Homeland Security 3: Protect

HLS3 Presentation

Process Summary:

Section 2.1: Prepare Protect Scenario Map 1

Created new ArcMap document using Military portrait template.

Added COUA_MEDS_Boundaries, Orthoimagery, and Geographic Names (GNIS) layers created in previous lab.

Zoomed to extent of orthoimagery layer.

Started editing, and created point for NORAD.  Opened attribute table and named point. Saved edits, and stopped editing.

Selected newly created NORAD point. Created 3 mile buffer around point.

Saved map document as ED_Colorado_2S1.mxd.

Section 2.2: Locate Critical Infrastructure

Selected features from COUA_GNIS file that were completely within the buffer created in section 2.1 (49 features.)

Summarized selected features by Feature_Cl.  Output table: ED_GNIS_Buffer_Features.dbf. added to map document.

Section 2.3: Protect Critical Infrastructure

Selected the one airport from the selected features in section 2.2.

Created 500 foot buffer around airport. Changed transparency of buffer layer to 50%. Labeled features by Feature_Na field.

Added Transportation layer created in previous lab. Turned on primary, secondary and local roads layers. Labeled roads by name.

Selected roads that intersect with the Airport buffer.  Using intersect tool, generated points where Local roads intersected with buffer boundary.

Saved map document as ED_Colorado_2S3a.mxd.

Turned on 3D Analyst extension. Added Elevation layer to map document.

Used Hillshade (3D Analyst) tool to generate ED_HS613074PM raster from elevation layer. Azimuth: 270, Altitude: 39.

Moved orthoimagery layer above hillshade layer in table of contents, and set transparency to 60%.

Saved file as ED_Colorado_2S3b.mxd.

Created new shapefile ED_surveil_pts in results folder. Edited coordinate system to match elevation layer, and added to map. Symbolized as 12 point red triangle.

Started editing, and added 15 points to ED_surveil_pts layer over NORAD layer.  Saved edits, stopped editing, and saved map as ED_Colorado_1S3c.mxd.

Turned off orthoimagery layer.

Using Viewshed (3D Analyst) tool, selected elevation layer as input surface, and ED_surveil_pts as observer points. Saved output as ED_viewshed0, added to map, with transparency 50%.

Added OFFSETA  short integer field to ED_surveil_pts attribute table, and gave each field a value of 10.

Repeated Viewshed analysis, and saved output raster as ED_viewshed10, with 50% transparency.

Saved map document as ED_Colorado_1S3d.mxd.

From the 3D Analyst toolbar, selected Create Line of Sight tool. Observer offset: 10, Target Height: 1. Created line of sight lines with this tool.

Selected one of the lines of sight created. Created Profile Graph from 3D Analyst toolbar button. Saved graph as ED_NORAD_LOS.

Launched ArcScene. Added Elevation layer, selected properties, and checked “Floating on a Custom Surface” button under Base Heights tab.

Added Orthoimagery layer, selected properties, and checked “Floating on a Custom Surface” button on Base Heights tab. Set transparency of layer to 60%. Copied and pasted line of sight lines from ArcMap to ArcScene. (Only one line would paste.)

Saved ArcScene document as ED_Colorado_2S3.sxd. Saved ArcMap document as ED_Colorado_2s3f.mxd.

Washington DC Crime

The DC Metropolitan Police Department is interested in patterns of crime relative to the location of existing police stations, to see if current patrols are effective or if adjustments may be necessary.

This is a basemap of the area showing roads, police stations, census blocks, and incidents of crime during August 2009.

Types of crimes committed were shown in the chart on the above map.  This is an expanded view of that chart.

We were interested in the proximity of crime to police stations.  This map shows buffers indicating 0.5 mi., 1 mi., and 2 mi. from the police stations. Two-thirds of the crime incidents during August 2009 occurred within one mile of the police stations.

It might make sense to put new stations at some distance from existing stations, and near identified areas of crime incidents. Of all possible locations, the southern-most spot, located outside of the Seventh Police District, is most needed.

