Jennifer's GIS Blog

This exercise involved a comprehensive geospatial analysis to identify areas most suitable for development. We integrated five critical environmental and proximity criteria, transforming raw data into a composite suitability map. Methodology: For each of the following five spatial datasets, a reclassification model was applied. In this model, a suitability rating scale of 1 to 5 was used, where 5 represents the highest suitability for development and 1-2 indicates the least suitable land. These five reclassified suitability layers were then combined using a Weighted Overlay technique. Each criterion was assigned an equal influence (20%) to produce a final, integrated suitability map. This resulting map visually highlights a gradient of development suitability across the study area, with higher values (closer to 5) indicating prime locations. An Alternate Weighted overlay was also conducted where Land Cover and soils were weighted at 20%, Slope at 40%, and distance to streams and roads...

 For this week's exercise we were tasked with conducting a structural damage assessment of a few block area impacted by Hurricane Sandy. We were asked to create a map of the path of Hurricane Sandy We were then given a very large raster data set with a very small project area to assess structural damage. Knowing something about Hurricane Sandy and the amount of damage, I immediately ruled out the possibility that there were any structures with no damage or only affected or sustained minor damage. I then narrowed in on determining which structures were destroyed and which sustained major damage. To complete the analysis of damage within a distance of the shoreline I used the created shoreline to create buffers of 0-100 m, 100-200m, and 200-300m. I used a basic join to derive counts for the following result

 For this week's exercise, we spent a considerable amount of time evaluating out-of-date elevation data, looking at the potential impact of storm surge data. This included comparing potential inundation models using LIDAR and USGS DEM-derived data. For the first exercise we were asked to compare Lidar data from New Jersey pre and post Hurricane Sandy. We were given several areas to asses and evaluate what has changed as well as looking at how the area has redeveloped based on a more recently derived buildings shapefile. We then turned our attention to the Tampa Bay Area where we looked at different methods of modeling storm surge and evaluated the predictive ability of each model. This included comparing counts of the types of buildings each building would see as flooded or not As well as comparing which models predicted a percentage of each type of building would be flooded to compare the accuracy of the different models Finally we created a map showing the agreement between the d...

 I had completed 3 of the assigned modules, or very similar ones, when working on a visibility project for some other research in 2020 so I did not learn many new tips or tricks in this week's course.  Overall, these courses provide an excellent overview of the topics of using 3D data, visibility analysis. These include when certain tools such as extrusion can be used and for what purpose. The one new course was sharing 3D data using layer packages, while I use layer packages all the time, the advise on structuring a 3D scene for sharing was helpful, particularly highlightin the ability to define an area of interest (AOI) within the scene.

 This week's lab focused on using Lidar data provided by the USGS to create products derived from Lidar, these included assessing vegetation density, height of vegetation, as well as creating digital elevation and digital surface models. Additionally, this week we had a selection of readings looking at watershed analysis.

We employed three distinct methods to identify homicide hotspots from 2017 data:      Grid-Based Thematic Mapping: First, we created a fishnet grid and spatially joined it with the 2017 homicide data. We then filtered this grid to include only cells with at least one homicide and manually selected the top quintile of these cells. These selected features were then dissolved into a single set of polygons representing the hotspots. Kernel Density Estimation: Next, we used the Kernel Density tool with the totalhomicides_2017 shapefile. We set the population field to none, specified homicides_kd as the output raster, a cell size of 100, and a search radius of 2630 square miles. We used the planar method for density calculation and incorporated the Boundary_chicago layer as a barrier. The resulting raster's symbology was edited to display two distinct values, which were then used to reclassify the raster. Finally, this reclassified raster was converted into a polygon file. Loca...