This document outlines a framework for measuring indicators of community health and well-being based on future land use and transportation planning scenarios. It summarizes work analyzing the City of Abbotsford's Official Community Plan using geographic information systems. Key findings include: - Eleven design indicators across seven categories were identified to measure health impacts of planning scenarios. These included density, access, and natural areas. - Studies show relationships between walking and access to services/mixed uses within 800m network buffers of homes. Larger scale indicators can also be used. - Scenarios for Abbotsford included population and employment projections, built environment typologies, and transportation networks. Indicators were calculated and maps/dashboards developed

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HEALTHY COMMUNITY DESIGN
MEASURING INDICATORS OF COMMUNITY HEALTH + HOLISTIC WELL-BEING
BASED ON FUTURE LAND USE + TRANSPORTATION PLANNING SCENARIOS USING
GEOGRAPHIC INFORMATION SYSTEMS
Anthony Smith, Ryerson University
For: Jennifer Fix, DIALOG Design Vancouver
Final Report for 2015 MITACS Accelerate Summer Internship

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TABLE OF CONTENTS
HEALTHY COMMUNITY DESIGN FRAMEWORK................................................................................................. 3
SCENARIO ANALYSIS PROCESS SUMMARY ........................................................................................... 3
CASE STUDY: CITY OF ABBOTSFORD OFFICIAL COMMUNITY PLAN...................................................... 4
EVERYONE WANTS TO LIVE IN A HEALTHY COMMUNITY................................................................................. 4
HOW CAN WE MEASURE HEALTH + WELL-BEING?............................................................................... 4
WHAT FACTORS INFLUENCE HEALTH + WELL-BEING? ......................................................................... 4
WHAT IS HEALTHY COMMUNITY DESIGN?........................................................................................... 5
WHAT ARE THE BENEFITS OF HEALTHY COMMUNITY DESIGN?........................................................... 5
UNDERSTANDING COMMUNITY CONTEXT + SCALE............................................................................. 6
LOCAL INTERSECTORAL COLLABORATION............................................................................................ 6
COMMUNITY DESIGN INDICATORS + HEALTH IMPACTS .................................................................................. 7
DESIGN INDICATOR TYPOLOGIES.......................................................................................................... 7
DESIGN INDICATOR LITERATURE REVIEW............................................................................................. 8
DISCUSSION: CITIES FOR PEOPLE.................................................................................................................... 12
APPENDIX A: ABBOTSFORD OFFICIAL COMMUNITY PLAN PROJECT DETAILS ................................................ 15
DEVELOPMENT OF POPULATION SCENARIOS .................................................................................... 15
BUILDING TYPOLOGIES ....................................................................................................................... 17
FUTURE EMPLOYMENT SCENARIODEVELOPMENT................................................................................ 19
FUTURE TRANSPORTATION NETWORK............................................................................................... 21
PUBLIC ENGAGEMENT MAPS AND DESIGN INDICATOR DASHBOARD ............................................... 22
APPENDIX B: GIS INDICATOR CALCULATION METHODS................................................................................ 24
ROAD NETWORK INTERSECTION DENSITY.......................................................................................... 24
NATURAL AREAS.................................................................................................................................. 24
GROCERY STORES................................................................................................................................ 24
APPENDIX C: GHG EMISSION CALCULATIONS................................................................................................. 26
APPENDIX D: MITACS PROJECT OVERVIEW .................................................................................................... 28
DESCRIPTION OF PRIMARY INTERNSHIP ACTIVITIES........................................................................... 28
ADDITIONAL INTERNSHIP ACTIVITIES ................................................................................................. 29
PROJECT RESEARCH BACKGROUND.................................................................................................... 29

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COMMUNITY DESIGN INDICATORS + HEALTH IMPACTS
This section will summarize a cross-sectional scan of planning and public health research that relied on
objective measures of community design calculated using GIS. This discussion has been grouped into 11
planning and design categories related to each of the community design indicators identified in the
conceptual community design indicator framework.
DESIGN INDICATOR TYPOLOGIES
To focus the scope of this review, it is important to contextualize the characteristics of the CDIs that will be
considered in this study, to be clear about what they are – and are not. Broadly speaking, the type of CDIs
used in this study could also be described as healthy built environment indicators or planning performance
indicators. Additionally, CDIs can be calculated based on many different methods, geographic scales, and
time periods.
This review will consider evidence from studies that are based on individual household or postal code
locations, neighbourhoods, or city regions. In some cases, individual indicators have often been calculated
based on circular buffers or network-based walksheds surrounding a home location or postal code. This
approach provides a ‘custom’ neighbourhood for each resident that may improve the accuracy of
measurement and better reflect individual exposure to local residential environments.
For example, Oliver, Schuurman, & Hall (2007) compared circular and network buffers to examine the
influence of land use on walking for leisure and errands and found a significant relationship between
walking and built environment based on land use mix within 800-metre network-based buffers.
Alternatively, CDIs can be constructed based on larger scale sample areas, such as urban centres or
municipalities. For example, the performance indicators used to assess the Growth Plan for the Greater
Golden Horseshoe in Ontario were calculated based on municipalities and within specific sub-city-scale
planning areas such as urban centres or transit-oriented development zones (Government of Ontario,
2015). The current study will include CDIs that are based on the following five spatial analysis methods.
1. Area Indicators based on the fraction of a study area polygon that is covered by a certain feature.
2. Density Indicators based on the number of points, the length of lines, number of polygons, or
summaries of other spatial attributes such as population totals within a given study area. These
measures must always be standardized and expressed per unit area. Reporting raw feature counts can
result in bias due to study area size variability.
3. Population or Access Indicators are based on the proportion of the population within a study area that
lives within a specified distance from a point, line or polygon feature. These are expressed as a fraction
of the total population with access, divided by the total population of a study area. Moreover, these
measures can be based on spatial buffers such as circles from points, or network-based buffers that are
often called ‘walksheds’ or ‘drivesheds’.
4. Distance Indicators examine the location of a study area relative to one or more external locations.
5. Index or Ratio Indicators are derived from primary measures. These indicators can take many forms.
Each of these indicator typologies can be calculated based on existing spatial data for a community, or
based on modelled or constructed scenarios related to future growth scenarios. Results can then be
directly compared across time or between scenarios to evaluate direct impacts of planning and design
decisions. Based on the evidence presented in the following section, these impacts can then be related to
potential health and well-being outcomes.

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This report highlights research conducted during a MITACS Accelerate internship that was completed in the
summer of 2015.This report was produced by Anthony Smith, a Graduate Student in the Master of Urban Planning
Program in the School of Urban and Regional Planning at Ryerson University.
This project was inspired and supervised by Jennifer Fix, an Associate planner and urban designer at DIALOG Design
Vancouver. Academic supervision was provided by Dr. Raktim Mitra, a faculty member in the School of Urban and
Regional Planning at Ryerson. Anthony would also like to thank Martin Neilson, Esteban Matheus, Molly Steeves
and many other DIALOG team members and City of Abbotsford staff who provided valuable input to this project.