Japan Tsunami Lab

This lab was in two parts and was based on the Tsunami that hit Japan and damaged the Fukushima Nuclear Power Plant.  We assessed the areas damaged, the evacuation zones at different distances and the population affected. 

The objectives for the first part of the lab were to:
- Understanding geodatabases in ArcGIS
- Creating a file geodatabase in ArcCatalog

The objectives for the second part of the lab were to:
- Create and work from a file geodatabase in ArcMap
- Map the evacuation zones surrounding the Fukushima-Daiichi Nuclear Power Plant
- Determine the at-risk population within each of those zones

This lab was a little easier for me than the last two until I tried to put the final map together. As I was trying to upload the final map here I noticed that it is missing elements, important ones. This screenshot shows what the map looks like in ArcMap - Print Preview.

This is what is apparently showing up as the exported jpg:

Part I: Building a File Geodatabase

1)      Reviewed all the layers available

2)      Created a new file geodatabase “Tsunami.gdb” and added the feature datasets with WGS1984_UTM54N projections:

1.      Transportation

2.      Damage_Assessment

3.      Administrative

3)      Added the layers to the appropriate datasets.

4)      Built a new raster dataset from the DEM elevation rasters using the Data Management tools – Build Raster Attribute Table.

5)      Created Raster Dataset using New – Raster Dataset in ArcCatolog.

Part II: Fukushima Radiation Exposure Zones

1)      Added the layers:

a.       JpnBndOutline

b.      NuclearPwrPlnt

c.       JapanCities

d.      Roads

e.      NEJapanPrefectures

2)      Isolated the Fukushima Power Plant

3)      Created a Multi-ring Buffer using the ‘Multi-Ring Buffer’ in the Proximity tool

a.       Created layer in the Damage_Assessment Feature Class

4)      Clipped the Buffer so it was shown just on the land using the ‘Clip’ tool

5)      Clipped the roads layer and made them not so noticeable

6)      Ran the Select By Location Search Query to find the cities within 50 miles of the Fukushima Power Plant.

7)      Created labels for the cities and populations in Japan using [City]&vbnewline&[Pop]

8)      Created map with:

a.       Evacuation Zones

b.      Cities labeled

c.       Fukushima Power Plan labeled

d.      An inset map of Jap and the NE region

e.      With all essential map elements

9)      The putting together of the final map deliverable did not go as smoothly as usual.  The Data View worked perfectly but the Layout View did not.  I could not get the map to zoom to a view that I was satisfied with.  I tried several things:

a.       Closing and restarting ArcMap and computer

b.      Copying layers and starting a new map as suggested by one of the students (this made it so the Layout View did not show up at all

c.       I tried what one of the other students suggested, checking the projections – these were fine (but something to keep in mind for the future!)

d.      I gave up after several days of this and put together the map which has all the relevant information but does not look quite like I had wanted it to.

10)  Saved map as FukushimaEvacuationZones_cmb.mxd and exported as a jpg

Washington DC Crime Lab

This week we worked on statistics on crime in Washington, DC.  We created 3 maps and a bar graph with the following objectives:
- Create maps showing crime distribution in  Washington, DC
- Create graphs to demonstrate crime distribution
- Use Kernel Density to display crime clusters
- Geocode addresses in ArcMap to map police stations
- Create multiple dataframe maps in ArcMap to show various crime distributions across DC
- Use Field Calculator to calculate attribute table values
- Utilize Swipe Tool to allow crime cluster distributions to be displayed over census block data.

Process Summary Details

Step 2:

1)      Set the environments and predefined Projected Coordinate Systems to NAD 1983_Zone_18N, the current workspace as WashingtonDC\data and scratch workspace as \results\DC_results.gdb.

2)      Set the cell size to 73 and limited the analysis by setting the mask to ‘WashingtonDC’

3)      Added layers: ‘police_stations.csv’, WashingtonDC.shp and DC-streets

4)      Selected the geocode address ‘North America Geocode Service, saved the output layer as “police_stations” as a geodatabase feature class in the DC_results.gdb folder.

5)      Added excel file ‘DC_Jan2011.csv’ and added the coordinate system GCS_North_American_1983

6)      The scale is 1:160,978.

7)      There is no Object_ID Field

8)      Added the new table “Crime”

9)      Symbolized the police stations and made them much larger.

10)  Symbolized the DC_Streets layer.

