GIS 4048 Final Project

Figure 1: Layout of all six weighted overlay rasters.

Figure 2: Weighted overlay using all variables with equal influence.

For this final project, I was required to create a Weighted Overlay analysis (similar to that seen in Module 8) in order to narrow down home search areas for hypothetical clients. My clients work from home and are looking to move from Las Vegas, NV to southwestern Utah. They prioritize access to outdoor recreation but are also interested in living near Cedar City, Utah with its annual Shakespearean Festival. The results of this analysis were the six overlays seen in figure 1.

The link to my presentation can be found below:

http://students.uwf.edu/tae12/TyEvans_GIS4048ProjectPres.pptx

I am reasonably satisfied with my results. Although I am familiar with many of the areas considered in this project, I was surprised by the amount of highly rated land around smaller communities such as Kanarraville and Brian Head. If I were to continue this analysis I would like to consider other variables such as topography, elevation, and weather. Although mentioned in the presentation, I did not include proximity to Zion and Bryce Canyon National Parks as inputs for the Weighted Overlay tool. I would also like to conduct a similar analysis for the communities of Springdale and Moab.

Module 9: Urban Planning - GIS for Local Government

Figure 1: Page 6 from a 16 page map book depicting all parcels within 0.25 miles of the Zuko parcel and the associated zoning.

Throughout this course, we have been presented with a variety of GIS tools suited for different tasks. Some modules have focused on analyzing data, some on editing data, and some on presenting data to an audience. The foundation of module 9 was on the presentational side of GIS, specifically how to compile a map book, using the Data Driven Pages tool, for delivery to a client. This map book consisted of 16 map layouts presented in a single PDF file. Figure 1 above depicts page 6 of this book. Each page is composed of the same data frame sizing, legend, symbology, and text with a main data frame depicting a section of the area in question at a scale of 1:2400. The locator map, created with multiple copies of the index layer, shows the current location with a hollow frame and all others masked with a grey fill. This allows a viewer to know exactly where in the area the current main map is located.

In addition to the map book production, we explored the use of GIS in local government tasks. We reviewed the details of the township and range system as well as locating information from property appraiser's websites. We explored the types of data that can be acquired and used from such sites, including the data utilized in this module.

Of all the tools we have explored in this course, it is Data Driven Pages that I most wish I had understood for my internship and volunteer experience. Many tasks would have been much simpler had I known how to use this tool. I am certain this knowledge will be useful in the future.

GIS 4048 Participation Assignment: Urban Planning - GIS for Local Government

As an extra assignment to help us achieve a better understanding of how GIS data may be used to aid local governments in assessing property values, we were required to explore property appraisers' websites for our areas. In my case, this meant the counties of southern Nevada. Below are the questions and answers for the assignment.

Q1: Does your property appraiser offer a web mapping site? If so, what is the web address? If not, what is the method in which you may obtain the data?

I have found that several Nevada counties offer various levels of web mapping. The Lincoln County, Nevada site, which was used to answer question 2, offers only scanned PDFs of parcel maps (http://lincolncountynv.org/assessor/parcelbooks.html). The Clark County, Nevada site, which covers Las Vegas, has many more options for mapping and searching but is more difficult to navigate. It can be found at http://www.clarkcountynv.gov/depts/assessor/pages/recordsearch.aspx.

Q2: What was the selling price of this property? What was the previous selling price of this property (if applicable)? Take a screen shot of the description provided to include with this answer.

Because I could not see a way on the Clark County site to limit a search to all sales in a particular month, I used the Lincoln County site. The selling price of this parcel was $8,715,000. No previous selling price was listed.

Figure 1: Search results for June 2015 in Lincoln County, NV.

Figure 2: Listing for the chosen parcel.

Q3: What is the assessed land value? Based on land record data, is the assessed land value higher or lower than the last sale price? Include a screen shot.

Using the record above, the sale price of $8,715,000 is significantly more than the assessed value of $1,131,900.

