GIS Programming Module 5

This week in GIS Programming we wrote a workable script in Arcpy that manipulated spatial data. We first added xy data to a hospitals point file. Using python we then wrote a script to add a 1,000 meter buffer around the hospitals and finally dissolved the overlapping buffer circles. I was nice to apply python scripts to data file.

Applications in GIS. Week 5 crime.

This week we worked with data concerning crimes in Washington DC. In the first map we were looking at the different types of crimes. The second and 3 maps we analyzed the number of crimes that occurred near police stations and if the presence of a  station affected the number of crimes. It seemed to be depended on the crime.  Finally for the 4th map we looked at the number of crimes that occurred within 1,000ft of schools. This was upsetting. It was hard scrolling down the attribute table and seeing the amount and offences committed by juveniles. Drug free school zones don't seem to be affective in densely populated areas.

GIS Programming Dice game loop

 This week we wrote a script that ran a dice game loop which "rolled" random numbers. We created a nested while loop and an if conditional statement.

Participation Assignment: Using GIS to Identify Drug Markets

 

                                                                                              Map from the Wall Street Journal online

                                                    http://online.wsj.com/article/SB115930493250674733.html#slide/1

 

                   Targeting the drug epidemic is critical as there is a direct correlation to violent crimes. However, traditional drug enforcement methods have not been successful in battling the drug market. These traditional methods, which consisted of stopping citizens in the streets and questioning them was counterproductive, as it resulted in citizens becoming weary of law enforcement and disinclined to help. In High Point, North Carolina the police department embraced a new method which implemented the use of spatial data to create a focused deterrence model. This was built from a modification of a model used by Boston's Violent Crime Task Force. The goal of this model is to target specific areas where there is a high density and correlation between the drug market and violent crimes. Once the area and the offending dealers are know. The hope is that the community and the offending drug dealers will work with the police department to alleviate crime in the area.

           Unlike previous approaches to alleviate drug crimes, this method did not ask the question “Where are the drug markets”, it instead asked “Where are the densities of violent, sex, or weapons crimes that may be spatially concurrent with drug sales?” This questions reinforces the relationship of dealing drugs to violent crimes. To answer these questions GIS was used to generate a series of crime density maps based on a year of data. The data included 911 calls, drug arrests, field contacts, and a category of serious crimes which included; murder, rape, robbery, aggravated assault, weapons, sex, and prostitution. Collected data was converted to point data consisting of addresses. This data was geographically related using point features and each point included attributes on date and time, address, nature of offense, and XY coordinates. The data was then used to analyze dealer locations, distribution of dealers within the market, relationship between dealer location and crime. However, one alteration to the methodology would be to use more selective 911 data, for instance, only calls related to drugs, guns, and persons crimes . The 911 data applied to the High Point model was not selective enough and overwhelmed the density portion of the map with non-applicable results.

            Each layer was used to create a different kernel density map; a map of 911 calls, a map of drug arrest, a map of field contacts, and a map of serious crimes. The kernel density used a 1,000 foot radius that clustered nearby offenses. Interestingly the four different density maps did not show a similar pattern and had a significant difference. Each map and the density clusters of each map were analyzed separately in a process called “unpacking” this process closely looks at the relationship of the layers crime and drugs. Unpacking the data eliminated many of the areas as possible targets of the deterrence model. The dated was when overlayed with each layer and the chosen area was the West End neighborhood.

          The spatial data showed that the West End neighborhood had a high volume of crime associated to the drug market. Further analysis of this neighborhood reveled that it had a “small local 'drug' market in equilibrium” this means that the drug market consisted of walk-up and curbside drive through drug transactions and that the market was not expanding or getting smaller. A list of known street level dealers was compiled and an unique approach was taken. They were given a choice; accept the help of the community to stop dealing and find alternative employment or education, or be prosecuted to the fullest extent of the law. This was known as the “call in” phase of the model. Notified dealers were given a set time period when there decision had to be made.

