GIS4048 Module 6: Homeland Security - Prepare MEDS

The focus of this week's module was the compilation of a Minimum Essential Data Set (MEDS) to support a standards-based geospatial model for GIS systems as developed by the Department of Homeland Security in the interest of prevention and protection of areas during a time of crisis. Having a standards-based geospatial model founded on a MEDS provides a uniform basis for any community or organization before and during a time of crisis, whether security- or hazard-related. The uniformity of the system ensures that at the time of crisis, the system will be available and useful to anyone involved with crisis planning or recovery. MEDS have already been created for large metropolitan areas through the cooperation of local, state, and federal government agencies.

Data for MEDS are compiled from several different internet sources; shapefiles, rasters, tables, and geodatabases are included. Layers to be included in MEDS are: orthoimagery, elevation, hydrography, transportation, boundaries, structures, land cover, and geographic names. Data must be in North America Datum of 1983 to meet MEDS specifications.

The task for this particular exercise was to identify and put together an essential dataset in preparation for the homeland security crisis of the Boston Marathon bombing. All of the necessary data for this module was downloaded from National Map Viewer and provided. After organizing the map document with a default geodatabase and stipulating the data frame's units (meters), group layers were created to organize the required MEDS layers. Each created group layer was populated with the applicable data layers. The Boston Metropolitan Statistical Area (BMSA) layers were added to the Boundaries group layer. (BMSA includes Boston, Brookline, Cambridge, Chelsea, Everett, Quincy, Revere, Somerville, and Winthrop along with a 10-mile buffer around the outer extent.) The transportation group layer was populated with three road layers (local, primary, and secondary) which were created by using Joins and Relates to join the attributes from the CFCC table to the BMSA Roads layer and then selecting by atttributes to create the three aforementioned layers. The selections were exported data (matching the coordinate system of the receiving geodatabase) and then added to the map as layers which were then adjusted for symbology and labels. Adjustments were made to the Scale Range for each transportation layer as well as the transportation labels so that clarity would reign supreme with any changes in scale.

The Hydrography group layer was populated with three feature classes that had been provided, and Orthoimagery and Elevation group layers were populated with their respective raster datasets. Working with the Landcover raster was quite a lot of fun. After using Extract by Mask to limit the area covered by the raster to only that of the BMSA, the symbology was altered using more meaningful colors imported with a color map created from the National Land Cover Database 2006 Legend. Those colors portray the types of landcover much more adequately than the random colors. Labels for the landcovers were also changed to match the color map labels.

In order to add geographic names from the Geographic Names Information System data file, the text file format was first changed from CSVDelimited to Delimited(|) format to create columns of information. The x,y coordinates of the table were then used as input to create a feature class. After saving the output to the geodatabase, the layer was added to Geographic Names group layer and then projected to the State Plane system (the re-projected layer was also added to the Geographic Names group layer). From this re-projected layer, only the features that were entirely within the 6 counties of the BMSA were selected using Select by Attributes and then Select by Location. This final selection of geographic names was exported as data, added to the map, and moved into the Geographic Names group layer as well before removing the other layer.

The creation of these seven group layers was essentially the staging portion of the geospatial model to follow. In order to make the layers accessible to others who might need to use them, each group layer was saved as a layer file. With the preparation part complete, the next step is to protect ~ the focus of next week's module.

GIS4048 Module 5: Homeland Security - Washington, DC Crime

The purpose of this module was to perform as a District of Columbia Metropolitan Police Department GIS Analyst who is responsible for crime analysis. The goal was to help allocate resources by analyzing January 2011 crime statistics for DC. Specifically, the project involved determining the following:

• whether current patrols are effective as is or need to be altered based on crime patterns in proximity to police stations
• patterns of aggravated assault, homicide, and theft-related crimes

The first map depicts each existing police station's location and percentage of the District of Columbia's total crime for the month. The locations of individual crimes were included for a visual comparison of crimes in relation to existing police stations. Proposed police station locations were based on analysis of crimes in proximity to existing stations, proximity of stations to other stations, and availability of land or existing building for a new station.

