ArcGIS analysis – ACQUIRING AND PREPARING SPATIAL DATA FOR ANALYSIS
SSCI 581: Concepts in Spatial Thinking Project 3 PROJECT 3 – ACQUIRING AND PREPARING SPATIAL DATA FOR ANALYSIS Due Date: 11:59pm on March 19, 2024 (PT) Deliver your responses (described below) into the appropriate Blackboard Dropbox Value: 10% of the course grade Penalty for late delivery: This assignment will be penalized two points up to four days late. No points will be given for submissions more than four days late. ASSIGNMENT DESCRIPTION Perhaps the defining feature of modern spatial science is the quantity and variety of spatial data that exists. Thus far in the semester, we have learned a fair bit about the different technological platforms that create spatial data, such as remote sensors that produce aerial and satellite-based photographs, LiDAR imagery, thermal imagery, and multi- and hyper-spectral data, as well as global navigation satellite systems (GNSS) and the location-based software applications that rely on them to create all types of geotagged information. We have also learned about the variety of domains that produce relevant data, be it environmental, social, or administrative, and the breadth of online geoportals that offer up such data to users. By considering different types of data from the same location, we can see different viewpoints on place. For example, consider the following types of data from Los Angeles: counts of bird sightings, footprints of building outlines, and imagery classified by the imperviousness (whether water can flow through) of the surface (e.g. asphalt, concrete, rooftops are impervious). Each dataset tells its own important story about the city. When brought together, however, we can find connections and begin to understand complex spatial processes. For example, using the above three datasets, we might connect larger building outlines and higher percentages of impervious surfaces with an expected finding, such as higher counts of birds (but fewer species of birds). In sum, spatial data are meaningful on their own, but when they are brought together the potential to gain insights about our world multiplies. Of course, different types of spatial data from different sources will need to be prepared before they can be brought into conversation with each other, so to speak. Large datasets need to be clipped to our area of interest, and all datasets need to be brought into the same coordinate system and projection (which is commonly done by bringing them into the same projected coordinate system). This project has two parts. In the first, you will explore a variety of spatial data centered on the University of Southern California (USC) University Park campus (UPC) that have been provided for you. You will visit the websites that sourced much of the data, and you will bring the data into ArcGIS Pro. You will also review how these data were acquired and prepared. You’ll consider what you might learn from the data individually and together, and you’ll consider what other datasets could help you learn more about the UPC. The second part of the project is self-directed. You will choose a location of interest and acquire and prepare at least four different datasets from different sources on this location, as if you planned to study them together. You will not actually conduct a study on this data, and thus the choice of data sets relative to each other is less important than the fact that they represent a variety USC Spatial Sciences Institute © 2024 1 SSCI 581: Concepts in Spatial Thinking Project 3 of data types from a variety of domains and sources. Do NOT merely replicate the datasets that are already provided for your location of interest. LEARNING OBJECTIVES • Find and acquire a variety of spatial data for the same location. • Choose an appropriate projected coordinate system for a chosen location. • Import a variety of data into ArcGIS Pro. • Prepare a variety of data for the same location by clipping data to a study area’s extent and projecting data into a chosen projected coordinate system. • Write a succinct description of data acquisition and preparation processes. INSTRUCTIONS NOTE: if you are using Pro via VM, recommend you clear up your G drive (e.g., delete the old project and data) given data used in this lab may run beyond its storage limit. If you use Pro locally in your laptop, then no concerns here. 1. Copy the Project3.zip zipped folder from the H: drive 581 folder to your G: drive. The description of the contents and the process of acquiring or creating each dataset is described below. The contents of the Project3 folder are: • • • • • • • • Project3.aprx Project3.gdb Project3.tbx map.osm OSM.gdb NLCD_USC folder Flickr_USC