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.