Lauren Chaphe's Geographic Information Science (GIS) Portfolio

This week applied ArcGIS Pro towards analyzing hurricane impacts to buildings using LIDAR and USGS DEM raster data. Part one of the assignment included building data and USGS .las data collected pre and post hurricane Sandy in New Jersey. By utilizing the RASTER CALCULATOR tool to subtract the pre-hurricane data from the post hurricane data, I was able to create a raster dataset that shows areas with negative and positive changes in elevation.  As shown in the map above, red areas are areas that experiences a loss in elevation. Specifically the red areas in the center of the map can be assumed to be buildings/structures demolished during the storm. The blue areas are most likely areas were sediment was deposited and resulted in a gain in elevation. One other thing to note is the change in elevation running parallel to the shoreline, shown as a linear orange area. This is most likely a loss of elevation due to the erosion of the sand dunes during the storm. The black building outlin...

Visibility analysis determines what areas are visible to one or more observation points. There are two types of visibility analyses, specifically Line-of-Sight Analysis and Viewshed Analysis. A line of sight is a straight line originating from an observation point to a target point. A line-of sight analysis produces lines that show which parts of the line-of-sight segment are visible from the observation point. On the other hand, viewshed analysis shows the areas of a raster dataset, such as 3D topography data, that are visible to the observer. Two scenarios were introduced:  1. Line-of-sight analysis and parade route camera placement A line segment and 3D building scene was used to show what areas of a parade route would be visible from a selected observation point. This analysis shows the best camera locations with the best viewing angles, but this type of analysis can also be applied to security/surveillance scenarios or many other applications.  2. Viewshed analysis and ca...

LiDAR, or light detection and ranging, data is a very useful tool for foresters to understand the forest canopy and terrain, which is useful information for monitoring forest health, calculating forest biomass, classifying terrain, identifying watersheds, etc. In this lab, ArcGIS Pro was used to analyze USGS Lidar data in the form of a .las file to create a digital elevation model, which shows bare earth elevations, and a digital surface model, which shows both natural and man-made features, like buildings and trees. The LAS DATASET TO RASTER tool was used to create both the DEM and DSM. However, the DEM appearance was set to filter for Ground points before using the tool, while the DSM appearance was set to filter for Non Ground points. The Minus tool can calculate the difference in height from the ground and the tree canopy surface, or essentially the tree height. The negative values are located  along roads or clearings. This likely means there was some return issue along the fo...

The first week of Applications in GIS kicked off with crime analysis using ArcGIS Pro. Crime statistics data for Washington D.C. and Chicago for the years of 2017 and 2018 were provided to examine any spatial patterns and to create crime hotspot maps.  The original Washington D.C. 2017 crime data was queried to select rows that were classified as BURGLARY or ASSAULT and the selection was exported as two new feature classes to create two hotspot maps. For both maps, a spatial join (from the burglary/assault table to the census tracts table) was completed to create a new field that showed the number of burglaries or assaults in each census tract. The crime rate was calculated by dividing the number of burglaries or assaults by the number of household units and multiplying by 1000. For the burglaries map, the census tracts were set to show the crime rate for each census tract. Five classes were created using a natural breaks classification method and a graduated color scheme. Extremel...