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Table 4 Health impact planning support systems

From: Exploring the potential for planning support systems to bridge the research-translation gap between public health and urban planning

Paper Schoner et al. [39]
Bringing health into transportation and land use scenario planning: Creating a National Public Health Assessment Model (N-PHAM)
Boulange et al. [40]
Improving planning analysis and decision making: The development and application of a Walkability Planning Support System
Ulmer et al. [24]
Application of an evidence-based tool to evaluate health impacts of changes to the built environment
Health PSS tool/system National Public Health Assessment Model (N-PHAM) Walkability Planning Support System Coalitions Linking Action and Science for Prevention (CLASP)
Country USA Australia Canada
Description Provides baseline built and natural environmental conditions and pre-calculated health outcomes/levels
Health Module “engine” contains computed equations describing the association between built environment features and health outcomes
N-PHAM calculates/forecasts new outcome values based on the provided custom inputs/user changes to the built and natural environmental measures
Simulates changes in the built environment and modelling the impact these would have on transport walking behaviours:
Measure the walkability of an area
Test potential impacts of future policies and scenarios by allowing users to create and manipulate a virtual representation of an urban precinct
Assess selected health impacts of these planning decisions/scenario for the community
Designed to predict physical activity levels, health-related indicators and GHG emissions associated with proposed land use and transportation developments
Software and hardware characteristics Web-based API plug-in
Integrates with multiple existing scenario planning platforms and software applications, allowing users of available scenario planning tools (CommunityViz, Envision Tomorrow, UrbanFootprint) to choose an area of interest represented by Census block groups which return baseline input and outcome values for each block group, as well as aggregated values for the study area
These data are then available to the tool user to map and analyse data in ways specific to the respective tool
ArcGIS + CommunityViz 5.1
Commercially available software package owned and administered by City Explained Inc
It is customisable and is an extension of ESRI’s ArcGIS
Displayed on a touch-enabled 46-inch MapTable that can support up to 10 people around its screen
ArcGIS + CommunityViz 5.1
Commercially available software package owned and administered by City Explained Inc
It is customisable and is an extension of ESRI's ArcGIS
Functions and user interface Spatial data visualisation
Dynamic interface for sketch-planning + editing of spatial layers
Maps, charts
Health impact analysis and modelling
Spatial data visualisation
Dynamic interface for sketch-planning + editing of spatial layers
Maps, charts
Real-time Health impact analysis and modelling
Spatial data visualisation
Dynamic interface for sketch-planning + editing of spatial layers
Maps, charts
Real-time Health impact analysis and modelling
Urban magnitude/scale of the project/scenario application/s Precinct
Census block groups
Region/state
National
Precinct
Suburb
Precinct
Suburb
Postal codes
Built environmental/urban design layers Gross population density
Gross employment density
Jobs within a 45-min transit commute, distance decay, walk network and GTFS schedule travel time
Employment entropy index using a 5-tier employment classification scheme
Retail jobs within a 5-tier employment classification scheme
% of CBG employment within 1/4 mile of a fixed guideway transit stop
Network density—facility miles of pedestrian-orientated links per square mile
Street intersection density, weighted auto-orientated intersections eliminated
% of land cover developed as open space
% of land area covered by tree canopy
% land cover = forest
% land cover = natural
% of land cover = developed open space or natural space
Land use mix (commercial, education, industrial, parkland, residential)
Dwelling density
Housing diversity score
Local living destination score—convenience (convenience store, newsagent or petrol station); supermarket; speciality food destination (fruit and vegetable, meat, fish or poultry store); post office; bank; pharmacy; general practice/medical centre; dentist; community centre; childcare facility; library;
Closest train station (< / > 800 m);
Closest bus stop (< / > 400 m)
Street network—intersection density
Length of roads; bicycle and sidewalk facilities;
Distance to nearest major arterial, school and transit stop/station;
Accessibility to major regional destinations; several density vectors, including net-residential, intersection, schools, transit stop and type of each food location (sit down and fast food, grocery and convenience stores);
Land use—an entropy-based measure of the mix, retail floor-to-land area and park area
Health behaviour or outcomes or impacts have been used/estimated as coefficients for the health impact scenario model Transportation walking (binary participation + continuous duration)
Leisure walking (binary participation + continuous duration)
Transportation biking (binary participation + continuous duration)
Auto travel/sedentary time (binary participation + continuous duration)
Recreational physical activity (binary participation + continuous duration)
Body mass index (continuous)
Overweight (binary)
Obese (binary)
Kessler-6 mental health—moderate (binary)
Fair or poor general health (binary)
Transportation walking Walking and biking for exercise;
Walking and biking to work/school
Body mass index
Daily energy expenditure
Blood pressure;
Walk/bike trips/day,
Transit trips/day, Automobile trips/day,
Kilometres of travel/day
Estimated vehicular emissions of CO2/day
Scale at which the health outcome data is collected/modelled Census block Meshblock (the smallest geographic region on the Australian Statistical Geography Standard) Postal code
What predictive/statistical modelling technique was used to estimate the health impacts? Likelihood of participating in the activity = binary health outcomes = binary logistic regression Multi-level, multivariate logistic regression
To estimate the probability that an individual participates in transport-walking, the formula takes in the values for each built environmental variable multiplied by the corresponding regression coefficients and summed with the constants
Multivariate regression models were used to predict the value of each health outcome/behaviour based on each participant's built environment and demographic/socioeconomic characteristics
Four different types of regression model were used, depending on the type and distribution of the outcome variable: linear, log-linear, binary logistic and two-stage (zero-inflated). In each case, a base model was first built to include any statistically significant (p < 0.05) demographic/socioeconomic variables
Population demographic/s Adults 18–64
Older adults 65 + 
Adults > 18 years Adults > 18 years
Target application/planning-related task or stage Baseline analysis
Scenario testing
Baseline analysis
Scenario testing
Baseline analysis
Scenario testing
Intended users Planners
Community
Policy makers
Planners
Community
Policy makers
Planners
Academics
Policymakers
Does it support individual or group decision making? Individual
Group
Individual
Group
Individual
Group