folder USC_Building_Addresses.xlsx 2. Extract the contents of Project3.zip. 3. Inspect the ArcGIS project (Project3.aprx). As you learned in Project 1, a .aprx file is an ArcGIS project file. When you create a new project in ArcGIS, the program automatically creates a .gdb and a .tbx for the project. The project’s .gdb file, its geodatabase, is the default location in which the results of any geoprocesses will be stored. The .tbx, the project’s toolbox, stores any tools that are created as a part of the project. When you create the project, both the .gdb and the .tbx are empty. When you create a new Project, ArcGIS Pro asks if you would like to create a new folder for the project. In this case, I chose yes, because I knew I would be corralling a lot of data and wanted to have a clear place for it to be stored. Open the Project3 project by double-clicking on the Project3.aprx file or opening ArcGIS Pro and navigating to the project from the initial window. You will be prompted to sign in to your ArcGIS Enterprise account when you open the project. USC Spatial Sciences Institute © 2024 2 SSCI 581: Concepts in Spatial Thinking Project 3 The project contains nine layers with five types of data; you can see these in the Contents pane (Figure 1): • • • • • A bounding box polygon, which was used to clip larger datasets to our location of interest; Geotagged photo locations, with photos attached; National Land Cover Database (NLCD) layers, including: • two tree canopy layers, and • two impervious surface layers; Building footprint data: • one layer from OpenStreetMap, and • one layer from the City of LA; Point locations of USC buildings, geocoded from a list USC building addresses Figure 1. Project3.aprx, opened in ArcGIS Pro Inspect the layers in the Contents pane in the map window by clicking to turn them on and off. Open the attribute tables for the feature layers (right-click on the layer name and choose Attribute Table) to inspect the features. Check the coordinate system of the layers in the Properties window (right-click on the layer name and choose Properties, and examine the Spatial Reference information under the Source tab). These layers are the final layers for our theoretical project. You can see in the map they all share the same extent, bounded by the bounding box, and you can see in their Properties that they all share the same projected coordinate system. To arrive at these layers, a fair bit of work was required to acquire the data and prep the data, including intermediate geoprocessing steps. The next sections will walk you through these processes. For now, you can get a sense of how much work was required if you look in the USC Spatial Sciences Institute © 2024 3 SSCI 581: Concepts in Spatial Thinking Project 3 Catalog pane on the right. The Project3.gdb holds all the layers you see in the map as well as the intermediate layers created via preparatory geoprocessesing. 4. Inspect the OpenStreetMap layer and its source. The Building_Polygon_USC_OSM_SPCS layer contains data downloaded from the volunteered geographic information site OpenStreetMap (OSM). Visit www.openstreetmap.org and explore the site. To download the data and prepare the data, I employed the following workflow. i. If you are not already, log into the USC VM. (This is so you can download the data directly to the VM.) ii. Use Chrome to open www.openstreetmap.org. iii. Zoom to the appropriate location. iv. Click the Export tab (top middle of page). v. Click “Manually select a different area” to open an interactive box to select the download extent (Figure 2). Figure 2. OSM export selection window vi. Make note of lat/long extents for later reference, as needed. vii. Click Export. (NOTE: if you receive an error of too many nodes you need to select a smaller area – DO NOT download the entire last dataset via PlanetOSM!) viii. Open the File Explorer and find the file map.osm in the Downloads. Move the file to the Project3 folder in the G: drive. NOTE: OSM data is made available in the .osm file format, which is a form of an XML file. This is a good example of the fact that not all spatial data exists in the most well-known formats, such as the shapefile. When you encounter a new file format, you will need to research if and how it can be imported in ArcGIS, or whatever platform you may be using. In this case, I googled “.osm” “ArcGIS” and learned how to import the file, highlighted in the following portion of the workflow. USC Spatial Sciences Institute © 2024 4 SSCI 581: Concepts in Spatial Thinking Project 3 ix. In ArcGIS Pro with Project3.aprx open, open a New Map (add all subsequent datasets to this map in Project3), open the Quick Import tool. x. Click the folder icon