Drilling down the data further from the last map, here is crime associated with the most proximal police station. This graph shows the number of crimes committed in August 2009, by nearest police station to which the crime incident occurred.


12.39% of the crime during this month occurred near the Seventh Police District.

The DC police department is also interested in patterns of aggravated assault, sex abuse crimes and homicides.

Burglaries occur far more often (by census block population) than sex abuse crimes or homicides.

In the District of Columbia there are drug-free zones within 1,000 ft. of schools. There are additional, mandatory, tough penalties for arrests related to drug crimes (use, purchase, or sale of illegal drugs) in these zones.

King Elementary school had the highest number of crimes (89) occurring within it's surrounding 1000 ft. buffer. Only one of these crimes was a drug-related offense.

Participation Activity - HLS1

Exploratory Spatial Data Analysis Techniques for Examining Urban Crime

The significant costs of crime, both monetary and social, drive interest in the use and development of techniques to investigate and understand criminal activity. Geographic information systems, along with crime mapping software, have proved to be a powerful tool, especially in the area of environmental criminology. Environmental criminology is concerned with determining whether the physical characteristics of an area promote or prevent crime.

GIS has been used to create displays of crime in specific areas, so they could be visually analyzed for patterns and trends.   It provides a platform for which relationships between layers of data can be easily queried, and inferences developed.

The possibilities for the use of GIS in crime spatial data analysis are far greater than just creating visual aids.  GIS can also be a stage for modeling future possible crime in a given area.  Further, the use of spatial statistical methods in analysis makes identifying statistical significant patterns of crime straightforward, and the output reliable for use by stakeholders.

Tornados Participation assignment

Geographic Information Systems and GIS analysts play a key role in the response to a natural disaster.  After a disaster has occurred, they quickly and simultaneously identify locations for decision makers to establish ground command posts and create a base map of the extent of the disaster in the community for these decision makers.  Communicating the scope of the disaster in the form of large visual aids and individual maps to government and public safety officials both on the ground and facilitating the relief elsewhere, is an important step in determining what supplies may be needed so they can start the process of obtaining them.  GIS analysts can also produce information on continuing primary hazards, such as fires created in the wake of a tornado, so that people in harms way can be warned or resources can be deployed to deal with the hazard.

Once this base map has been established, the process of analyzing the disaster area and immediate surroundings begins.  Analysts will put road, infrastructure, and building data into the GIS from before the event.  Because the GIS can be updated as reports of damage come in, the GIS will evolve as a tool in the recovery process too so communities know what will have to be replaced, and how this waste will have to be handled.  In the response process, schools and other public buildings on the edge of the disaster path with the proper access to basic resources can be identified as probable shelter locations.  The roads and transportation information will inform how additional supplies can be brought to these shelter locations and how people can travel to them.  Analysis of the primary disaster area for residential locations will generate paper maps, which can help recovery personnel locate any persons who couldn’t or didn’t evacuate before the disaster, and also avoid infrastructure hazards such as gas leaks from infrastructure damaged.  The GIS will also be used to prioritize reestablishing necessary public services such as hospitals and sanitation/waste disposal based upon how badly damaged the facilities are, how much they are needed, and their proximity to where people are sheltered.

The GIS is ultimately a necessary organizing tool, with analysts as operators, for response to natural disasters and will evolve into a tool to facilitate rapid recovery of the community. 

I created a very basic map of the Joplin tornado path here: ArcGIS Map Viewer.

Tornados!

This is a basemap of Tuscaloosa County, Alabama,  showing roads and schools.

The tornado took a bisecting path across the county.  This map shows a 0.5 and 1 mile buffer around the tornado path, as well as schools and roads located within the buffer.

The recent Joplin, MO tornado occurred in Jasper County, MO.  This is a basemap of the area, showing major roads and the location of schools.

The tornado cut a path in the South-West corner of the county.  This map shows the schools and roads impacted by the tornado, both within the tornado path itself and within a 0.5 mile and 1 mile buffer of the path.

