Final Project: Location Analysis

 Links to final project. 

http://students.uwf.edu/snb22/Final _SBergeron_SlidesPres.pdf

http://students.uwf.edu/snb22/Final_power_point.pptx

Planning abd zoning for local goverment.

This week we working on planning and zoning in local communities.

Lab 9 Urban Planning

Participation assignment 2

Question 1) The Pinellas county appraisers office has a web mapping site   

but it is curently under construction. Here is the site http://www.pcpao.org/TAXPARCELVIEWERBETA.html

http://egis.pinellascounty.org/apps/PropertyAppraiser/paotpv/

Question2)What was the highest selling property for the month of June?

I was unable to locate this information on the Pinellas county appraiser's website. Perhaps it is because the site is currently under construction. However I used Zillow to obtain the requested information. The selling price of the property was $1,800,000. The previous selling information given listed the previous sell date as 12/26/12 for 225,000. Sounds like an amazing house flip/investment. WOW!

Question 3) The assessed land value is much much lower than the last sale price. The assessed land value is $137, 914 and the house sold on 5/1/13 for $268,000. I know because this is a family members house. The data is also not up to date, the owners name listed is stated as the previous owner, not the current. I checked my own home out of curiosity and my land value is $30, 000 less then I paid, and I have completely remodeled the house since.

Question 4)This home is one of Glenn Q Johnson's famous 'birdcage' homes in Pinellas Point. (This home was featured in Architectural Digest in the 50's).

Question 5) Based on size and location the land parcel 090310105 (the parcel in yellow) is valued much lower than the surrounding parcels of equal size and should be adjusted.

GIS Programming Manipulating Spatial Data

This week we used python to create a new geodatabase, move files from the data folder into the new geodatabase and using a for loop, we populated a dictionary with lists city's with there population.

GIS Applications MEDS Protect

In this weeks lab we created surety plans for the Boston Marathon. We located critical infrastructures (Critical care facilities within 3 mile of the finish-line. Establish crucial checkpoints around the finish line and established line of sight with elevation layers and hillshade tools.

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.

Geoprocessing in Arc

This week in GIS programming we used model builder and PythonWin to make tools. Above is a screenshot of the results of a tool I put together in model builder. It is a basin with soils data in which I have excluded the areas of poor soils from the polygons.

Apps in GIS Lahar mapping

 

This week in Applications in GIS we worked with vector and raster data in the area of Mt. Hood Oregon. First we manipulated raster elevation data to reflect "streams" or areas of possible lahar inundation. This was a process that took many steps but the outcome was a  vector file, a vector "stream" file from raster elevation data.....I thought that was pretty impressive.  We were then able to relate the possible lahar inundation areas to populated areas.

GIS Programming: Intro to Python

This week we went over Python scripting language and ran our first script (ran, not created) and I must say it was quite a useful one. It was a script that created organized folders and subfolders which we will be working from and adding data to all semester. Talk about an assignment that starts you off on the right foot…..Thank you!!

How it was ran:

1.I copied the folder from my R drive to my S drive.

2.I then right clicked on the file “CreateModuleFolders.py” and clicked again on “Edit with PythonWin”

3. This opened up PythonWin, from there I opened up the files tab and chose Run

Utilizing Remote Sensing and GIS for Conservation Research

Using GIS in remote sensing applications for conservation biology and research most interests me. In the simplest terms, remote sensing is the acquisition of data without being in the same physical location as the object you are collecting data from. This is a widely used technology. Remote sensing technology can be used to collect data on a broad rage in the form of aerial images that encompass whole ecosystems, or much more detailed data like the movements of a single animal. The latter is where my interests lie.

In order to conserve and manage species of interest it is critical to understand their movements (daily and large migratory trend) and there geographic range. Because researches can not follow most species for an extended time, this is done with satellite transmitters and GIS technology.  I was fortunate enough to use this technology when working on a leatherback sea turtle tracking project.  Leatherbacks are an excellent example of why remote sensing is so critical to conservation biology.  In the Pacific Ocean the leatherback population is plummeting, however researchers were having a hard time putting together a comprehensive picture of what was happening to the population because so little was known about their geographic location besides where females nests. However satellite transmitters, some fancy software, and a GIS allowed researchers to identify critical migratory corridors in the Pacific where leatherbacks are facing high mortality do to interaction with high concentrations of fisherman.  The Atlantic leatherbacks  on the other hand have no defined critical migratory corridors and that is a large factor in why the Atlantic population is faring better.  This data allows policy makers to move forward to help enact laws or conservation plans to prevent high mortality in the Pacific.

This type of remote sensing doesn’t work or all species. I am eager to learn about other methods in which remote sensing can be utilized to identify critical species habitat.