Module 5: Geoprocessing with Python

Script Result

This is a screen shot of the Pythonwin Interactive Window displaying the successful result of a script written to run three geoprocessing tools. The first tool adds xy coordinates to the features of a point shapefile called hospitals. The second tool creates a 1000 meter buffer of the hospitals shapefile. And the third tool dissolves the hospital buffers into a single polygon feature. Writing and running a script does not automatically display each tools messages in the interactive window. To get feedback on the result of a tool, it is necessary to instruct Python to print each tools messages. I successfully incorporated two while loops to accomplish this part of the lab.

Homeland Security: Prepare MEDS

Minimum Essential Dataset (MEDS)

This is a screenshot of the minimum essential dataset stipulated by the Department of Homeland Security for the Boston Metropolitan Statistical Area. Layer (.lyr) files store symbology and scale range settings for the each group layer. This makes distributing data easy and ensures everyone is using the same symbology. 

DC Crime Lab

Population Density with Washington D.C. Crime 

This map shows the population per square mile of Washington D.C. overlaid with crime event points and police station locations. The vertical bar graph shows the number and type of crime reported during the month of January 2011.

Crime Distribution Among Police Stations 

This map takes the data above one step further. I created a spatial join between the police station locations and crime event points to determine the distribution of crimes. Knowing the total number of crime incidents for Washington D.C., I was able to determine which police stations may be overwhelmed. This information can assist planners in determining a location for a new police station. 

Crime Heat Maps

The Kernel Density tool was used to create three different heat maps. This information can be used to help direct law enforcement patrols with the goal of deterring future crime.  

HLS Law Enforcement: Participation Activity

In the article, A Better Method to Smooth Crime Incident Data, crime data from the New York City Police Department On-Line Complaint System is used to compare the differences between methods of kernel estimation. Kernel estimation is the spatial statistical method used to generate density maps. Police departments use crime density maps to identify crime hotspots and plan patrol schedules, so it is important that the map is as accurate as possible. In this week's lab we used crime data from Washington DC to generate crime density maps and propose a location for a new police station. The kernel estimation method used in the lab was based on the areal extent of Washington DC. However, as this article points out, there is a more accurate method for kernel estimating.

Kernel smoothing estimates the variation in density of events based on a point pattern, resulting in a map of smooth density values. Kernel estimation is successful in making sense of complex point patterns. The most important step in kernel estimation is selecting the bandwidth. Maps generated with a small bandwidth are spiky in appearance, and those maps generated with a large bandwidth appear smooth and generalized. As with either method, density maps can then be used to create other datasets for further analysis.

Most GIS programs base its kernel estimation on the areal extent of the event data without considering the spatial distribution of the points. The result is large bandwidths being selected for small sample sizes and small bandwidths for the large. This article proposes selecting the bandwidth based on a predetermined number of points, or neighbors, represented by the variable 'k' called the k-nearest neighbor method. This method bases the bandwidth on the average distances between the event data. Varying the value of 'k' allows the GIS analyst to specify the degree of smoothing which reveals the previously unrealized variation in density across the study area. This added capability must be used with caution though, as the user defined input can still result in a misleading or inaccurate map.

http://www.esri.com/news/arcuser/0199/crimedata.html

Python Fundamentals II

Python Dice Game Result 

The result of this script lists eight players with their score from the roll of a dice and whether or not they are a winner. The list of numbers at the bottom are the result of a while loop counting from zero to five.

Hurricanes

 Hurricane Sandy 2012

This map charts the track of Hurricane Sandy displaying wind speed and pressure along the way. States that made disaster declarations with FEMA are represented as well.

Hurricane Sandy 2012 Damage Assessment

This map shows pre and post storm imagery of a small section of the New Jersey shore. Structure damage points were placed over each parcel and then classified by comparing the two sets of imagery.

Python Fundamentals I

Successful Result

This is a screen shot of a script result that displays my last name. Then it calculates the length of my last name and displays the result multiplied by 3.

Japan Tsunami Lab

Fukushima Evacuation Zones

This week I created a File Geodatabase with Feature and Raster Datasets. I populated the datasets with shapefiles and DEMs of North East Japan. I also created a point feature class for Japan Cities from XY coordinate data in an Excel spreadsheet. This map was then created to show the evacuation zones in relation to the Fukushima II Nuclear Power Plant.