March 6, 2013 Tony Giarrusso, Rama Sivakumar Center for GIS, Georgia Institute of Technology

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1 March 6, 2013 Tony Giarrusso, Rama Sivakumar Center for GIS, Georgia Institute of Technology

2 N, W Location: Georgia Institute of Technology, College of Architecture Established:1996 Primary Mission: Sponsored Research and Education Staff: Director (Joint appt. with CRP ), 3 Senior Researchers, 1 Post Doc,~15 Students Expertise: Geospatial Analysis, Course Development, Web Applications, Image Processing, Modeling Past Projects: SMARTRAQ, Wetlands Extension, Park Access, Beltline, CIR DOQQs, HAZUS Inventory Data, Pavement Management, Biofuels, ANDP Land Prioritization, GIS Data Clearinghouse, Tidal Energy

3 Tree Cover Statistics Across Geographies Year Updates Erdas Imagine, October 2008 Quickbird Imagery, ArcGIS Vegetation Index, Supervised Classification, Accuracy Assessment City of Atlanta, Tree Commission

4 Phase 1

5 Most and Least Vegetated NPUs

6 Zonal Function Summarize vegetation by grid cell 500*500 Sq. ft. Grid Cells City Area = ~ 132 square mile City Vegetation = ~100 square miles Percent Vegetated = 74.5% ~10% over estimate Next Steps Tree Extraction from Vegetation Subset Project Ends May 2013

7 ArcGIS Server, Javascript API, DoJo Open Source Toolkit Current Version (Beta) Official Release 2014 Coastal Resources Division Georgia DNR

8 Geocode Daily Gorilla Tracking GPS Readings Home Range (Kernel Density, MCP) and Habitat (LC Classification, DEM) Imagine, ArcGIS, Google Earth, Home Range Tools ArcView 3x Extension Diane Fossey Gorilla Fund International

9 Objective: Finding optimal walking routes based on user preferred factors. The research methodology models the influence of built environment that facilitate or impede pedestrians propensity to walk. Developing a detailed database of walkability attributes. Developing a process for weighting the importance of each walkability attribute. Developing and evaluating a composite walkability cost for pedestrian network segments. Developing a routing algorithm to route walking paths based on user criteria.

10 Despite growing research on walkability, knowledge about paths and corridors that are conducive for walking is still largely unavailable. Various travel surveys document that walkability factors and their impacts vary from person to person. Built environment highly influences walking behavior. Walkability scores for neighborhoods and streets

www.walkscore.com Tool for estimating the accessibility of nearby facilities. For a given location a walk score between (0-100) is presented, primarily based on the amenities.

11 Tool for estimating the accessibility of nearby facilities. For a given location a walk score between (0-100) is presented, primarily based on the amenities. Characteristics of built environment is not considered. Calculates a walkshed and derives a walkscore. Enables the user to select impact factors for walkability. Lacks some key built environment factors (i.e. crime, aesthetics). Available for NY and Philadelphia as sample cities.

12 Walkability attributes were chosen based on extensive literature, that fall into below categories: 1) residential density; 2) business density; 3) land use diversity; 4) accessibility; 5) street connectivity; 6) crime safety; 7) traffic safety; 8) physical barriers; 9) aesthetics; and 10) pedestrian infrastructure.

13 Analytical Hierarchy Process (AHP) (Tom Saaty 1980) Developed to organize and analyze complex decisions. Stratified system of ranking each attribute with respect to all others. Matrix of relative ranks are used to calculate eigenvectors, which are then normalized to derive weights for various attributes.

For each network segment, the overall walkability score is calculated as: Where WS j is the walkability cost of the street segment j; D j is the length

14 For each network segment, the overall walkability score is calculated as: Where WS j is the walkability cost of the street segment j; D j is the length of the street segment j; n is the number of the attributes of the walkability and Vi and Wi are the value and the weight for the attribute i, respectively.

15 Optimal route with lowest walkability cost: Where O r is the optimal route between two points, WS j is the walkability cost of the street segment j and m is the number of the street segments of the route.

16 Screenshots

28 Webportal for SPLOST data Center for GIS / Center for Quality Growth and Regional Development Development a clearinghouse to facilitate the exchange of SPLOST information and provide access to local and county decision makers and legislators Search Window Visualization Window Data Mining Window

Webportal for SPLOST data 1. County Based Search 5.

Status Housing Status Commute Pattern Census

29 Webportal for SPLOST data 1. County Based Search 5. Map Frame 7. Search Results based on the Selected County Purpose Voting Results Voter Demographics Economic Status Housing Status Commute Pattern Census Demographics Revenue Expenditure Debt 2. Time Based Search 3. Purpose Based Search 6. Graph Frame

30 UrbanSim is a softwarebased simulation system for supporting planning and analysis of urban development, incorporating the interactions between land use, transportation, the economy, and the environment.

Spatial Patterns / relationships. Model / Predict

Human Environment Spatial Patterns / relationships Model / Predict 2 3 4 5 6 Comparing Neighborhoods with high Quality of Life & health Overlap matrix NPUs with high NH & NQoL SEC High QoL High Health