CARTOmazing

Module 6 like Module 5 still deals with Hurricane Sandy and its effects and damages. This week we focused on creating a map to show Sandy's track points and path from the Atlantic over to the Gulf coast, create a citizen damage assessment using Survey 123, creating damage assessment using pre and post images, and examining damage patterns based of distance from the coastline.  Hurricane Sandy, a Category 1 storm, affected the states of New Jersey and the Gulf coast with billions of damage and homes destroyed. Below, shows the map for Sandy's track and path with information regarding wind speeds and barometric pressure. T he second part, dealt on using Survey123 to create a citizen damage assessment which can be accessed by anyone with organizational account. This survey helps field collectors to assess the damage and compile it in one database. The database collects data like GPS point, photo, date and time, type and description of the damage. Here's the link to the damage ...

This week's module is about coastal flooding which I had worked with during Typhoon Haiyan in the Philippines. I had occurring issues at the beginning due to intermittent connection and my personal laptop is taking time to run some analysis. The first analysis is about elevation change using pre and post sandy rasters.  Pre-sandy in LAS dataset Post-sandy in LAS dataset With raster calculator we are able to show elevation change from raster taken from the past and after the disaster. The map below represent the coastline erosion in red while the blue depicts possible accumulation of building debris (see map below). The second part focuses on creating a storm surge of 2 meters in Cape May County, New Jersey. The map shows the percentage of about 46.77% of Cape May County is affected by storm surge as depicted below. Lastly, the part that I had a hard time running the analysis for spatial join due to the number of buildings. This focused on showing the no. of buildings that are affec...

I am one of the discussion leader for this module as I'm very familiar with the use of kernel density analysis. I had used this to compute for water table of are area of interest based on data points of water table in a series of sampling area. For this module, we focused on Chicago's Homicide Crime Data for 2017 and then did a statistical comparison for 2018. We used 3 different types of hotspot analysis named, Grid-based Thematic mapping, Kernel density and Local Moran's I.  Grid-Based Thematic Mapping  We had spatially combined grid data over crime data and then summarized the numbers per grid cell. We only selected the top 20% of the overall grids with highest point count per grid. Kernel Density For this analysis we did not need a bounding area to spatially join our crime rates as it calculates the density of features in an area. This considers the occurrences of crimes that could put additional weight to an area when producing a hotspot for a certain radius or cell si...