Q4: Share additional information about this piece of land that you find interesting. Many times, a link to the deed will be available providing more insight to the sale.

I would be interested in learning why there is such a difference between the assessed value and the sale amount. However, the amount of land is fairly large at 1,760,000 acres. The owner, Gaea Theos LLC is based in Las Vegas and, based on a web search, is under two months old. This was likely an investment purchase.

Figure 3: Map of parcel values in West Ridge Place, Escambia County, FL.

The second part of this assignment required us to compile the above parcel map of the West Ridge Place neighborhood in Escambia County, FL. The most important component of this map is the parcel value symbology displayed as a color ramp from red (most expensive) to green (least expensive). This symbology allows us to easily see potential problems with parcel valuation.

Q5: Which accounts do you think need review based on land value and what you’ve learned about assessment? 

A few parcels stand out as possibly in need of a review. Account number 090310165 on the north side of West Ridge Place is valued significantly higher than the parcels on either side ($33,250 versus $27,075) for no apparent reason. None are impacted by easements and all three are rectangular parcels of equal size on the same street. Interestingly, two parcels across the street from the above property are valued significantly less than neighboring parcels. These two parcels (090310320 and 090310325) are valued at $24,938 rather than the prevailing $27,075. Two additional parcels on the south side of West Ridge Place appear to be similarly undervalued compared to neighboring parcels; these are parcels 090310260 and 090310245. While they are impacted by easements, all the neighboring properties are similarly impacted but are not devalued in the same way. 

GIS 4048 Module 8: Location Decisions - Homing in on Alachua County, Florida

Figure 1: Layout depicting the four variables that are the basis for choosing a housing location.

Figure 2: The results of two Weighted Overlay tool operations using the four variables from Figure 1 and different weightings.

The assignment for this module saw us working through the process of selecting areas for buying a house based on four variables that our hypothetical clients provided. These variables were proximity to two workplaces, percentage of the population between 40 and 49 years old, and percentage of homeowners versus renters. Our first map layout (Figure 1) depicts the four variables separately while the second layout (Figure 2) depicts the results of using the Weighted Overlay tool to combine the four variables and categorize areas based on different variable priorities.

The first step in preparing the data for our variables was to use the Euclidean Distance tool to arrive at a classification of Alachua County, FL based on distance from the two work places. Because all variables were to be compared and combined in the Weighted Overlay tool at the end, we then used the Reclassify tool to break the proximity categories into simple, single-digit values (reversing the values made the higher values closer and the lower values further from the workplaces).

The variable of population percentage between 40 and 49 years of age required a new field be added to the census tract layer's attribute table in which this calculation could be run. The field calculator was used in the new field with a formula to divide the number of people aged between 40 and 49 years by the total population with the result multiplied by 100 to get the percentage. This field could then be used as the basis of a choropleth map. The next step was to convert this vector layer into a raster (in order to use it with the proximity rasters and weighted overlay tool) with the Feature to Raster tool and to reclassify it as described for the proximity layers above.

The method for processing the percentage of homeowners variable was the same as for the age variable. A new field was added to the census tracts attribute table and the field calculator was used to populate the field with the results of dividing the number of homeowners by the number of households and multiplying the result by 100. The Feature to Raster and Reclassify tools were then used to finalize the processing.

The final task was to use the Weighted Overlay tool with the four rasters representing the variables described above as inputs. Because the Weighted Overlay tool would be run more than once, a model was created to streamline the process. The first run of the Weighted Overlay tool gave each variable equal weight (25%); the results are seen in the top map of Figure 2. For the second run, we were to consider the fact that our hypothetical clients were not happy about the traffic of the region and wished to prioritize workplace proximity. Thus, for the second run the weights for the two proximity rasters were set to 40% while the other two variables were set to 10%. Additionally, the scale values for the lowest three proximity categories were set to restricted, effectively eliminating such areas from consideration. The results can be seen in the second map of Figure 2. The second weighted overlay opens up areas between the two workplaces as being most highly rated, giving the hypothetical clients more options to consider in their pursuit of a home location.

