Lab 8: Applications in GIS

This week's lab focus on damage assessment. The lab mainly focused on Hurricane Sandy. The map that was created at the end of the lab shows the path the hurricane took and what category of hurricane it was at that point in time. To create this map, I added the world and US shapefiles to ArcMap. I needed to select only the states affected by the hurricane, so I used a select by attribute to select the states. I added the hurricane points to the map and added XY data. To create a path of the hurricane, I used the point to line tool.

Next, I needed to adjust the hurricane symbology to look like a hurricane. To do this I had to edit the symbol's properties. I had to change the symbols to ESRI Meteorological. I found the symbol that looked like a hurricane, but then used the angle setting to tilt it. I also added a center dot on top of the symbol as well. I also changed the color of the symbol to red. I saved the new symbol and category under unique values.

Hurricane Sandy Path

The next step was to add graticules to the map. I selected the data frames properties and went to Grid. From there, I selected the graticule that uses meridian and parallels. Finally, just had to add the key map elements.

To perform a damage assessment on the New Jersey shoreline, I added a new feature class to place a point on each parcel. After placing a point on each parcel, I updated the points attributes. I did this for every parcel in the layer. To determine how many structures fell within 100, 200, or 300 meters of the coastline, I used the select by location tool. I also did create a buffer for each to determine how many fell within the distance from the coastline. To create the coastline I created a new feature class of a polyline that was parallel to the parcel area.

Below is the table of the result:

Structural Damage	Counts of structures within distance categories
	0-100 M	100-200M	200-300M
No Damage	0	0	1
Affected	0	0	7
Minor Damage	0	14	24
Major Damage	3	19	11
Destroyed	9	10	7
Total	12	43	50

Lab 7: Applications in GIS

This week's lab focused on coastal flooding.  With concerns of sea level rising during storms and due to global warming, coastal flooding analysis is important to decision makers.  The map below shows the effects of flooding on Honolulu, HI.

To perform the analysis, I had to create the flood zone by using the Less Than tool to find areas less than 1.41 and 2.33 meters.  To find the area of the flood zones, I converted the rasters to polygons and used the field geometry tool to calculate the geometry.

I used the Multiply tool to multiply the DEM with the flood zones.  Then to get the Minus tool to get the flood depth.  Next I added the tract shapefile.  I added a field tool to calculate the area in square kilometers.  I also calculated the population density by taking the population divided by the area.

People over 65 are less likely to be affected by the floods compared to the other types of population.  In the 3 foot scenario, the white population make up the greatest percent of being affected by flooding.  In the 6 foot scenario, home owners are most likely to be affected by flooding.  In either scenario, people 65 and older are less likely to be affected.  Home owners do have a high social vulnerability of the three groups here.  In the 3 foot scenario, it is the second highest percentage.

Flood Depth Analysis

Lab 6: Applications in GIS

This week's lab focused on crime analysis.  The three types of to determine crime hotspots in Albuquerque was Grid-based thematic mapping, Kernel Density, and Local Moran's I. After performing all three analysis, the next step was to compare which hotspot analysis is best for predicting future crime.

To create the map below, I first performed the grid-based thematic mapping.  To find the hotspots by using grid-based thematic mapping, I did a spatial join of the grids with the 2007 burglaries.  I selected the grids that contains at least 1 burglary and made it into a new shapefile.  I then found the top 20%.  I then dissolved the polygons to make one single polygon.  I then added a field to calculate the square kilometers.

Next I used Kernel Density to determine the hotspots.  I set the environment to only show the grids.  I then used Kernel Density tool to calculate.  The parameters for the tool used output cell size of 100 and the search radius of 1320 feet.  I kept the area units to square miles.  Next I removed the areas with 0 density.  I found the mean and used that to determine the classifications.  Once that was complete, I converted the raster to polygons.

Finally, I used Local Moran’s I to determine hotspots.  I did a spatial join of block groups and 2007 burglaries.  I then found the crime rate of burglaries to housing units.  Next I used the Cluster and Outlier Analysis script and left the parameters to the defaults.  Next I used a query to create a shapefile of just the HH polygons.  I dissolved the polygons and then found the area by using calculate geometry.

Below is a map layout of all three analysis. This helps the Albuquerque police determine where to patrol more by comparing the 2007 hotspots to the 2008 burglaries.

Hotspot Analysis

Lab 5: Applications in GIS

This week's lab covered spatial accessibility modeling.  To perform the analysis, the network analysis extension needed to be enabled.  A few of the tools within network analysis that were focused on this week was Closest Facility, New Service Area, and Spatial Joins.

To create the map below, I used the New Service Area option from the network analysis toolbar.  I found the service area for each of the 7 college campuses.  The tolerance was 5000 meters and it used breaks of 5, 10, and 15 minutes.  After clicking the Solve button, I was able to see the results of the service areas.

Next, I created a new shapefile that excluded Cypress Creek campus.  I reran the New Service Area for the 6 college campuses.  I used the same setting as I did for find the service areas for the 7 campuses.  I converted the block group shapefile to centroids by using the Feature to Point tool.  By doing this, I was able to see how the service areas overlay with the block groups.