Proposed police station locations were based on the data showing that most crimes occur 1/2 to 1 mile from a police station. This was done by applying a multiple ring buffer around each station before performing a spatial join between the crime data layer and the buffers. The 6th and 7th District police stations both had relatively high percentages of crime occuring close to them. Having lived in southeast Washington in the past, I know that that area's reputation for higher crime rates in general is common knowledge to District residents. (One of my roommates there was mugged not once, but twice. In the same night.) The proposed location south of the 7th District police station was selected because of the 7th’s highest rate of crime occurring close to it along with the numerous crimes that have occurred in extreme southern DC south of the 7th District where no police stations are located currently. (A large empty parcel is situated at Mississippi and 10th Place SE). Wherever an additional station to reduce crime around the 7th District is located, it should be east of the Anacostia River to minimize response time.

Also in southeast Washington, the 6th District has a relatively high crime rate occurring near it as well, but perhaps personnel could be transferred from the 6th Substation, 1st District Station, or 1st District Substation, all of which have significantly lower crime rates than the 6th District.

Additionally, DC would benefit from having another station located south of the 3rd District police station (12.56% of total crimes) and west of the Asian Liaison Unit with its 9.1% crime rate occurring in its neighborhood. There is a lot of overlap of police station buffer zones north of the 3rd District station and east of the Asian Liaison Unit, but the areas south of the 3rd District and west of the Asian Liaison Unit have a significant number of crimes and there is no other police station from that area all the way out to the DC boundary. The area of 20th St. NW and L St NW has commercial buildings which might have sufficient space available to accommodate a substation. Percentages of crime, police station locations, and proposed station locations area shown on the first map:

Analysis of Crimes Committed in District of Columbia in January 2011

The density maps of burglaries, homicides, and sex abuse crimes were created using the kernel density method with a radius of 605 (area in square kilometers). The density maps reveal similar patterns to those shown with the previous map: higher concentrations of crime in the southeast quadrant of DC as well as central DC.

Density Maps of Burglaries, Sex Abuse, and Homicides
in Washington DC (January 2011 data)

In general, theft in its various forms, burglary, and robbery were the most common crimes. Because these maps were created by a DC MPD department GIS analyst and are intended for internal use, a locator map was not included. The first map is a bit busy with the locations of individual crimes shown, but seeing those locations of specific crimes helps determine patterns, and the visual aspect reinforces the statistics and written analysis. Some portions of the District of Columbia are noticeably free of crime incidents. Among other reasons, this may be due to those areas being green spaces where fewer incidents of robbery and theft-related crimes would be expected. Another reason is that there are many federal parks (Presidents’ Park, The National Mall, The Ellipse, etc.) in DC which are patrolled by US Park Police. I suspect that crimes handled by US Park Police are not included in the DC police department statistics.

This module was very extensive and very informative. Again, the value of GIS and the wealth of information that can be illustrated with GIS was demonstrated. Throughout this module, much experimentation was done to find optimal color schemes, labeling techniques, layer order, transparency, and so forth to determine the most optimal combination to illustrate the information clearly and completely. A feature that was especially invaluable for this particular analysis was the Identify feature. As in other modules, this experience has left me with the desire to know how to accomplish other things in ArcGIS.

GIS 4102 GIS Programming Module 5: Geoprocessing with Python

This week's module included using tools after setting the workspace. This specific script used the AddXY tool, the Buffer tool, and the Dissolve tool to add XY coordinates to a hospitals layer, add a buffer of 1000 meters around each hospital, and finally dissolve the buffers into a separate, single feature. Prior to using these tools, a copy of the original layer was created in order to retain that information unchanged. Since variables are very helpful in creating code that is reusable and can be shared, I took this opportunity to practice assigning variables to input and output features. There was also an opportunity to apply Comment Out Region to a section of code while making adjustments to the code. The script included code to print out the tool messages directly after each tool was run. These are shown in the image below.

PythonWin is my preferred place for scripting. Throughout the scripting process I double-checked that results were added to ArcCatalog. After the script was completed, it was run in ArcMap; the code running through its cycle and adding the results of each step to a map was quite amazing to watch. With each Python exercise and assignment, terms and coding are becoming more familiar making scripting easier overall.

