando Highlands Bay Osceola 45 Bradford Sumter Taylor Monroe Highlands Note: Higher ranks indicate lesser sprawl. 19

20 3. Results Spatial Analysis of Urban Form Factors 20

21 3. Results Correlation between Urban Form and Congestion Table 3-8. Pearson correlation coefficients Urban form RCI Delay per capita TTI Size/density/mixed-use.435 ** ** Centrality/job Concentration Housing ** ** ** Continuity.361 *.339 *.416 ** Note: * Significant at the 0.05 level; ** Significant at the 0.01 level. 21

22 3. Results Correlation between Urban Form and Congestion Table 3-8. Pearson correlation coefficients Urban form RCI Delay per capita TTI Size/density/mixed-use.435 ** ** Centrality/job Concentration Housing ** ** ** Continuity.361 *.339 *.416 ** Note: * Significant at the 0.05 level; ** Significant at the 0.01 level. 22

23 4. Discussion Results Comparisons 23 Similarities Some urban form dimensions are highly correlated with one another. Some Grouping UAs of rank urban high form in some indices urban into form s s while low in others. Stronger relationship between urban form and congestion Divergent Use of more correlations accurate and exist up-to-date between urban data form and congestion Demographic measures. characteristics of Florida Variation of land size Differences Reasons Separations of UAs by county boundary Wang (2010) Cutsinger et al. (2005) Blanco et al. (2010) Sarzynski et al. (2006)

24 4. Discussion Suggestions More detailed research about urban form on individual basis case study Modification of congestion model Geo-spatial statistical analysis between urban form and congestion 24

25 5. Conclusions Divergent relationships exist between urban form s and congestion measures Sprawl Congestion Housing Size/density/mixed-use Continuity Centrality/job Concentration Many conceptually distinct urban form indices are statistically inter-correlated necessity of conducting analysis Sprawl is a relative term Urban form dimension Land use 25

26 Thank you! Questions? Vince Wang March 04, 2011 Chair: Ruth Steiner Co-Chair: Andres Blanco