GIS Programming, Lab 5: Geoprocessing with Python

This week in lab, we learned about geoprocessing with Python.  I found this lab to be a little stressful, but very rewarding once I was able to figure it out.  The outline of it was to create a script that would add XY coordinates to the hospitals shapefile, create a 1000 meter buffer around each hospital point and then dissolve the buffer layer.  Below is a screenshot of the Interactive Window using PythonWin once my code was successfully run.

First, I added my comments to the script.  Those included my name, the date, the script name and a brief description of what the end result should turn up.  I imported arcpy and set the environments.  I made sure I was able to run the script more than once using overwriteOutput.  The next step was to use the AddXY tool.  I used the Help section of ArcGIS to look at a sample code and syntax for the AddXY function.  After that, I turned, once again, to the Help section to look at sample code and syntax for the Buffer tool.  Here I needed to write a code that would create a 1000 meter buffer around each hospital point.  Once that was done, I needed to dissolve the buffers together.  Using the ArcGIS Help section, I reviewed sample code and the syntax for the dissolve function.  Finally, I made sure that the comments appeared after each step of code, letting me know it was run successfully.

This lab definitely helped me get a better grasp and understanding of using the tools in Python.

Applications in GIS, Lab 6: Homeland Security - Prepare MEDS

This is a screen shot of the minimum essential dataset set forth by the Department of Homeland Security for the Boston Metropolitan Statistical Area.  Group layers were created and distributed by boundaries, hydrography and transportation.  The .lyr files contain all the symbology and scale for each group for ease of use later on in the next lab.

Applications in GIS, Lab 5: HLS - Washington D.C. Crime

This week was the introduction into how GIS is used in Homeland Security.  We focused on the crime in Washington D.C. and used various techniques to analyze it.  This first map is a map is a basic outline of Washington D.C. with all of the police stations labeled.  You can see where all the crime take place in relation to the population density. 

Washington D.C. Crime and a graph displaying what types of crimes occurred the most often in January of 2011.

The second map we created, focuses more on the police stations.  We broke down the crimes based on each police station and determined where new substations would work well. 

The red proposed substations were placed in areas with high crime rates and were relatively far from existing stations.  

 The third and final map we created, we used a kernel density tool.  I had a lot of issues with this tool, but was able to overcome them with the help of the professors and some fresh data.  In this map, I keyed in on three different types of crime, burglary, sex abuse and homicide.  I also provided an inset map of the population density for comparison reasons.

Density of crime: Burglary, Sex Abuse and Homicide in Washington D.C. in January of 2011.

This week was definitely challenging but it's always fun to learn new things and find new ways to work around and fix problems.

Applications in GIS, Participation Assignment #1

Article Outline: The Incident Map Symbology Story
Link: http://www.napsgfoundation.org/blog/napsg-blog/131-the-incident-map-symbology-story
By: Lt. Chris Rogers
Date Published: May 4, 2012
Outlined by: Lynne Johnson
Date: June 11, 2013

As you can imagine, maps are very useful tools when it comes to emergency response.  They can provide a wealth of information about previously existing data along with the current emergency situation, whatever that may be.  Across many agencies, maps are indicative to success, however, inconsistencies can be more burden than help.  This article emphasizes the need to have consistent symbology in maps across different first responding agencies.  They refer to this consistency as the “Common Operating Picture.”  This is an important issue because if that one group, or even, one person isn’t available to understand and convey the map then emergency response could be delayed.  And a delay like that could cost lives.  The goal of this project was to come up with universal symbols that would have the same meaning across various agencies all while:

• Setting guidelines instead of standards
• Creating symbols that can be hand drawn
• Having symbology what doesn’t require a lot of training to understand
• Creating symbolofy that is usable in routine business of a public safety agency.

This problem was approached with the help from NAPSG Foundation, the DHS S&T First Responder Group and FEMA's National Integration Center.  A small group of first responder practitioners with knowledge of GIS were put together and posed with this issue.  Keeping in mind the bulleted points above, they used a probable situation of a building fire to create a map with working symbology.  Initially, they had two conference calls to get the ball rolling with ideas and end goals which eventually led to several in-person meetings to go over what they had come up with.  The correspondence between several agencies to work on this problem was a particularly smart method.  It allowed for numerous difference view points all while having the same end goal.

