Angelique's GIS Portfolio

The purpose of this lab was to perform a post Hurricane Sandy damage assessment on structures within a study area in New Jersey. I began by performing a raster mosaic with pre- and post-Sandy imagery. With these mosaics added to a map, the Flicker and Swipe tools could be used to examine the structures pre- and post-Sandy.  I created a new point feature class for the damage assessment and created attribute domains for the analysis. Using domains helps assessments such as this one by ensuring data integrity because they limit value choices for each field. In addition, once the domains are created, a form can be created to use with ArcGIS Collector which is an app can be used in the field for a thorough damage assessment. Below is a screen capture of the domains I created with the Codes and Descriptions of the Structure Damage domain visible. I then performed my damage assessment by locating and identifying attributes based on storm damage.  I zoomed into the Ocean Cou...

The purpose of this module was to explore procedures for coastal flooding and storm surge analyses using elevation models, overlay analyses for vectors and rasters, and spatial queries. The first data set was for an area in New Jersey that was impacted by Hurricane Sandy. I converted LAS files from pre- and post- Sandy for the coastline area were to TINs and then rasters. I subtracted the two rasters from each other using the Calculate Raster tool and analyzed the resulting shapefile was for damage with a 2019 building overlay. There are several areas in the study area that show significant erosion (red areas) that have not been rebuilt. The map below shows the overall results of this analysis. The second data set was also for New Jersey. A DEM was provided and I reclassified it into areas that would flood based on the Hurricane Sandy storm surge of 2 meters. I then converted the raster to a polygon and examined the result for Cape Map County. Based on the analysis, about 52% of C...

The purpose of this week's lab was to explore 3 selected hotspot mapping techniques for crime analysis for 2017 homicides in the Chicago area. The results of each technique were compared against 2018 homicide data to assess each technique's reliability for predicting crime. The first technique was Grid Overlay Hotspot Analysis. The goal was to determine the number of 2017 homicides in each grid cell and select the cells with the highest count. This was accomplished by first performing a spatial join between the 1/2 mile grid cells and the 2017 homicide data which added a field representing the number of homicides in each grid. I then used the Select by Attributes tool to select all counts greater than 0 and saved the selection as a separate feature class. I selected grids with the top 20% manually from the attribute table - the total number selected was calculated by dividing the total by 5 - and saved the selection as a separate feature class. To dissolve this feature class,...

The purpose of this week's lab was to complete 4 ESRI trainings to explore the concepts of line of sight analysis, viewshed analysis, 3D visualization and sharing 3D content. The first training, 3D Visualization Using ArcGIS Pro was concerned with creating and navigating 3D scenes. 3D maps and scenes can be helpful for visualizing and analyzing data in a more realistic setting. Investigating data in 3D allows a different perspective in order to gain different and new insights that might not be answerable in a 2D setting. Applications for 3D maps are many and include showing the impact of a new building in an area, displaying a transportation route through an area, or visualizing subsurface features such as wells, pipelines or fault lines. Although 3D maps have wide ranging applications, navigating them can be initially cumbersome and sometimes difficult to interpret, depending on the data being displayed. In this training, I explored data for Crater Lake in Oregon, and San Diego,...

The purpose of this week's lab was to find and use LiDAR data in an analysis to calculate forest height and biomass. The original .las LiDAR file was acquired from the Virginia LiDAR online application (https://vgin.maps.arcgis.com/home/index.html). The LiDAR Download Grid: N16_5807_20 was downloaded and decompressed using an LAS Optimizer from ESRI. The DEM and DSM were created from the LiDAR layer by changing the appearance to Ground and Non Ground, respectively, using the LAS Dataset to Raster tool in ArcGIS Pro using a sampling value of 6. The original LiDAR scene and derived DEM are below. To create a forest or tree height layer, the DEM and DSM were used as inputs in the Minus tool in ArcGIS Pro. A tree height distribution chart was created with data from this layer. The chart shows the total count of tree of different heights. It approximates a normal distribution bell curve ranging from -5 (an error value) to 163 feet, with an average of 54 feet. Most of the tree heigh...

The purpose of this lab was to create a corridor of potential movement of black bears between two protected areas in the Coronado National Forest. The variables for the analysis included distance to roads, elevation and land cover. The flow chart for the work flow in this analysis is below: In order to begin the corridor development, I first developed a habitat suitability model. I began by  reclassifiying the roads shapefile and elevation and landscape rasters. The rasters were reclassified using the Reclassify tool with the cost values provided. The roads shapefile was first converted to a raster using the Polyline to Raster tool, then using the Euclidean Distance tool to identify distances away from the road within the elevation raster’s extent, then using the Reclassify tool with the cost values provided. The habitat suitability model was then developed using the Weighted Overlay tool with all three reclassified rasters, weighted with landcover at 60% and elevation and roa...

The purpose of this lab was to create a suitability model for a developer. The analysis included provided suitability ratings for land cover, soils, slopes, proximity to streams and proximity to roads. The flow chart of the analysis work flow is below: I began by using the Reclassify tool to reclassify the landcover raster. I used the Euclidean Distance tool on the rivers and roads shapefiles, individually, and then reclassified each raster output using the Reclassify tool. I used the Slope tool on the elevation raster to convert the raster elevations to slope and then reclassified the output raster using the Reclassify tool. I converted the soils shapefile to a raster with the Polygon to Raster tool and reclassified the output raster using the Reclassify tool. To create the final suitability model I added all the reclassified rasters to the Weighted Overlay tool and ran two analyses. The first analysis gave each variable the same weight, 20%. The second analysis used unequal...