march 6, 2013 tony giarrusso, rama sivakumar center for...
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March 6, 2013
Tony Giarrusso, Rama Sivakumar
Center for GIS, Georgia Institute of Technology
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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
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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
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Phase 1
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Most and Least Vegetated NPUs
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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
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ArcGIS Server, Javascript API, DoJo – Open Source Toolkit
Current Version (Beta) Official Release 2014
Coastal Resources Division
Georgia DNR
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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
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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.
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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
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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.
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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.
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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.
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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.
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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.
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Screenshots
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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
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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
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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.
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