Module 1, Part 1: Suitability Analysis

The purpose of this lab was to create a suitability model for a developer. The analysis included provided suitability ratings for land cover, soils, slopes, proximity to streams and proximity to roads. The flow chart of the analysis work flow is below:



I began by using the Reclassify tool to reclassify the landcover raster. I used the Euclidean Distance tool on the rivers and roads shapefiles, individually, and then reclassified each raster output using the Reclassify tool. I used the Slope tool on the elevation raster to convert the raster elevations to slope and then reclassified the output raster using the Reclassify tool. I converted the soils shapefile to a raster with the Polygon to Raster tool and reclassified the output raster using the Reclassify tool.

To create the final suitability model I added all the reclassified rasters to the Weighted Overlay tool and ran two analyses. The first analysis gave each variable the same weight, 20%. The second analysis used unequal weights for the variables as follows: slope 40%, land cover and soils 20% each and distance to streams and roads 10% each. The final suitability model for each result shows suitability ratings from 2 to 5, with the lower values being less suitable for development. Below is a comparison of the results of both analyses:


The results show a distinct difference in suitable areas for development, depending on the weight of the variable. In the unequal weights analysis, areas near the river are clearly more suitable for development (dark green), whereas in the equal weights analysis there are only a few areas near the river meeting all criteria and more suitable for development. This makes sense given that the areas near the river most likely have lower slopes, which is weighed heavier in the unequal weights analysis.

Given the results and experience in this lab, suitability analysis is a valuable tool for solving a variety of problems with many variable inputs.

Comments

Post a Comment

Popular posts from this blog

Module 6: Proportional Symbol and Bivariate Choropleth Mapping

Image
The objectives of this week's lab was to explore proportional symbol and bivariate choropleth mapping techniques, as well as customizing legends for each technique. One of the data sets for this lab included positive and negative values. The final map utilizes proportional symbols to show both positive and negative values. This map is essentially two proportional symbol maps overlaid on each other, one for positive job growth and another for negative job growth, with the symbols for both variables sized the same. Utilizing proportional symbols to show both positive and negative values is an effective way to visualize areas that show the greatest and lowest values. These areas are easy to see quickly on the map and give the map reader a simple way to visualize the variable being mapped. Fig 1. Proportional symbol map using positive and negative values.  Another data set for this lab examined contained two data variables: percent of the US population in each state that is obe
Read more

Module 2: Coordinate Systems

Image
In this week's lab I explore different coordinate systems and projections in order to better understand the nature of distortions introduced by the systems. For the last part of the lab, I was tasked with selecting a state in the contiguous US and selecting an appropriate projection for the area. I selected the state of Wyoming as my area of interest. Wyoming has 4 State Plane zones and two UTM zones, so neither a single State Plane nor UTM system would work for this area without distorting the size or shape of the state. Therefore, the best coordinate system for this area is Custom Coordinate system. I chose NAD 1983 WyLam (Meters), a custom system specifically for the state of Wyoming, as the projected coordinate system. This coordinate system is a Lambert secant-case normal-aspect conic type projection which preserves shape and is appropriate for a general reference map. The standard parallels are placed at 41N and 45N, the boundaries of the state of Wyoming, which minimizes
Read more

Module 4: Color Concepts & Choropleth Mapping

Image
In this week's lab, I explored using color and selecting color ramps in ColorBrewer, as well as applying these concepts to create meaningful choropleth maps. I created a map of population change from 2010 to 2014 in Georgia counties. I normalized the data and produced a choropleth map of population change using a diverging color scheme with 6 classes divided around a midpoint of 0. Fig 1. Choropleth map of population change in Georgia counties using a diverging color scheme. For the Georgia population data set, I chose to divide the data set into 6 classes around a midpoint of 0: 3 showing a decrease in population and 3 showing an increase in the population. The classes were grouped based on a manual classification closely based on the Natural Break classification, where the midpoint was adjusted to 0 with the other groups unchanged. Natural Breaks classification creates classes based on similarities in values in the data, thereby revealing patterns in the data. By adjust
Read more