Lab 3 – Attribute Tables & Vector Analysis

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Lab 3 – Attribute Tables & Vector Analysis

 

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This lab had us show similar data normalized through different methods to show the impact we as the cartographer and analyst can present information that can be perceived differently. This analysis of King County was done to determine if the effects from hazardous waste sites are distributed equally throughout the greater Seattle area. A quantitative analysis was done to identify the demographics of those residents within a 1.5 mile distance to these sites. The two factors of interest are percent of the population made up of non-white minorities and resident who fall beneath the poverty line. We began data collection through the United States Census Bureau to select all non-white minorities and residents in poverty. The data was stored in an excel file that we had to then format appropriately for the GIS software to process. From the same website, we obtained a shapefile containing King County census blocks. Areal Weighted Interpolation was used to estimate the population in buffers around each hazardous waste site. This was done by determining the proportion of each block group that falls within 1.5 of each site. A series of fields and calculations were added to the attribute table of our joined data sets. Darker colors in the map represent a higher population while census blocks symbolized with lighter colors have a lower population of the factors of interest.

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Lab 3 Practical Exam

 

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Our practical exam was similar to the in-class except the extent of the area we were working with. Some problems I ran into included getting census data for the “Salish Sea” which crosses national borders. So I had to play around in Excel merging US tables with Canadian tables. Once done in Excel I brought the tables into Arc and began to work with them there. I used batch project to get all of my layers into HARN State Plane Washington North. Once I had all of the layers in the same projection I had to recalculate area for my newly joined polygons of counties and provinces because Arc does not auto update user generated fields (sq. km). After this I performed weighted areal interpolation to find out the projected change in population of this area over a 25 year time period.

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