Here it is, our final project for Applications in GIS! I conducted a property search for a Mr. and Mrs. Brown, who were looking to purchase a large, undeveloped parcel that was zoned for agriculture to build there future dream home and horse stables complete with pastures and riding arena. The Browns wanted to be close to town, so they decided that they wanted a property less than 15 miles from one of the two larger cities in Walton County. They also wanted to ensure the safety of their animals by having the property be at least one mile from any major roads in case they had an animal escape. They also ideally wanted a property with a slight natural (2-5%) slope to aid in drainage of the property so there was less likely to be standing water and conditions that would not be optimal for keeping horses and livestock. To start, I began by finding parcels that met their required parameters. I used select by attribute to select all parcels that were over 10 acres and were listed as vacant in the use description field (this also ensured that the large, vacant military owned parcels were excluded from the search). I exported the selected parcels as a feature class and used select by location to select all of the parcels in the newly created feature class that were within the agriculture zone layer (that I created from the Walton County land use layer) and exported those selected parcels as a new feature class.
Next, I worked with the optional parameters. I used the buffer tool to create a one mile buffer around the major roads layer and used the Euclidean distance tool on the buffer layer. After that I used the Euclidean distance tool on the selected cities layer, which contained the two larger cities in Walton County. Next, I used the reclassify tool on both of the created Euclidean distance rasters so the best options were set to the highest value, and the worst options to the lowest. I created a mosaic dataset of the two elevation rasters that cover the county and clipped them to the county boundary. After that, I used the slope tool on the clipped elevation raster to convert it to slope percent. Finally, I edited the symbology manually so there were 9 classes ranked from closest to the 2-5% range to the furthest from that range and color-coded them accordingly (and the same as the first two rasters), then used the reclassify tool on that raster also to set the areas that best fit the parameter to the higher number.
For the final analysis step, I created a toolbox to hold the weighted analysis tools. After that I created a new model in the toolbox and added my three optional parameters and the weighted analysis tool. For the first weighted analysis I gave the parameters equal weight (33% for slope, 33% for distance from major roads, and 34% for distance from town), and saved the model as weight. I ran the tool then created a new tool for the second weighted analysis, then I gave slope the least weight (20%) because it was not as important as the other two variables in the selection, distance from major roads medium weight (%30) because it was a more important variable to the couple, and distance to town the highest (50%) because they were more adamant about not having to drive a long distance to get groceries or home goods.
The second weighted analysis with the highest weight allotted for proximity to the city provided the best results for the Browns. All three results are less than 15 miles from the center of DeFuniak Springs. I feel confident that the Browns will be able to purchase one of these three parcels to be the future site of their dream estate. If they would like more options to choose from, I can find many more parcels to compare by dropping the variable for the lot to be vacant. They say that money is not a limiting factor, so they could always remove existing structures to get their perfect parcel to start fresh from.