11)  Summarized the ‘Offense’ data and saved as a dBASE table and changed the labels in the attribute table.

12)  Created graph

13)  I worked on the graph to make it look right because it was totally messed up, fields on top of eachother, font all out of order, etc

14)  Saved as Crime1.mxd and exported as jpg.

STEP 3: Produce a Map of Police Stations with Crime Proximity

1)      Set the environments and predefined Projected Coordinate Systems to NAD 1983_Zone_18N, the current workspace as WashingtonDC\data and scratch workspace as \results\DC_results.gdb.

2)      Set the cell size to 73 and limited the analysis by setting the mask to ‘WashingtonDC’

3)      Added the WashingtonDC boundary, police_stations, DCstreets and crime layers.

4)      Created a Multiple Ring Buffer around the police stations at 0.5, 1 and 2 miles.

5)      Performed a spatial join between buffer and crime.

6)      Added an Event field in Crime attribute table with a value as 1.

7)      Joined the Buffer and Crime layers

8)      Had an issue with the attribute table not being populated – many of the columns were set as “null”.  I deleted the laters and tried again with the same result.

9)      Used “joins and relates” tool to join the police_stations layer with the crime layer with an output name “pol_sta_crimes” – This did not work either.

10) 

11)  I could not get any further. I asked on the board for help and email Tanya.

STEP 4: Produce Density Maps of Burglaries, Homicide, and Sex Abuse Crimes

1)      Set the environments and predefined Projected Coordinate Systems to NAD 1983_Zone_18N, the current workspace as WashingtonDC\data and scratch workspace as \results\DC_results.gdb.

2)      Set the cell size to 73 and limited the analysis by setting the mask to ‘WashingtonDC’

3)      Added WashingtonDC and Crime layers.

4)      Ran a Selection Query for burglaries and crime.

5)      Used the Kernel Density on the crime layer and the population field Event. The Kernel Density actually seemed to work.

6)      Everything seems set as it should be but the results are not coming out right…this is he screen I am getting when I try to classify the crime density:

7)     

I don’t know what to do from here. Once again I am at the end of the assignment, I have worked nearly every hour available outside of work and I am not done because I keep having issue after issue.

 

Law Enforcement Participation Activity

Title of Article: The Incident Map Symbology Story

Original Article Written by: Lt, Chris Rogers, Kirkland Fire Department

Summary Written by: Catherine Bronson

Date Posted: June 21, 2012

            We all know that certain symbols are universal in a particular community or society.  We all know that a red sign at the end of the street means stop, a green light means go and a yellow triangle means caution.  Maps are the same way for those who need to use them.  We all know that N means north on a map.  First responders of all types need maps to negotiate where they are, where they are going and to make quick analysis of dangerous situations.  In order to do this, the maps they use need to have symbols that are easily recognizable.  These maps come in many forms, from paper to computer, some hand drawn and some computer generated.  Many of these first responders are now using GIS for construction of their maps and integrating their data.  The problem arises when different types of first responders use different symbols and different map forms but need to work together.  A specific incident may involve local organizations such as town police or fire, state organizations such as spill response units, state police and health and natural resources officials along with federal organizations like FEMA and the Red Cross.  They may all be using different forms of maps such as:

            Street Maps

            Emergency or building pre-plans

            Incident command and control applications

Damage assessment applications

Crisis management systems

And many others

Different responders have different terminology, priorities and data needed.  However, they all have the same goals of protection, fast response and mitigation and need to work together as a group to achieve these goals and therefore need consistent data.  According to Lt. Rogers, this is often called a Common Operating Picture.

            A common symbol known by various responders makes critical and timely decision making easier and clearer.  The problem arises that different organizations have different needs and different terminology.  An example of a success story for standardization of symbols is the National Wildfire Coordination Group.  This standardization of map symbology makes maps clearer and easier to understand.  However, this symbology does not work for other groups.  Another group under the US Department of Homeland Security attempted to create symbols included into four categories: Incident, Infrastructure, Sensors and Command Features.  There were issues with this set of symbols such as they were too graphical and hard to hand draw, some symbols were vague in their purpose due to a variety of definitions in various agencies and although over 200 symbols were created, there were still a lot missing.