GIS 4048 Module 7: Homeland Security - Protect MEDS

Figure 1: Military template map layout showing the 3 mile security buffer zone around the Boston Marathon finish line as well as checkpoints set wherever a road enters the 500 foot finish line buffer.

Figure 2: A map layout with multiple elements focused on 16 suggested surveillance points surrounding the finish line and how clear their view is of the finish line and environment.

The assignment for this week, the final module focused on homeland security and crime analysis, was to take the data collected, processed, and organized last week and use it in an analysis of security planning at the Boston Marathon finish line. The focus of the first map is the three mile security buffer zone created around the finish line. By using the Select by Location function of ArcMap with this buffer, we can find the potential targets of attacks near the finish line (including hospitals, schools, and airports). For the purposes of this assignment, we focused on hospitals. However, given the number of hospitals within the three mile zone (49 total), we looked only at the ten hospitals closest to the finish line. We used the Generate Near Table tool and joined the resulting table to the hospitals layer to see the distances of each hospital from the finish line. We then created buffers of 500 feet around these ten hospitals symbolizing areas requiring extra surveillance and security. The final task in the first map was to create a 500 foot buffer around the finish line and place checkpoints at each road as it enters the buffer. This was accomplished using the Intersect tool with the local roads and finish line buffer layers as inputs. The result was a layer with points at each intersection point.

The next group of tasks for the second map was to use LiDAR data to aid in the placement of surveillance points near the finish line. After exploring the provided LiDAR data with the LAS Toolbar, we used the LAS Dataset to Raster tool to create an elevation raster. We then used the Hillshade tool to create a hillshade layer from the elevation raster; the altitude and azimuth data we needed were acquired from http://aa.usno.navy.mil/data/docs/AltAz.php. This layer allows us to see the shadows that will be present at a particular time of day, in this case the time of the bombing. We next created a new point layer indicating several surveillance points around the finish line. The Viewshed tool was used in conjunction with this point layer to see the estimated visibility of the area from the surveillance points. To increase visibility, we added an OFFSETA field to the surveillance points layer to adjust the height of each point. The Viewshed tool was run several times with different point height variables until visibility increased significantly. To further check visibility from each point, the 3D Analyst toolbar was used to create lines of sight. This tool creates lines indicating areas that can be seen and areas that cannot be seen from one point to another. The positions of several points were adjusted based on this information. Profile Graphs of these visibility lines were also created from the 3D Analyst toolbar to better see how much the views were obstructed. Finally, ArcScene was used to create a 3D model of the finish line environment and the proposed surveillance points as well as their lines of sight. The elevation raster and orthoimagery layers were added (with their Base Heights variables adjusted accordingly) along with the lines of sight. This was to add another element to the final map layout to aid in visualizing the finish line's surroundings.

Overall, this was one of the most enjoyable modules of the course thus far. We worked through many steps and used many tools, but it gave us a taste of how GIS data are processed and used in real-world analysis tasks.

GIS 4048 Module 6: Homeland Security - Prepare MEDS

This week was our second module within the broader category of GIS use in homeland security. Last week the focus was localized and specific to crime in Washington, D.C whereas this module expands our focus to consider the data needs in order to prepare for and respond to national security incidents. To do this effectively, cities, states and the nation need to have data on hand, organized, processed, and compatible with other datasets.

While we may expect a certain degree of preparedness in geospatial data on the parts of various governmental entities, the critical importance of these data for efficient and effective response to security incidents (whether human or naturally caused) requires coordination to ensure different datasets work together and are available for all users who may need them. This geospatial data coordination forms a small part of the larger aim of the Homeland Security Presidential Directive-8 to guide governmental entities at all levels to prepare for emergencies. This Directive outlines scenarios to which governmental entities must be prepared to respond (National Planning Scenarios); a list of approximately 1600 tasks for entities to incorporate in their preparedness planning (Universal Task List); and a list of 37 capabilities necessary for effective preparedness and response (Target Capabilities List).

