Sunday, June 30, 2013
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.
Monday, June 24, 2013
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.
Friday, June 21, 2013
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.
Thursday, June 20, 2013
Participation Assignment: Using GIS to Identify Drug Markets
Map from the Wall Street Journal online
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.
Friday, June 14, 2013
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
Thursday, June 13, 2013
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.
Sunday, June 9, 2013
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.
Saturday, June 8, 2013
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.
Thursday, June 6, 2013
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.
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