A Quicktime animation of tornados occurring on April 27, 2011 is located here.  The points on the map correspond to tornados reported at times throughout the day on April 27th.

The April 27th tornados can also be viewed on a map in Google Earth.  The KMZ file used for the map shown in this screenshot of the Google Earth application is located here. Unfortunately, just clicking on this link will probably not start the Google Earth application (if the application is loaded on your computer), and will instead give you a 404 error.  Sorry!

Tornado Lab process summaries

Tuscaloosa Lab Process Summary

1.     Examined metadata of provided lab files.

2.     Created geodatabase. TornadoGDB.gdb

3.     Added shapefiles to the geodatabase. (Single/Multiple)

4.     Added aerial photos to the geodatabase.

5.     Created basemap of Tuscaloosa County.

a.     Shapefiles: Tuscaloosa_Cty, Schools, PrimaryRds, Aerial images.

6.     Saved file, exported to JPG.

7.     Copied 1^st .mxd file, and renamed Tornado2.mxd.

8.     Opened file, and added TCLBHM_Path shapefile, and changed name in TOC to Tuscaloosa Path.

9.     Selected Multiple Ring Buffer from Analysis Tools in ArcToolbox.

a.     Input: Tuscaloosa Path

b.     Output: Buffer (results folder)

c.     Distances: 0.5, 1

d.     Buffer Unit: Miles

10.  Clipped schools layer to Tornado Buffer.

11.  Performed location query to see how many schools were located within the tornado path, within 0.5 miles of the tornado path, and within one mile of the tornado path.

12.  Added Census Tract layer to map document.

13.  Downloaded population values by census tract from Census bureau website.

14.  Deleted fields in CSV file, and renamed headings to match attribute table of Census Tract layer in map document.

15.  Added this CSV table to map.

16.  Joined Census Tract layer, and Census Data table.

17.  Performed location query to see how many census tracts were impacted by tornado path. (“Target layers are within a distance of source layer feature”, “0 miles”)

18.  Labeled census tracts by population.

19.  Performed location query to see how many roads were impacted by tornado path. (“Target layers are within a distance of source layer feature”, “0 miles”)

20.  Created deliverable map.  Exported file to JPG, saved file and closed.

21.  Opened new map document. In ArcCatalog pane, right clicked on April27_UWF file in the Tornado/Data/Animation.gdb.

22.  Checked XY settings, and set coordinate system to GCS_WGS_1984.

23.  Exported file to Animation.gdb as Tornado_Paths as feature class.

24.  Set symbology of this layer to tornado symbol.

25.  Added US boundary layer.

26.  Enabled Animation toolbar. Selected Time tab, and changed settings.

a.     “Each feature has a single time field”

b.     Time Field: “Time_DATE3

c.     Field Format: <Date / Time>

d.     Display data cumulatively: checked.

27.  Opened animation controls.

a.     By duration: 20 seconds.

28.  Exported to video with default settings.

29.  Uploaded file to I: drive.

30.  Right clicked layer in ArcMap table of contents, and selected properties.

31.  On HTML Popup tab, checked “Show content for this layer using the HTML popup tool”.

32.  In ArcToolbox, under Conversion tools, selected “To KML, Layer to KML.”

33.  Input the Tornado_Paths file.  Saved output as Tornado_Paths.kmz.  Uploaded file to I: drive.

Joplin Lab Process Summary

1.     Downloaded data from some given sources, and also the Missouri Spatial Data Information Service.

2.     Imported layers into JoplinTornado.gdb, reprojecting them into the GCS_North_American_1983 coordinate system in the database if necessary.

3.     Added all layers to blank map document in ArcGIS, and symbolized appropriately.

4.     Created buffers around the tornado path with Multiple Ring Buffer tool at 0.5 miles and 1 miles from the tornado path as in the undergraduate section of this lab.