Foundational for geospatial preparedness is the concept of Minimum Essential Datasets (MEDS). As the name indicates, MEDS defines the types of data needed to maintain the required level of preparedness for emergencies. This is applied primarily to areas deemed to be most at risk (e.g., Tier 1 urban areas). The data needed to meet the MEDS requirement are as follows: orthoimagery, elevation, hydrography, transportation, boundaries, structures, land cover, and geographic names. The table below goes into detail on the requirements for each dataset.

Table 1: From Fiscal Year 2009 Homeland Security Grant Program Supplemental Resource: Geospatial Guidance, available at http://www.fema.gov/pdf/government/grant/hsgp/fy09_hsgp_geospatial.pdf

This week's exercise guided us in importing, processing, and organizing the required data outlined in MEDS for Boston in preparation for our analysis next week of the 2013 Boston Marathon bombing. Some of the data needed no processing (such as the boundary of the Boston urban area) while others needed to be masked, clipped, or selected to cover only the needed extent. The transportation data, originally in a single file, were separated into three layers, each representing a road category and symbolized accordingly. The geographic name data were imported in table form and added as XY data in ArcMap. All layers were projected into the Massachusetts Stateplane coordinate system. After all data layers were organized in a geodatabase and symbolized accordingly, they were each saved as a layer file. This ensures the layers retain their symbology when they are added to additional map documents. 

The result is a well-organized geodatabase and layer files with data processed and ready for further analysis. Such analysis will be the task in the next module.

GIS 4048 Module 5: DC Crime Mapping

Figure 1: Map of police stations categorized by number of crimes nearby; depicts the site of a proposed new police station.

Figure 2: Kernel density maps of three offence categories.

Our task this week was to make use of a variety of tools to analyze crime that occurred in Washington, D.C., in January of 2011. We began by geocoding police station locations using a CSV file. Only one of the stations needed to be manually located on the map. We then imported crime data from another CSV file. In order to analyze the crime and police station data, we performed two spatial joins. The first join involved a multiple ring buffer of half a mile, a mile, and two miles. This gave us an idea of the number of crimes that occurred at different distances from police stations. The second join was to police stations themselves, letting us know the relative number of crimes that occurred closest to each station.

The second layout depicts three kernel density maps, one for each of three crime categories. Kernel density maps work by using a user-provided radius (1500 square kilometers in the above case) and summing the values of each crime instance. The kernel density analysis assigns the highest value to the location of the incident; the value then decreases out to the radius. This gives us an idea of where potential crime hotspots may lie. Assuming the analysis is sound, it also helps mitigate the contingency of the exact locations of each crime in our interpretation. In other words, the specific location of a crime may be arbitrary, but the larger hotspots seen in a kernel density analysis may tell us more about areas of high crime potential and that need attention.

GIS 4048 Module 4: Natural Hazards - Hurricanes

Figure 1: Map layout depicting hurricane Sandy's path with storm categorizations and data readings.

Figure 2: Side by side comparison of one street in Toms River Township showing the visible damage extent of the storm.

Continuing the previous modules' focus on GIS applications to natural disasters, this week we focused on some of the GIS tasks that may be utilized in response to hurricanes. Specifically, we looked at Hurricane Sandy and the damage sustained by Toms River Township, New Jersey in October of 2012.

Compared to the previous module, our task this week involved more work in organizing data into two geodatabases. Raster datasets (seen in Figure 2 as the before and after imagery) were imported into two newly created mosaic datasets within one geodatabase. New Jersey reference data layers, such as roads and townships, were imported into one feature dataset. Keeping data well-organized may take time, but doing so will more than likely save time for the original map creator and especially anyone else working on the project. In my internship I have seen examples of poorly organized data that have forced me to spend time looking for data in obscure places. Had some time been invested in keeping data organized and well-labeled, my task would often have been easier.