As a solution to this problem was the creation of a group with the help of the NAPSG Foundation.  This group consists of people representing various first responder organization and requiring that the members have practical hands on experience as emergency responders and practical knowledge of mapping and GIS.  This group of responders were first presented with a challenge to create a map that showed the features: Hazards on an incident, Features that can help mitigate and incident and Mapping where command functions are located.  They then presented the map to the group.  At the in-person meeting they discussed each map and looked for common features.  What was decided at the meeting was the following requirements:

            A set of guidelines instead of standards

            Symbols that can be hand drawn

            Symbology that does not require a lot of training to understand

            Symbology must be usable in routine business of public safety agencies

Along with the acknowledgement that two types of maps are needed, a Tactical Map with a single problem emergency and a Strategic Map for a multiple problem emergency.   Below is an example of these types of symbols on a Strategic Map.

           

           

            This group that came together made a good start identifying the problem and the broad solutions to this problem.  They decided from the beginning that they did not wish to “re-invent the wheel” and to keep in mind that what they came up with needed to be flexible, scalable and consider all the hazards first responders may encounter from the smallest incident to a multi organizational large event.  I found this article very informative particularly with my involvement as support for some of these first responders and having taken FEMA courses.  I can see where there will be a broad benefit to the many organizations that need to come together to solve issues at hand and I see where there will need to be flexibility as different uses are and different priorities are met.

Hurricanes - Coastal Flooding from Hurricane Katrina

In this lab we were to assess the damage from storm surge caused by Hurricane Katrina to  three counties in Mississippi.  The three counties Hancock, Harrison and Jackson Counties are all on the Gulf Coast with wetlands and barrier islands that normally protect the coast from too much damage.  We are told that the wetlands and coastal woodlands are important not only for habitat but also for the economy due to the fishing and paper industries.

Looking at the maps, we can see that many of the populated areas are along the Gulf coast and rivers.  The storm surge inundated the coastal communities, wetlands, coastal forests and inland. 

This map shows the elevation and hydrography of the three counties and the surrounding area.

This map shows the flooded areas with a graph of percentage of type of lands flooded.

This map shows the infrastructure and health facilities at risk from storm surge.

This Map is an addition showing the table with acres of coverage per type of land.

Using these maps as reference, I would suggest that the first priority would be to repair damage to Highway 90 that runs through all three counties along the coast.  Along this highway are the majority of hospitals and populated areas.  This highway will be essential to bringing more aid in and allowing access to vital health facilities and communities. Restoring the wetland and forested areas to bring the economy back to a viable and sustainable working area will need to be a priority after these first essential areas are repaired.

Process Summary

Deliverable 1:

1)      Examined the directory structure

2)      Documented the map for “Coast1”

3)      Set the environments

4)      The instructions said to Expand General Settings.  There was no “General Settings” that I saw, so I chose “workspace” and it seemed to be the same thing.

5)      Used the Mask function to limit the analysis to the three counties.

6)      Set the features for the Elevation feature class.

7)      Added the layers islands, water and river layers, setting the symbology.

8)      Created a map showing elevation, hydrology and populated areas.

9)      Exported as Coast1_cmb.jpg 

Deliverable 2 and 3:

   Documented the map for “Coast2” and set the environments

1)      Used the Spatial Analyst feature and the Math toolset to calculate the storm surge in meters and to isolate the area flooded by the storm surge.

2)      Used the Reclass tool to “reclassify” the land values.

3)      Saved the output raster as “reclandcover”

4)      Isolated the flooded land cover using the Times tool and saved the raster as “floodedlc”.

5)      Relabled the reclassified flooded land cover by adding new field to attribute table.

6)      Saved as a new layer.

7)      Added the counties, places and island layers and labled and symbolized.

8)      Calculated the percent in the attribute table and created graph of flooded land by land-cover type.

9)      Added the graph to the map. Saved and exported the map of flooded land and graph to Coast2_cmb.jpg

Deliverable 4 and 5:

   Documented the map for “Coast3” and set the environments

1)      Added counties and usa_streets layers. 

2)      Changed the symbology

3)      Added railroads, hospital and churches layers and symbolized

4)      Added flooded_land layer and made 50% transparent instead of 25% because I thought it worked better to show the other information more clearly.

5)      Calculated the acreage by clearing map and adding counties and floodedlc layers. 

6)      In the floodedlc attribute table added two float fields “acres” and “sqmiles”

7)      I tried to add this table to the Coast3 map but I couldn’t get the sizes to work right.

8)   Saved and exported the map of infrastructure impacts to Coast3_cmb.jpg