March 6, 2013

Tony Giarrusso, Rama Sivakumar

Center for GIS, Georgia Institute of Technology

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

33° 46’35.74” N, 83° 23’48.33” W

Past Projects:

SMARTRAQ, Wetlands Extension,

Park Access, Beltline, CIR DOQQs,

HAZUS Inventory Data, Pavement

Management, Biofuels, ANDP Land

Prioritization, GIS Data

Clearinghouse, Tidal Energy

Tree Cover Statistics Across Geographies - - 5 Year Updates

Erdas Imagine, October 2008 Quickbird Imagery, ArcGIS

Vegetation Index, Supervised Classification, Accuracy Assessment

City of Atlanta, Tree Commission

Phase 1

Most and Least Vegetated NPUs

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

ArcGIS Server, Javascript API, DoJo – Open Source Toolkit

Current Version (Beta) Official Release 2014

Coastal Resources Division

Georgia DNR

Geocode Daily Gorilla Tracking GPS Readings 1999 - 2011

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

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.

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

www.walkshed.org

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. Characteristics of built environment is not considered.

www.walkshed.org 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.

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.

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 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.

Optimal route with lowest walkability cost:

Where Or 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.

Screenshots

Webportal for SPLOST data

Search Window Visualization Window Data Mining Window

Development a clearinghouse to facilitate the exchange of SPLOST information and

provide access to local and county decision makers and legislators

Center for GIS / Center for Quality Growth and Regional Development

1. County Based Search

2. Time Based Search

3. Purpose Based Search

5. Map Frame

6. Graph 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

Webportal for SPLOST data

UrbanSim is a software-

based simulation system

for supporting planning

and analysis of urban

development,

incorporating the

interactions between

land use, transportation,

the economy, and the

environment.