Identifying Suitable Places for Development

Objectives of the Project

The goal of this project to identify approximately 60 square miles of land that is appropriate for mixed-use development. This project will adhere to environmental, urban development, and socioeconomic models that are indicated in Urban Land Use Planning by Philip Berke (fifth edition). Through the course of this assignment, we will explore the logical possibilities for our development and assess the potentials each geographic location provide. The study area is to the north of City of Greenville and the west of City of Greer, and it shares borders with City of Travelers Rest at its west.

This analysis utilizes spatial modeling to facilitate the decision-making process. By visualizing the characteristics of the land, we intend to analyze the geographic features and identify those areas in which development will cause less environmental harm and more socioeconomic benefits for the public. The more detailed socioeconomic aspects of this study, although worth studying further, are not in the scope of this assignment. In other words, this study will not discuss and take into account whether communities are likely to be in favor of a new development in their proximity or not. Moreover, it is not going to include the political will (local powers to lobby, etc.) to initiate such a land use proposal. Economic feasibility and whether the localities are even capable of launching such projects is also excluded from this study. The following, is an introduction to the study area, methods of analysis, demonstration of the results, and conclusion. You may download the data for this exercise from this link.

Study Area

The study area is located in Greenville County—which is in the northwest part of the state of South Carolina. This area is surrounded by three cities of Greenville, Travelers Rest, and Greer. Spartanburg, Laurens, Abbeville, Anderson, and Pickens in South Carolina, and Polk, Henderson, and Transylvania in North Carolina are counties with regional proximity to the study area. The most dominant trend in this region is the fast-growing urbanized areas surrounded by rural places. This pattern is not unique to these counties as other areas throughout the American South could be identified with same trends. Therefore, the analysis could be a basis for future comparative studies for the regions that share these trends throughout the southeast. The availability of data and access to the information was a contributor in site selection.

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Geodesign Process

This analysis follows Philip Berke’s Urban Land Use Planning and utilizes the geodesign process suggested by Carl Steinitz in his book A Framework for Geodesign. ESRI’s ArcGIS for Desktop with enabled Spatial Analyst extension has been the main tool both for generating and interpreting the data available. To answer the question posed in the assignment, the first step for developing a suitability model was to determine what factors play a role in the final analysis. In other words, the variables which determine whether an area is suitable for development were identified and filtered. Main variables have been selected in two categories: Environmental and general urban development factors. The following is a detailed description of the process model.

Ideally such model shall include socioeconomic factors such as median income, population, and more detailed urban development factors such as projected growth, proximity to major roads, etc. However, due to time restrictions and limited access to relevant data, we have only included environmental factors and land cover classification in this model. The diagram below shows the elements that are relevant for building such model. In this exercise, we have only used the ones that are outlined with color red.

Methodology

The routine procedure for such suitability analysis is to calculate a value for each dimension (environmental, urban development, and socioeconomic). After obtaining a value for each, we weight them based on our priorities. For example, if we are more concerned with environmental issues, our environment dimension will get a higher value in overall aggregate value. Or, if we are primarily concerned with economic development, we will prioritize urban development factors over other dimensions of the model. Here, we have prioritized environmental factors.

The Environmental Factors

Step 1: Download the folder "Dr. Ellis_1st" and copy the folder directly to your C drive. Open an ArcMap document. Go to your Catalog panel and connect the folder to your map document. Right-click on your data frame and from Data Frame Properties go to Coordinate System tab > Projected Coordinate Systems > State Plane > NAD 1983 HARN (Intl Feet). Select NAD_1983_HARN_StatePlane_South_Carolina_FIPS_3900_Feet_Intl as your projected coordinate system. Now you have defined the projected system for your data frame. Save your map (Figure #1).

*** Please click on pictures to see enlarged versions. ***

In the folder, you should have two geodatabases and a toolbox. Open your toolbox and open the development_model. Remember, to open your model, you should click on "Edit..." and not "Open..." (Figure # 2).

Once you open your model, you should see the model below. In this exercise, we will go through the steps for creating this model (Figure # 3).

Figure #1

Figure #2

Figure # 3

Step 2: We have already ran the model, and you should have all the model outputs in your Raster_data geodatabase. However, we recommend you to create a new geodatabase (Model_Output) and set it as your Default Geodatabase so you can compare your model output with the given data. Now, right-click on your toolbox and create a new model. You can rename the model from your toolbox. Drag the following layers from your geodatabases to your model (Figure #4)

- From Vector_data.gdb, drag the feature class WaterBodies_Clip

- From Raster_data.gdb, drag the raster datasets: DEM_ft_Clip, Amphibians_Clip, Mammals_Clip, Canopy_Clip, and Landcover_2010_Clip

Go to Model > Model Properties and set the following values as displayed in Figure # 5.

