Sara's GIS Journal

    In this lab, Washington D.C. burglary data were analyzed by selecting burglary incidents, performing a spatial join to count burglaries within census tracts, joining housing data, and calculating burglary rates per 1,000 housing units. The results were displayed as a graduated color choropleth map after excluding outlier tracts.        A kernel density analysis was then completed for assault incidents using a 100-foot cell size and a 1320 foot search radius. The raster was classified into six classes based on multiples of the mean density to identify hotspot areas.       For the Chicago homicide analysis, three hotspot mapping techniques were compared. The grid overlay method counted homicides within grid cells and selected the top 20% of cells. Kernel density was performed using a 100-foot cell size and 2630 foot search radius, with hotspots defined as areas greater than 3 times the mean density. Local ...

 In this module, I worked with geometry objects and nested loops in Arcpy to extract vertex information from a shapefile. The objective was to access the geometry of river features, iterate through every vertex, and write the coordinate information to a txt file.  I began the script by creating a Search Cursor for the rivers shapefile and accessing the OID, geometry and river name fields. Because the geometry object contains arrays of vertices, I used loops and the getPart() method to access each point that makes up a river feature. As the script iterated through the vertices, it assigned a vertex ID, printed the information and wrote the feature OID, vertex ID, X coordinate, Y coordinate, and river name to the created txt file. The results of the script:  This assignment helped me better understand how geometry objects are structured in GIS datasets. I learned how loops are used to move from features, to arrays, to individual vertices and how the getPart() method simplif...

 For this module, I worked with the ArcGIS notebook to explore and manipulate data. I focused on working with geodatabases, feature classes, lists, dictionaries and cursors.  The first goal was to create a new file geodatabase in the Results folder using my student username. After setting the workspace and enabling overwrite output, I generated a new geodatabase.  Next, I generated a list of all shapefiles in the Module 5 Data folder and used a for loop to copy each feature class into the newly created GDB. The describe function and its basename property were important because geodatabases do not use the .shp extension found in shapefiles. This process reinforced the use of loops and ArcPy geoprocessing tools.  After the data was copied, I changed the workspace to the new geodatabase and created a search cursor for the cities feature class. Using a SQL where clause, I filtered the records so only cities classified as County Seats were returned. I then created an empt...

 In this lab, I created a model in ArcGIS and created a python script to automate geoprocessing tasks. The modelbuilder workflow identified areas within the basin that may be suitable for farming by clipping soils, selecting non-prime farmland, and erasing those areas from the basin. I also created a Python script that added XY coordinated to hospital locations, generated a 1000-meter buffer, dissolved overlapping buffers, and printed geoprocessing messages. This assignment taught me how ArcPy and Modelbuilder can be used together to automate GIS analysis and improve workflow efficiency.  Python script results:  steps used to create script:  1         Import the arcpy module and environment needed to run the geoprocessing tools 2         Set the workspace to Module 4 Data folder to allow script to access shapefiles 3         Enabled overwrite output so existing fi...