The result was that they came up with different categories of symbols for “pre-incident, hazard and incident command.”  Specific key factors are that:
• “The shape of symbols is defined by the category”
• “What is IN the shape is dependent on the map output and use”
• “Can be hand drawn”

These results are based on a small-scale incident; however, the symbology required will be used across a wide variety of incidents, including large scale.  They recognize that this is only a small step in the right direction for map consistency throughout various agencies for emergency response. However, with the progress they made other groups can learn from their methods and use their results off of which to build.  This article provides a great example of how GIS can aid in crime analysis, but mostly aid in law enforcement.  Trends can be tracked about incidents; several attributes coinciding with the incident can be noted and analyzed.  This article relates to this week’s lecture and lab because it speaks to keeping our communities safe.  Once we gain consistent symbology, we can move on to prevent and mitigate incidents.

Applications in GIS, Lab 4: Hurricanes

This past week in lab, we still focused on natural disasters - however, we keyed in on hurricanes.  More specifically, Hurricane Sandy.  We worked on plotting a hurricanes path, turning points to a path using the Point to Line tool.  We also learned a little more about creating customized markers - in this case the hurricane symbols.  We labeled each point with the MPH winds and barometric pressure; two key elements in hurricane categorizing.

Hurricane Sandy's path up the Atlantic Coast starting as a tropical depression and ending as a post-tropical cyclone. 

The second map we created, we used aerial images of the New Jersey shore that were taken before the hurricane hit and after.  We learned about cool effects that can be used to compare before and after pictures almost immediately.  We created new data by creating attribute domains - which allowed for the classification of each building. Each building structure was categorized based on the amount and type of damage sustained.  

Before and after structure damage comparison of Fielder Avenue in Ocean County, NJ.

GIS Programming, Participation #1

The GIS Institute is a non-profit with many focuses.  One is on reducing, preventing and eliminating plastic waste in our oceans.  This post first outlines the problem and where it is occurring the most.  The epicenter of marine debris is the North Pacific gyre convergence zone, or better known as the “Pacific Garbage Patch (PGP).”  The way the ocean currents run, most marine debris (mostly plastic) is brought to this area and can never leave.  Recently, the PGP has gained national and world attention, along with the need to clean it up.  It is such a massive undertaking but ideas are flying. 

GIS is being introduced in the efforts.  This post outlines how GIS can help.  Once the data is gathered, it can be mapped and analyzed in ArcGIS.  There is so much data and information attached to a collection of marine debris (along with GPS info) that it would need to be organized.  With GIS, maps can be made to organize beach clean ups, create models depicting how it ends up out there, how we can prevent/reduce that, make future projections based on past events (like the 2011 tsunami).  This information can be shared with the public and, perhaps, progress can be made. 

Another aspect in which, GIS is being used in relation to marine conservation, is studying the impact on marine life.  Sea turtles are common victims, but they are easily tracked!  Attaching a tracking device to the shell of a sea turtle provides a wealth of data.  That data can then be analyzed using ArcGIS.  Steps can be taken, lessons can be learned and turtles can be saved!

Link: http://thegisinstitute.org/blog/gyres-and-marine-debris/

Source:  Stephens, Drew. “Gyres and Marine Debris.” The GIS Institute; Connecting People, Planet and Geotechnology. WordPress. Blog. May 30, 2013.

GIS Programming, Lab 4: Python Fundamentals - Part 2

This weeks lab was a little more difficult.  We focused on modules, conditional statements, loops (for and while) and learned how to leave comments and instructions in a script.  We were given a partial script (with numerous errors) and we had to figure out what was wrong, correct it and then write a loop.

Results of a dice game

The result of the corrected script was a dice game.  It took into account the length of the players names, a random "die roll" and that determined whether the players won, lost or tied.   It ran through all of the players, giving them each a turn.  At the end we had to create a loop that printed the numbers 0-5.

Applications in GIS, Lab 3: Tsunami (Bonus Assignment)

 We were given the opportunity to work on a bonus assignment this week.  For this map, we did a run-up analysis focusing on the Fukushima coastline.  We initially figured out the elevations of the coastline and used that to determine evacuation zones.  Obviously, the lowest lying coastal regions were classified in Zone 1, which is at a "Severe Risk."  From there we used classifications of "High Risk" being Zone 2 and "Elevated Risk" as Zone 3.

Evacuation zones based on elevation and level of danger.

Because so much data goes into creating a map like this one, it can take a lot of time and redundant steps.  Creating a model using ModelBuilder allows the us to run all these tasks at once and gives us the outputs we need.  One benefit of ModelBuilder is that you can immediately use generated outputs as the next step's input data.  I enjoyed getting more in depth with ModelBuilder.

Model created by ModelBuilder