next to Input Dataset, and browse to the map.osm file. The program should read the file type. Complete and run the tool, giving an appropriate name to the geodatabase that will be created. Confirm the geodatabase is saved to the Project3 folder. xi. Find the new geodatabase in the Catalog pane and open it. Note the many types of features that are downloaded. Find the building polygons and add them to the map by dragging and dropping or by right-clicking and selecting Add to Current Map. xii. Use the Project tool to project this data into the appropriate PCS, giving the resulting layer an appropriate name. TIP: Review the Esri Help Pages for each tool you want to use. You can easily find it by googling “ArcGIS Pro” “help” and the tool name as key words. If you are not sure of a tool’s name, just use relevant keywords to narrow your search. You can also use the Search icon within ArcGIS Pro with the relevant keywords to find a tool or process. The Sources and Resources section at the end of this document includes URLs to some relevant Esri Help pages for this project. xiii. Remove the original file from the map, by right-clicking and selecting Remove. This does not delete the file. You can confirm it still exists, by checking the .gdb holding the OSM data in the Catalog Pane. xiv. This extent of the OSM layer was about the size I wanted for my study area, so I used it to help me draw my bounding box for clipping other layers, to be discussed in the next section. However, if your OSM layer is larger than necessary, you could use the Clip tool as necessary to refine the extent of this feature (see the section on the LA City data for an example). 5. Create a bounding box around the study area. To clip larger datasets to the extent of your study area, you need a polygon that bounds your study area. Often an existing layer, such as a county or city boundary polygon, can serve this purpose. In this case, there was no existing polygon of USC UPC I could use, so I created one: the Bounding_Box_USC_SPCS layer. There is more than one way to create a bounding box in ArcGIS. The Minimum Bounding Geometry tool will draw a polygon that encloses another feature or set of features according to the user’s parameters. For this project, however, I used the Create Feature Class tool to create an empty polygon feature and then used the Edit toolbar to draw a new polygon for this feature class, using the following workflow. i. Open the Create Feature Class tool. ii. Specify the feature class as a polygon feature class and give it an appropriate name. NOTE: The Create Feature Class tools allows you to specify fields for your new feature class, whether it will include z-values, and other parameters, which will come into play in later projects throughout the semester. These will become the fields in the Attribute Table and can be based on a current Feature Class. All I needed for this was a basic polygon feature class, so I did not use another Feature Class as a template; I simply created a new one and left the other parameters blank. iii. Select the appropriate projected coordinate system. iv. Run the tool. 6. Inspect the LA City building outline data and Portal tab. The City of LA has a great geoportal called the LA GeoHub. The LA GeoHub is integrated into the ArcGIS Portal so we can access many of the layers that are shared in the GeoHub from within ArcGIS Online and ArcGIS Pro. USC Spatial Sciences Institute © 2024 5 SSCI 581: Concepts in Spatial Thinking Project 3 I added the GeoHub’s building footprint layer to my map from the ArcGIS Portal and prepared it to create the Building_Footprint_USC_LACity layer using the following workflow. i. In the Catalog Pane, click Portal, and then click the All Portal icon. ii. Type appropriate search terms into the search bar. iii. Locate the layer shared by the LA GeoHub (owner = lahub_admin), and add it to the map (Figure 3). This is a massive layer with many thousands of features. Luckily we can run spatial processes on the layer before the program is able to display all the features in the map (which means once the layer appears in the Contents pan but not all the features are yet displayed on the map). Figure 3. Search results for Portal layers in ArcGIS Pro iv. Inspect the Properties of the layer to see that the coordinate system is already the correct PCS. v. Next we will run the Clip tool to clip LA GeoHub layer. You may need to create a new polygon layer and hand draw a rectangular polygon to align with the border of this study area and run the Clip tool based on this rectangular border. vi. Open the Clip tool. Select the LA GeoHub layer as the Input layer and the rectangular polygon you draw previously as the Clip layer. