SF House Site Suitability

Transcription

1 SF House Site Suitability Rick Waterman Geog /9/04 For my final project I chose to create a site suitability model for a house in San Francisco. Being a new resident to San Francisco, I was interested in learning about the geography of the city. After relocating from Marin County, I had a tough time figuring out which neighborhood would suit my specific needs. I thought that this would be a good exercise in GIS/Model Builder to explore my options by creating a model based on my desired criteria. Basically, I asked my self where I want to live, what I want to live close to, and what I would like to avoid. My personal criteria are based on the following; 1. I want to be close to designated open space areas yet not within them. 2. I desired excellent access to all bike routes in the city. 3. I also wanted to be a certain distance away from highways and certain roads within the city. 4. It was also important for me to live close to public school systems for my 3 children of various ages. 5. Being aware of the possibility for tsunami from regional tectonic activity, I opted for a location at certain elevations. 6. I also desired a house location within a certain degree of slope for the fact that I want to bike home and get a reasonable amount of exercise yet not kill my self on the hills. Method: I created four separate models; buffer model, slope model, mosaic dem model and a proximity favoring model. I later combined these models to create my final one called SF House Selection Model. I created a toolbox in my tool boxes in arc toolbox. There I created my models. I then set my model properties, (environmental and raster), to the proper specifications.

2 First Model: Mosaic DEM I used six USGS 10 meter DEM s (see references) to create a base map with proper coverage for the city of San Francisco. I used the DEM to raster tool in the conversion tool box. Next I used Data management>> Raster>> mosaic to combine my new rasters into a seamless base map, which will be important for future calculations of slope and elevation. From here I used spatial analyst>reclass>>reclassify to manually classify my elevation into 6 standard elevation classes to more easily work with in the future building process of my model. Second Model: Slope Model Using spatial analyst>>surface>> slope and imputing my mosaic raster I chose the Degree of slope option. Next I used spatial analyst>>reclass>>reclassify my slope output raster. I again used the reclassify to create 5 classes from 0 degrees of slope to the maximum 84 degrees. Third Model: Buffer Model Here I used the analysis>> Proximity>> buffer to create 3 buffer around various shape files (openspace, freeways, and streets). I used a parameter on each of the shapefiles to give flexibility for future running of my model. Next, I isolated my 1 road that I did not want o live close to. I did a query using the street name (19 th Ave) before running my buffer on my street shape. Then using the analysis>>overlay >>union tool I combined the three buffer shapes together creating my union buffer shape. Using conversion tools>>to raster>>feature to raster I created a new raster buffer union raster. Then using spatial analyst tools>>math>>logical>>boolean not tool I made the buffered areas true and the non buffer areas false creating my final Bufferzone raster.

3 Fourth Model: Proximity favoring model To make certain I was within certain distances to specific locations in this case, all open space, all bike routes and all public schools. I first needed to convert my 3 selected shape files into rasters. I did this using conversion tools>>to raster>>feature to raster From here I used Spatial analyst>>distance>>euclidean distance to specify a maximum distance allowable from these desired locations. After running these Euclidean distance tools then I combined them using two spatial anylist>>math>>plus tools creating Euclidean distance all. I then reclassified them using the reclassify tool into 5 classes. Combined Model: SF House Location Model Using the spatial analysis>>overlay>>weighted overlay tool I imputed all my final rasters. 1. For my reclassed buffer I set false data to restricted and changed my true data class to one. I set the influence at 10%. 2. For my reclass proximity favor model I kept the default 5 classes assigning a 40% influence. 3. Re classified slope raster was classed to reflect my desired preferences for living at certain degrees of slope then setting the influence at 30%. 4. I the reclassed the mosaic DEM raster to similar classes based on the elevation preferable to my living condition parameters. I set this input to a 25% influence. I named my final raster as run10 because I had 9 run previous at different weighted influences. I used this final version for my map. Evaluation:

4 I was very happy with the result of my map. It clearly showed the classes of desirability that fit my specific criteria. When I overlaid the bike, openspace, schools and neighborhood shapefiles over my final map the analysis proved to coincide very nicely to those features as was specified by my weighted overlay analysis. The map also well reflects the desired slope and elevation reclassed rasters. The most desirable locations were in perfect distances from all of my criteria and my overlay of neighborhood shapes gave me a good guide for my future house hunting in San Francisco. References:

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