GIS Portfolio

As part of my master's program in GIS at University of Akron, I have carried out GIS analysis tasks involving:

1. Overlay Analysis
2. Proximity Analysis
3. Statistical Analysis in GIS
4. Performing Network Analysis
5. Raster Analysis - Hydrologic Modeling, Performing Surface Creation and Analysis
6. Performing Cost Distance Analysis 

Example of each analysis type is shown below.   

A)Performing Overlay Analysis :- In this study, an attempt has been made to document whether poor or minority population in Summit County, Ohio, are disproportionately affected by the negative externalities of heavy traffic using Overlay Analysis tools.

B) Performing Proximity Analysis :- In this study, the relationship between performance indicators for individual school building and the demographic characteristics of the surrounding community is examined using a Proximity Analysis.

• The U.S Census demographic data on Race, Median Family Income in 1999, Occupancy Status, Tenure, Median value for all owner-occupied housing units was downloaded.
• Data on report cards and location of school buildings was gathered from the Ohio Department of Education. 
• For this study, only elementary schools (grades combination of k,1,2,3,4,5, or 6) were selected.
• Next step was to remove the school which are too close to each other because in some cases there are multiple schools located very close to one another which makes this analysis difficult. Using  Point Distance tool the distance between school was calculated with search distance of 2000 ft. 
• After identifying the pairs of schools at the same distance , one of the these pairs is eliminated using lowest grade level and higher performance score in 2007-08.
• Assuming that students attend the school that is closest to the census block group in which they live, Near tool is used to calculate the distance to nearest feature in the Near Feature(elementary schools) layer, and the input features layers (census block layer) which also puts the results in the attribute table of the input features.
• Using the Dissolve tool,  a simplified layer that has a single value for each attribute (total contributing area for a particular school) for examining the relationship between demographic data and school building report card data. 
• The map below shows one strong and one weak relationship between the demographic variable and the report card variable.

C) Performing Statistical Analysis in GIS:- The objective of this study was to investigate whether organic soils have affected land use in Summit County over the years.

• Soil features for Summit County were digitized using scanned image of a hard copy soil map (Ritchie, 1969) using various digitizing techniques like "Auto-Complete Polygon", "Merge", and "Trace". 
• The Topology rules were created to guide in the digitized process. The following map shows the digitized soil features. Also, type of limitation associated with each soil type is shown on the right.

• Next, land use layer from Ohio Department of Natural Resources (ODNR)'s Geographical Information Management System (GIMS) site was obtained. 

• To simplify the process, the land use was divided into main four categories: (1)Residential, (2)Agricultural, (3)Urban or Industrial, (4)Forest, Recreational, or Other Open Land.

• The graphics shown below shown results of the analysis including GIS model.

• The final result for showing the statistical output is shown below.

D) Performing Network Analysis:- The objective of this exercise was to create map showing 3 minute and 5 minute travel time from elementary schools in Summit County  that had attendance rates in 2007-2008 of higher than 95%.

• The network dataset was created with travel time as a cost factor using Summit County street data. For this exercise, the elementary school layer created in earlier studies (B) was used in order to create service area of 3 and 5 minutes from the school as a facility. The final result is shown below.

• Finally, watershed boundaries were created using basin tool. The map below shows the watershed boundaries (area contributing to particular river).

F) Performing Surface Creation and Analysis: In this study, the changes in the amount of chemical deposits (NH4 and SO4) was mapped for the entire United States of using the spatial analyst tools.

• The raw data was downloaded from National Atmospheric Deposition Program website.

• Inverse Distance Weigting (IDW) and Spline interpolators were used to create the spatial distributions of SO4 ion deposition for 2007. For the IDW and spline tool, to keep the surface relatively smooth, 30 points was used for the local area. The output is shown below.
  

• It was found that IDW creates a fairly smooth surface and also represents the true data values. On the other hand, spline tool does not represent the true data value but provides fairly smooth surface. For this study, since data values was given more importance over smooth surface, the IDW was used for further analysis.
• Using the IDW technique, the surface was created for changes in deposition ofSO4 and NH4 deposition for both 1985 and 2007 shown in the graphic below.

• The cost was determined by the land cover classes:- The "cost" of travel was determined by the land cover class, such that it was most difficult to traverse wetland, water, urban, and strip mine classes, next most difficult to traverse forest classes, and least to traverse barren land or agricultural classes. 
• To carry out this process, the land cover layer was reclassified based on the Least Difficult, More Difficult, and Most Difficult to traverse from source and destination. 

• Objective:- The goal of this research project was to evaluate and compare the accessibility index (Plane & Rogerson, 1994) of public facilities (schools, hospitals, post offices, and libraries) for urban and rural region in Mid-Ohio region.

• Methodology:- Accessibility index is a ratio of population in specified region (census block) to the distance in meters to its closest facility (public facilities). In this study, network distance is calculated using closest facility tool available under Network Analyst extension. The closest facility tool calculates the closest facility using available network dataset from each census block in the study area.

 

• The accessibility index values indicate that there is a large variation of access to public facility across the study area.
• The following graph shows the mean accessibility index of public accessibility for both urban and rural region. It suggest that index for urban region is significantly higher than the rural region for all the public facilities.