The results of this model were said to be a drastic and immediate. The success of the model reeds like an ending to a “Happier Ever After” novel

“The West end drug market vanished overnight. Dealers and prostitutes
  were no longer present in the area.” “The character of the neighborhood
  changed immediately; residents ventured outside again, children
   played in the playground, people cared for their property”

            The goals of this project were met, and perhaps even surpassed, resulting in long term improvements. The alleviation of drug related crimes in this area allowed the police department to tackle other pressing criminal matters. This is a model that could be applied to many concentrated areas across the US and abroad. The use of GIS to alleviate crime allowed law enforcement to sift deeper into the root of the problem and became more “data-driven”, using GIS data to evaluate and eliminate crime hot-spots.

GIS Programming : The Orangutans of Borneo Are Aided With GIS

In Borneo, GIS played a critical role in conserving orangutan habitat which is diapering at a rapid rate. Borneo is a large Indonesian Island and one of only two places in the world which wild orangutans live. These great apes are losing habitat at an alarming rate. In just ten years; from 1992-2002 39% of orangutan habitat was lost in Borneo. Much of this loss was due to illegal logging.

A large scale effort was made by GIS scientist and biologist to curtail the logging in Tanjung Puting National Park, a 4,000km park that is home to 6,000 orangutans. This was the first large scale mapping and GIS analysis conducted on wild orangutan habitat. Because little GIS data previously existed about the area, the first step was to obtain Landsat imagery and start delineating habitat types and park boundaries. The Landsat data provided 30-meter spatial resolution in 7 spectral bands. This enabled the GIS technicians to differentiate 40 images classes which could be recorded into 14 major habitat types. After habitat types were identified data layers were obtained from universities and non-profits working in the area. These files contained vector shapfiles of physical and political features.

With this information, areas of critical habitat were identified, as well as areas of illegal encroachment. With the analysis of the boundary completed, GIS scientists discovered that a single palm oil plantation had encroached 3 miles into the National Park boundary. The same plantation owner had made plans to clear more land illegally just 2 months from the discovery, which was stopped.

The protection of critical orangutan habitat is still a huge conservation issue and Tanjug Puting National park still faces threats from encroachment. However, GIS was able to provide a spatial context for much needed habitat analysis.

http://www.esri.com/news/arcnews/spring05articles/the-orangutans.html

Applications in GIS: Hurricanes

For the last week of our natural disasters lab set, we worked with Hurricane Sandy data. In the first map we tracked the path of Hurricane Sandy adding metadata from the attribute table concerning wind seed and air pressure. To each point where the Hurricane was plotted. From this line of point data we were also able to create a polyline.

The second map shows pre and post aerial imagery of the Hurricane Sandy damage done to the Township of Toms River. We created point data and an accompanying attribute table to categorize the damage done to a street inside of the study area.

Using Models to Create Evacuation Zones.

For the third part of our Japan Tsunami Lab we built a model to create evacuation zones based on the elevation of the areas effected by the tsunami. Using the Con tool, Raster to Polygon tool, Append tool , and the Intersect tool we were able to build a model that isolated different elevations of the raster data (zones 1, 2, and 3) was well are the cities, roads and power plants inside the evacuation zones. It was a very interesting assignment and I look forward to working with models in the future. 

Python Fundamentals Part1

This week in GIS programming we worked on the fundamentals of Python. For those of us who have never worked with writing scrips, it was challanging and frustrating. However, for me, it was also incredibly rewarding once I figured it out and got the correct results. We manipulated variables and  used various functions; float(x), str(x) and more, to write a script which tells us how many e's we have in our middle name. Above are the results of the script.

Apps in GIS: Fukushima Evacuation Zones

For our second week of Natural Hazards we worked one making evacuation zones for the Fukushima Nuclear Power Plant in the aftermath of the 2011 Tsunami. The zones created are in 3,7,15,30,40, and 50 mile increments. According to the data given there are no cities within 15 miles of the power plant, but I don't believe that means no one lives in that area. I would be interested to see the zones laid over some equivalent of census data.