Figure 1 depicts the path of Hurricane Sandy along with data collected at various points. The points were imported from Excel spreadsheet data and symbolized based on storm category. This layer was used as input for the Points to Line tool to create the polyline depicting Sandy's path.

Figure 2 symbolizes parcel damage based on a visual assessment of the imagery. The structure damage layer was created manually and symbolized according to damage level. A major part of the lab was the creation of attribute domains for the geodatabase in order to restrict the possible values of the structure damage layer's attribute table to a set of coded values. Not only does this make the completion of the attribute table more efficient as each point is digitized, it also helps minimize error when multiple people are entering data.

GIS 4048 Module 3: Natural Hazards - Tsunamis

Figure 1: Map showing radiation and runup evacuation zones surrounding the Fukushima II plant and along the coastline.

Module 3 of Applications in GIS continues the theme of natural hazards with a focus on the 2011 tsunami that struck Japan and created a radiation hazard from the Fukushima II nuclear power plant. This necessitated incorporating two hazard zones in the analysis and final map: the multiple perimeters of radiation exposure surrounding the plant and the runup from the tsunami along the coastline. An additional component of the module involved the proper organization of multiple datasets within geodatabases. This organization becomes crucial as the number of datasets and complexity of analyses increase, especially if projects will be worked on my multiple people and returned to over time. Finally, we utilized Modelbuilder to create a workflow that produced the needed datasets and that could be shared and modified.

The simplest step of the analysis depicted in the map above was the creation of the five radiation hazard zones using the Multiple Ring Buffer tool. This created a powerful visual of the potential radiation hazard facing populations and infrastructure at various distances. The tsunami runup hazard zones required more steps to create. In simplified terms, it is the result of combining layers depicting the lands affected by the tsunami with a DEM and categorizing the result into three zones based on elevation (0-10 meters, 11-40 meters, and 41-78 meters).

While the analysis portion of the assignment was a challenge, the requirements for the final map were just as challenging. The need to efficiently display two different hazard zones on the same map created many opportunities for symbologies to clash. After some experimentation, I decided to go with a patterned symbology for the radiation zones while keeping a simple color-based symbology for the runup zones. Each were given moderate transparency. This created a layout that communicated the information while not becoming cluttered. Major Japanese cities were added to the locator data frame to give context for the location of the Fukushima plant and tsunami damage.

GIS 4048 Module 2: Natural Hazards - Lahars

Figure 1: Lahar hazard assessment surrounding Mt. Hood, Oregon.

Having been away from GIS coursework for a few months, the first assignment of Applications in GIS was a welcome return to learning new GIS tools and skills. Specifically, this module focuses on the manipulation of raster data with a focus on tools used for hydrology as well as tools to perform mathematical functions on raster data. The purpose of using these tools was to produce a map depicting potential hazardous areas that would arise from lahars triggered by an eruption of Oregon's Mt. Hood.

Lahars are mixtures of material, generally mud and rock combined with water and volcanic material, that flow down the slopes of a volcano into surrounding valleys. The above map uses digital elevation model (DEM) data to predict the areas most likely to be affected by lahars originating at Mt. Hood. We proceeded through multiple steps to arrive at the final data product. First, two DEMs were mosaicked into one, then the Fill tool was used to "smooth" the raster data to diminish the potential for errors in the data affecting the subsequent analysis. The output of the Fill tool was the input for the Flow Direction tool, the result of which was a colorful raster wherein cells were categorized based on the direction water would flow over the surface. This output, in turn, was used with the Flow Accumulation tool. The output raster shows streams and rivers based on the previous calculations. The Int, Con, and Stream to Feature tools were then used, resulting in a line vector shapefile. The final step of the assignment was to create a buffer of 0.5 miles around this shapefile to catch the schools, cities and overall population (as represented by census block groups) that would most likely be at risk from lahars from Mt. Hood.

Although I do not know how often I will utilize the hydrology tools in my career, I am glad to have been introduced to them should the need arise.