Tool Messages Printed after Each Tool Ran

GIS4102 Participation Assignment #1: Ethics and Privacy Issues in the Use of GIS

The following is a summary of Ethics and Privacy Issues in the Use of GIS by Amy J. Blatt, Journal of Map and Geography Libraries, 8:80-84, 2012.

The increasing use of GIS across diverse disciplines includes ethics and privacy matters which Amy J. Blatt addresses in "Ethics and Privacy in the Use of GIS". Blatt focused on academic map and geography libraries' handling and distribution of geospatial datasets which are now being distributed to health care delivery and service providers outside the realm of medical research. Blatt stresses that map and geography librarians must be aware of privacy and ethics issues when individuals' personal health and cadastral data are used including in planning and medical geography. For instance, public health researchers can obtain patient records for research purposes. This information (names, addresses, phone, social security numbers, account numbers, images, etc.) is protected by HIPAA's privacy rules to prevent unauthorized use and must be protected while being transferred electronically.

Blatt also discussed planning. Cadastral datasets can include owner's name, address, assessed value, property boundaries, and more. At the time of the article's publication, there was little federal protection of this information. Local municipalities and jurisdictions allow individual, personal information to be made public as they see fit. (Identical information collected by the US Census Bureau may not be published.) Since there are no laws forbidding acquiring and cross-referencing parcel data and individual sales data, a market research firm could do this legally, although it would raise ethical issues.

Blatt suggests that map and geography libraries ensure that HIPAA regulations are followed when handling and distributing personal medical data; include comprehensive metadata with all datasets, and develop codes of conduct which address uses (ethical and unethical) of geospatial data. She concludes with stating that when no federal laws govern the use of geospatial data, individuals are responsible for determining whether their use of GIS (proposed or intended) would be viewed as unethical by those affected.

While searching for an article to summarize, the following article and website of interest were also discovered:

http://www.spatial.maine.edu/~onsrud/GISlaw.htm link to additional resources (hasn’t been updated for a few years)

http://www.geolawpc.com/faq.html law firm specializing in “spatial law”

GIS4048 Module 4: Natural Hazards - Hurricanes

Just in time for the beginning of hurricane season, this module presented an opportunity to work as a contract employee of the Federal Emergency Management Agency (FEMA). Of interest to FEMA in this scenario was an inventory of structures with structure damage, wind damage or inundation as a result of Hurricane Sandy’s landfall in New Jersey.

Although only classified as a Category 1 hurricane at landfall on the New Jersey coast, Hurricane Sandy was a catastrophic storm due to its enormous size and other factors which amplified the storm surge. States all along the Atlantic coast were impacted to some degree by Hurricane Sandy. Ten states and the District of Columbia were declared major disaster areas by FEMA, making them eligible for funding from the President's Disaster Relief Fund which is managed by FEMA and other federal agencies.

To meet FEMA’s objectives, the track of Hurricane Sandy was plotted. This was done by adding an Excel file to the map and exporting the data to a geodatabase. The Points to Line tool was used to show Hurricane Sandy’s track. That information is shown along with wind speed and barometric pressure on the map below along with the states and district that were declared disaster areas by FEMA. Graticules were added to this map as well. 4° intervals were selected for the graticules since 2° intervals are too closely spaced and 5° intervals are not as easily interpolated as 4° intervals.

Hurricane Sandy Track and
States/District with Major Disaster Declarations

Geodatabase datasets were created for mosaicking two separate sets of aerial imagery (before and after Hurricane Sandy) and imagery effects tools were utilized for comparing pre- and post-hurricane imagery. Attribute domains were created and used to catalog damage in order to compile an inventory of structures along one block of a street in New Jersey. The difficulty with this part of the exercise was determining the degree of damage. Without seeing elevation views of the structures, the extent of any damage was hard to assess. The following map shows the damage assessment and the pre- and post-imagery of structures which were cataloged along one block. 

