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Suitability and Community Risk Modeling Using Raster Analysis

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Suitability Model This model used five variables to determine the suitability of a frac sand mine in Trempealeau County.The five are: Geology Slope Rail Terminal Proximity Water Table Land Cover Each of the variables were converted to a raster if they were not already, and then a suitability ranking was developed to determine how each variable would function as a frac sand mine (Table 1). These rankings were determined off of a variety of factors. The geology layer was ranked based off of availability of sand. If there is no sand, the model determines that there is no functionality for a sand mine. The land cover layer is classified based on ease of access to the potential sand layers below. The railroad proximity layer is ranked based on how far mined sand should travel before setting off on a rail car. For the slope layer, smaller inclines are prioritized so that excavation can proceed with relative ease. Finally, the water table layer is ranked based on how deep
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Capstone Research Assessing the Factors Influencing Water Use in Austin, TX

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Link to research paper.
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Eau Claire Bus Transit System

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I was in charge of creating all of the features which compose the Eau Claire transit system. This was completed while working for the City of Eau Claire as a GIS Intern. I started by digitizing all of the bus lines and stops included in the geodatabase. At that point the map below, as well as a series of maps were created for the public.
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Calculating Impervious Surfaces From Spectral Imagery

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This activity is based on the Learn ArcGIS lesson:  Calculate Impervious Surfaces from Spectral Imagery . It utilizes AcrGIS Pro to familiarize users with Value Added Data Analysis. This lesson uses aerial imagery, like that collected with UAS, to classify surface types. It ultimately creates a layer that describes that impervious surfaces of a study area. Segment the imagery Users open up the existing Surface Impervious project first. The "Calculate Surface Imperviousness" tasks are used in this lesson.  The first step is to extract the bands to create a new layer (Figure 1). Figure 1: Bands 4 1 3 are extracted to create a layer such as the image above. The next step is to group similar pixels into segments of image using the "Segment Mean Shift" Task. The parameters of Figure 2 are filled into the task to create a new layer (Figure 3). Figure 2: Parameters used to create a segment mean shifted layer.  Figure 3: Result layer of
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Calculating Volumes of Mine Piles Using Pix4D and ArcMap

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Introduction Volumetric analysis is incredibly important to mining operations. Utilizing UAS technology to assess mine pile volumes is both cost effective and efficient. Having more frequent volume analysis on mine operation's stockpiles would allow companies to save money, while improving overall business. This lab's intention is to provide an example of how valuable UAS data is in assessing volumes of piles at Litchfield mine. There are three piles that will be assessed both in Pix4D and ESRI's ArcMap using a variety of volumetric analysis. Methods In Pix4D, the first step is to open a merged mine flight project. This project already has GCPs added, so the spatial analysis will be highly accurate. Three new volume objects are added to the imagery around different piles. Once this is done, the volumes are calculated by simply pressing calculate (Figure 1). Figure 1: The three objects are displayed with numbers corresponding to the volumes on the left.
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Sand Mine Trucking Network Analysis

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Goals and Objectives This assignment's goal is to utilize python to create feature classes which can then be used in later processing. Once this section is completed, Network Analysis are used within ESRI's ArcMap to perform cost analysis of trucks driving sand mine output across the roads of Wisconsin. The closest facility function is used within Network Analysis to describe the distances trucks must drive to reach rail terminals for further shipping. All of the input values are arbitrary and only meant for obtaining a working knowledge of Network Analysis. This sort of analysis is extremely important, and organizations such as the National Center for Freight and & Infrastructure Research & Education out of the UW-Madison have done extensive research on  this topic within western Wisconsin. Methods This first part of this assignment was to designed to create a python script which creates output which can be used later in Network Analysis. Figure 1 below descri
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LiDAR Remote Sensing

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Methods The first step is to enter all of the .las files into Erdas Imagine. While doing this, make sure to uncheck "Always Ask" and then select No on computing statistics. This is important, because all of the statistics will be calculated later on once the user knows the tile size and metadata. The next step is to open ArcCatalog and open the lab folder created to store the data. Create a new LAS Dataset and bring in the data for the lab. At this point, the statistics are calculated by opening the frame properties. While in the properties, the next step is to change the horizontal and vertical coordinate systems to NAD 1983 HARN Wisconsin CRS Eau Claire (Feet) and NAVD 1988 US feet respectively. A shapefile is brought in to verify that the projection has done the job correctly. In ArcMap, the dataset is brought in and the symbology is changed to have 8 breaks. With the LAS dataset toolbar open, the different ways of displaying the data is identified. Contours are also se
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