5.     Clipped schools within the buffer and tornado path as with the undergraduate section of this lab.

6.     Created two map deliverables; one basemap, and one of the tornado path.

Hurricanes

Hurricane Wilma, a category 3 storm, made landfall on October 19, 2005 at Key West, Florida.  The 8-foot storm surge associated with the hurricane was the source of the most damage to the area.  

This map shows the extent of the flooding due to the storm surge. The highest point in Key West is just over 12 ft. above sea level. 

Key West is a highly populated, developed area.  This graph gives a breakdown of the types of land flooded in the storm surge. 

To restore basic services in the city of Key West, it is important to know where they are located in comparison to the flooded areas.  Clearing roads will give access to hospitals and airports.  Assuming the infrastructure closest to the areas that did not flood is less damaged than areas experiencing a higher water level helps in prioritizing areas to be restored first.

Earthquakes

Earthquakes Part 1:

1.     Opened provided NewMadrid.mxd file in ArcMap.

2.     Turned on Quakes 5 layer.

3.     Turned on Urban Areas layer.

a.     Which major urban area is likely to suffer the most damage in this event?  Memphis, Tennessee

4.     Selected features likely to experience at least intensity VI-related damage.

a.     Select by Location -> Select features in Urban Areas layer that intersect features in New Madrid MMI layer. 91 features selected.

5.     Summarized DESCRIP field for selected features, and added to map as UrbanRisk table. Turned off Quakes 5 layer,

a.     How many urban centers would experience at least a VI level of intensity? 91

6.     Turned on Counties layer.

a.     Added field to attribute table to calculate Area Proportion. Field will represent persons per square meter.

b.     New field: Pop_Density, Type: double.

c.      Field Calculator -> [POP2001]/[Shape_Area]

7.     Overlaid population within MMI zones.

a.     ArcToolbox -> Analysis Tools -> Overlay toolset -> Intersect.

b.     Input features: Counties, New Madrid MMI

c.      Changed output feature class name to CountyMMI.

8.     Adjusted population totals to new areas.

a.     Added new Long Integer field (POP2001ADJ) to attribute table.

b.     Field Calculator -> [Pop_Density]*[Shape_Area]

9.     Summarized population within each of the MMI zones.

a.     Summarized MMI field

b.     Summary statistics: POP2001ADJ field by sum.

c.      Added resulting output table PopMMI to map.

d.     How many people live within the MMI zones? 60,099,857

e.     Created graph of people within each of the MMI zones.

10. Added new field to New Madrid MMI attribute table.

a.     Name: MMI_num, Type: Short Integer

b.     Populated field with numbers corresponding to the Roman numeral values in MMI field.

11. Turned on Interstates layer.

a.     Selected polygons with MMI value of 8 or higher, the intensities in which motor vehicle operation would be disturbed, according to the Modified Mercalli Scale.

b.     Extract toolbox -> Clip

c.      Clipped Interstates layer to selected features in New Madrid MMI.

d.     Output feature class: IstateRisk, added to map

e.     Renamed layer to Interstates at Risk, changed symbol to Highway with line width 1.

12. Turned on Rail layer

a.     Selected polygons on New Madrid MMI layer with MMI of 10 or greater, which are the intensities in which rails would be bent.

b.     Extract toolbox -> Clip

c.      Clipped Rail layer to selected features in New Madrid MMI.

d.     Output feature class: rrRisk, added to map

e.     Renamed layer to Railroads at Risk, changed symbol to railroad

13. Turned on Dams layer

a.     Selected polygons in New Madrid MMI layer that have MMI value greater than or equal to 8.

b.     Select by location -> Select features from Dams layer that are completely within the selected features of the New Madrid MMI layer.

c.      Created layer from selection, and changed name of layer to Dams at Risk.

14. Created deliverable map from output in Step 8 of lab/Step 12 in this process summary. 

15. Saved and closed file.

Earthquakes Part 2:

1.     Opened Northridge1.mxd in ArcMap.

2.     Checked to make sure Spatial Analyst extension was loaded.

a.     Customize Menu -> Extensions -> check Spatial Analyst box -> Close

3.     Turn on Building Status layer.

4.     Symbolized layer

a.     Properties dialog -> Symbology tab -> Categories -> Unique values à click  Add All Values

b.     Changed symbolization symbol to 1.00 point, and no outline.

c.      Under Advanced, chose Symbol Levels, and checked box to Draw This Layer using given symbol levels. Rearranged symbol levels.