Hurricane Sandy Damage Assessment
Coolidge Avenue, Toms River Township, New Jersey (2012)

Information from maps such as these would assist FEMA and other organizations to evaluate the extent of damage after a hurricane and to determine how best to allocate resources.

GIS4102 Module 4: Python Fundamentals Part II

The focus of this week's exercise and assignment was continuing practice of and developing familiarity with Python scripting. The emphasis was on importing modules, troubleshooting code (many opportunities arise for this in my particular case), creating while loops and conditional statements, adding comments, and iterating variables within loops. The first portion of the assignment involved the import of a module and troubleshooting the code provided for a dice game with random number generation.

The script-creating portion of this assignment included developing a while loop to add 20 random integers between 0 and 10 to a list. From that list one number was eliminated and the number of times it had occurred was printed in a message. For random-generations in which the number did not occur, a different message was printed. The screenshots included here show an example of each message:

The Number 5 Eliminated from List
and Associated Message

The Number 5 Does Not Occur in List
with Associated Message

The error message returned at the second while loop I initially coded was a bit deceptive, notifying me that my variable was not in the list although it could be printed out. After trying to troubleshoot it in one manner, I finally tried another tactic which solved the problem. Those while loops certainly do want to have an exit condition in order to process.

Throughout the script, descriptive comments were added. After the script was completed, header comments were included to identify the script name, author, date, and a brief description of the script. This was a great practice exercise and assignment.

GIS4102 Module 3: Python Fundamentals Part 1

After last week's experience with scripting, this lab assignment was a breeze. Practicing within the exercise part of the module helped prepare for the assignment which involved starting with a string of my full name, splitting it into a list of names prior to printing the last name, and then determining the length of the last name. Mine is 13 ~ did anyone top that? Finally the length of the last name was multiplied by three. Trying to remember the functions and methods took some effort, but the book and instructions provided handy reference. I was most nervous about making sure that my name was spelled right, since it would be out there for all to see:

Results: Length of Last Name Times 3

The scripting was so fascinating that I ended up trying it out with the names of several friends and family members. I can tell that this is going to be a lot of fun... 

GIS4048 Module 3: Natural Hazards - Tsunami

The March 2011 tsunami that devastated Japan served as a reminder of the importance of planning for natural and nuclear disasters. GIS provides a comprehensive system for producing maps and graphics to aid in such natural and nuclear disaster planning. This lab assignment tasked students with mapping the evacuation zones around the Fukushima Nuclear Power Point as well as determining the at-risk populations within each of those zones. The lab also included determining evacuation zones based on actual measurements of tsunami "run up" (observed maximum water height above a reference sea level) and identifying at-risk cities, roads, and nuclear power plants in those zones. This information was consolidated in one map:

Evacuation Zones in Vicinity of Fukushima II Nuclear Power Plant

Production of the map began with creating a file geodatabase to keep data organized. Feature datasets were created in the file geodatabase and then populated with imported shapefiles. All of the feature datasets were assigned the same projected coordinate system (WGS 1984 UTM 54N). One Excel file was converted and added to the geodatabase. As it was for several other students, creating raster datasets and mosaicking the DEMs caused plenty of issues. Many thanks go out to Brandon and Aaron for their very helpful discussion board posts which resulted in successful completion of that part of the lab. All of this organized data then was used to complete the second portion of the lab.

The second portion of the lab included creation of another file geodatabase and data creation using selection by location, buffering and clipping, extracting by mask, creating a graph (to determine breakpoints for classes of data), and model building. The model built was very involved and awesome to see in action. The flexibility and ease with which changes could be made to the model were appreciated. Another new technique learned in this lab was how to build label expressions. While many of the processes (especially those used to determine the run-up evacuation zones) are not among those that I use often, with continued practice I expect that they will be easier to use and troubleshoot should the need arise.

Finally, the information was synthesized into one map showing the evacuation zones around the Fukushima Nuclear Power Plant and the evacuation zones for tsunami run-up. This was a lot of information to pack into a single map, but, as the saying goes, "A picture is worth a thousand words". In all honesty, now I am feeling a bit vulnerable about living 75 feet above sea level.