The results show how the service area decreases if the Cypress Creek campus closes.

Campus Service Areas

Lab 4: Applications in GIS

This weeks lab covered visibility analysis.  Visibility analysis can be applied to a lot of different situations such as observation points or fire towers.  This weeks lab focused on viewshed analysis, and observation point tools.  Part of the lab covered visibility analysis using 3D Analyst and the LAS Dataset tool.

The first part of the lab used the Viewshed tool with the summit points and the elevation raster.  I also used the Observer Point Tool with the same inputs.  Once I had the outputs, I used Extract Values to Points to determine which summit is viewed by the most observation points.

Polyline Visibility Analysis

The next part of the lab used polylines to determine which areas of Yellowstone National Park are visible from the roads.  The inputs used the roads polyline shapefile and the elevation raster.

Part three of the analysis uses the 3D analyst extension.  This allowed for me to see a 3D model of city of Boston.  Once the streetview was selected, I was able to rotate the model to see all angles.  Next, I used the LAS Dataset to Raster tool.  This creates a new finish line raster.  I added the camera shapefile and used the viewshed tool to see how much area is visible by the one camera.  I then adjusted the offset so the camera was considered elevated and could see around the buildings.

It was important to then determine the start and end angle.  This allowed for a more realistic visible area.  I added two more cameras and performed the same analysis.  After I had the viewshed, I adjusted the symbology to show what area is seen by 1 to 3 cameras.

The last part of the lab covered line of sight analysis.  For this analysis, I needed the Create Line of Sight tool.  I created a line that connect two summits.  To see more details, I opened the Profile Graph.  The blue dot shows an obstruction.  I also used the Construct Sight Lines tool to create lines between all the towers.  This allowed to see which summits are visible from each summit.

Lab 3: Applications in GIS

This week's lab covers watershed analysis for Kuauai, Hawaii.  The first step of the analysis was to use the fill tool to fill any errors in the DEM file.  This is down so the flow of the watershed is more accurate and removes the sinks.

The next step was to use the Flow Direction tool.  This tool determined all the different directions the water would flow.  The tools determines the 8 possible directions by analyzing one cell to the next.  The cell direction goes from one cell to the lowest.

The third step uses the Flow Accumulation tool.  This tool produces a layer that accumulated the cells and collects a cell count.  This shows the number of cells that would flow using the flow direction raster.  The flow accumulation for the cell represents the upstream cells that flow in that direction.  I then added a threshold of 200 cells.  This produced an output that contained steams with 200 cells or greater.

The next step was to use the Stream to Feature tool.  This coverts the streams to vector files and also maintains the direction of the streams.  By using the Stream Link tool, it allowed me to clearly identify individual streams.  Next it was important to determine the hierarchy and scale of the streams by using the Stream Order tool.  This also looks at the flow direction of the streams.

Once the previous steps are completed, I used the watershed tool.  The output showed the where the streams drain out to.  I then added a pour point to the map at a location where the stream drains to the ocean.  The watershed tool shows where the watershed drains to for that pour point.

The results of this analysis compares streams to streams of the National Hydrography Dataset.  I then also compared the modeled watershed to the NHD watershed.

Watershed Analysis

Lab 2: Applications in GIS

Lab 2 covered least cost path and corridor analysis.  To perform the least cost path analysis, I reclassified land cover, elevation, and the euclidean distance found around the roads.  Once the layers were reclassified, I needed to use the Cost Distance tool.  This tool finds the lowest cost path from each of the national parks.  To find the least cost path, I would need to use the Cost Path tool.

However, to determine the national park corridor, I did not need to use the Cost Path tool.  Once the Cost Distance outputs were created for both national parks, I used the Corridor tool.  The tool requires the two cost distance outputs.  Once the Corridor layer was added, I needed to adjust the symbology.  I had to determine a threshold for the corridor.  I needed to make sure the corridors were not too wide.  I adjusted the colors to include 3 levels, least to most suitable.  Least suitable is the lightest and the most suitable is the darkest.

Most Suitable Corridor

Lab 1: Applications in GIS

Lab 1 introduced suitability modeling with raster and vector analysis tools.  After using the tools, I compared the different approaches used.  Another key part of the lab was to adjust parameters to see how it can affect the output.

To create this map, I reclassified the land cover shapefile.  I also reclassified the soil shapefile.  Next, I created a slope raster with the elevation layer with a suitability rating of 1-5.  I used Euclidean distance to create a 1000 distance around the streams and also used the Euclidean distance tool to create a distance to roads raster.  This was broken into 5 classes using the 1-5 suitability rating.  After the suitability rating was applied to all the layers, I used the weighted overlay tool twice.  The first time, I used equal intervals.  The second time, I used different variables for soil, slope, streams, and roads.

It is clear to see how added more weight to a criteria can influence suitable area.  To see how much area was the most suitable, I change the layer to polygons and calculated the geometry.

Suitability Modeling