Figure #4

Figure #5

Step 3: In the Search panel, type "Euclidean Distance." You can also select the tool from ArcToolbox, under Spatial Analyst. Double-click on the tool in your model and set the criteria for the tool as below:

Click OK, right-click on your tool and select Run. After it is completed, right-click on your output layer (the one in green) and select Add To Display. This will add the output to your map document (Figure # 6).

Figure # 6

Now that you have your Euclidean Distance layer, you have to reclassify your values to be able to have a measure. In this exercise, we have ranked the values from 1 to 5 with zero representing (NoData). Follow the steps as displayed in Figure 7. Notice that the higher the old values, the lesser the new values become. This is because we want to identify environmentally sensitive areas to avoid development. The closer to water bodies (or other environmentally rich area), the higher the sensitivity of that area.

After you run your model, right-click on the output and click Add To Display. Your results should look like Figure # 8.

Figure # 7

Step 4: In this step, you will create a slope layer from your Digital Elevation Model. DEM is a 3D representation of terrain's surface. To create the slope layer, drag and drop the DEM layer to your model. Drag the Slope tool to your model from ArcToolbox > Spatial Analyst Tools > Surface > Slope. Set the tool setting as Figure # 9.

After you run the tool and add the results to your display, your map should look like Figure #10.

Figure # 8

Figure # 9

Figure # 10

Step 5: Drag the raster datasets Amphibians_Clip, Mammals_Clip, Canopy_Clip, Birds_Clip, and Stewardship_areas_Clip to your model. Reclassify them as Figure 11.

Figure # 11

After you reclassified all your layers, choose Weighted Sum from Overlay tool-set. Connect your layers to the tool as below. Your final layer after you run the Weighted Sum layer should look like Figure 12.

Setting the variables of the model on stewardship areas, steep slope, and wildlife diversity pivots the analysis primarily around environmental issues. The current landcover was counted as not entirely environmental and is included in the Urban Development dimension. The variable of ecological diversity takes into account the importance of areas that possess a rich wildlife and are ecologically vibrant. This factors in birds, mammals, amphibians, and tree canopy. The element of slope impacts the development patterns significantly. Also, growth happening on steep slopes has a major environmental effect. Therefore, it is included in the factors which contribute to the decision-making process. Areas with steeper slopes are costlier to develop due to both construction restraints and erosion of the land. Therefore, it is common sense to leave these areas as untouched as possible. The proximity to lakes and rivers has been another contributor to the overall environmental assessment. Using the Euclidean Distance tool, the areas closer to lakes and rivers have been given higher value due to their scenic attractions. The Slope layer was created using the Digital Elevation Model (DEM) layer provided by USGS. Slopes that had a higher inclination degree were given greater environmental sensitivity. This was achieved by reclassifying the slope layer into five degrees of steepness.

Additionally existing tree canopy is used as a proxy for identifying areas that have a high presence of wildlife. The logic behind this is that within the study area much of the wildlife live in or require wooded areas. Since this variable might be overlapping with much of the wildlife (mammals, birds, etc.) attractions, all these variables were weighed with a small value. After running the environmental model, your outcome should look like the map below. Notice that the higher the value, the more environmentally sensitive the area (Figure 13).

The Urban Development Factors

Step 6: While your map (Figure 13) illustrates environmentally sensitive places, it does not give us any information about the current land use. For that, we develop the binary values assigned to each land cover category based on what was explained before. Figure 14 is the result of such binary value assignment.

The Urban Development Model takes into account different aspects of infrastructure and existing urban form. However, in this exercise we only look at existing land cover. The reason this layer was not included into environmental model was that the stewardship layer suffices for the environmental considerations. Accordingly, areas with development potentials were identified. For example, putting a development on an existing dense development or open water is impossible or hard to achieve. Existing high density developments were excluded due to economic factors. The land uses which were determined to be suitable for a new development were given a value of 1 (A binary value system (0 and 1) has been assigned to each cell in the current land use raster layer. Therefore, 0s represent the areas which need to be avoided for the development designation and 1s are the areas which are theoretically suitable for this purpose). See Figure 14 and Figure 15 for more details.

Figure # 12

Figure # 13

Figure # 14

Figure # 15

Step 7: After this step, we need to combine the raster dataset in a way that all the zeros in the binary assignment remain zero in the new raster dataset, and the cells that have the value of 1 should contain the weighted environmental models value. For that, we need to use Raster Calculator tool. We then need to reclassify our results.

Results

Your final results should look like Figure 16. You can add other layers (such as roads, rivers, etc.).

Your model is complete now. However, keep in mind that this process was a simplified version of a suitability study. In an ideal model, we should include many more factors that we have eliminated from this model due to time restrictions. Socioeconomic factors such as income, race, education, etc. have all been neglected in this model. However, younow know at tools to use to develop your own model.

Figure # 16

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