5.     Create Building Damage Density map.

a.     ArcToolbox -> Spatial Analyst Toolbox -> Density -> Kernel Density

b.     Input point or polyline features: Building Status, Output raster: MyData\DmgPattern, Search Radius: 500, Population field: NONE, Output Cell Size: 100, Area Units: Sq. KM.

c.      Turned off Building Status and MMI layers.

d.     Under Color Selector properties for DmgPattern, checked box for Color is Null.

e.     Adjusted transparency to DmgPattern to 15%.

f.      Saved raster as layer file.  Added new file to map and removed original raster file.

g.     Renamed DmgPattern to Building Damage Density

6.     Turned on Geology layer.  Looked for correlation between damage concentration and rock unit. Turned off Geology layer.

7.     Turned on Liquefaction layer, and compared relationship between building damage pattern and liquefaction potential. Turned off Liquefaction layer.

8.     Turned on Stations layer to compare building damage to peak ground acceleration and peak ground velocity.

a.     For PGA:

                                               i.     ArcToolbox -> Spatial Analyst -> Interpolation Toolset -> Spline.

                                              ii.     Input point features: Stations, Z value field: PGA, Output raster: \MyData\PGA, Spline type: tension, Weight: 4, Points: 12, Output cell size: 100.

                                            iii.     Symbolized layer with 45% transparency and a yellow->red color ramp.

                                            iv.     Compare building damage pattern against PGA.

b.     For PGV:

                                               i.     Interpolated again, except used PGV for Z value field, and named output raster PGV.

                                              ii.     Symbolized layer with 45% transparency and yellow->red color ramp.

                                            iii.     Compared building damage pattern against PGV.

9.     Created deliverable for this portion of lab.

10. Saved file and exited ArcMap.

Earthquakes Part 3:

1.     Opened Northridge2.mxd in ArcMap

2.     File menu -> Add Data -> Add XY data -> Add NorthridgeAfter.csv file in data file.

a.     Lon is X field, Lat is Y field

b.     Spatial Reference Properties: WGS 1984.prj

c.      NorthridgeAfter.csv Events is added to map. Exported this data to NorthridgeAfter.shp in MyData folder. (Exported all features, in the same coordinate system as the data frame).

d.     Changed symbolization to a Dark Navy symbol, renamed layer to Northridge Aftershocks.

3.     Selected main shock. Created new layer from selection, and changed symbolization to a red triangle size 18 point.

4.     Cleared selection of main shock. Created new selection by attribute for all aftershocks of magnitude 3 or greater.

a.     Symbolized layer with graduated colors based on magnitude and a green->yellow->red color ramp.

b.     Only needed three classes.  Set number of decimal places to 1.

c.      Changed symbol levels to draw according to specified levels.

5.     Created deliverable for this portion of lab.

6.     Saved file and exited ArcMap.

Earthquakes Part 4:

1.     Opened Northridge2.mxd in ArcMap.

2.     Summarized aftershocks by magnitude over time.

a.     Earthquakes layer Attribute table à Summarized DaysAfter field, including maximum magnitude of aftershocks.

b.     Output table saved as Aftershocks.dbf, and added to map.

3.     Created and exported to JPG graphs of the amount of aftershocks.

a.     Aftershocks table -> Options menu -> Create Graph

                                               i.     Type: vertical bar, Layer/Table: Aftershocks, Value field: Count_DaysAfter, X field: None, Add to Legend: unchecked

4.     Created and exported to JPG graphs of the maximum magnitude of aftershocks.

a.     Aftershocks table -> Options menu -> Create Graph

                                               i.     Value field: Maximum_magnitude.

5.     Tried to create final map…had trouble with changing symbol drawing order.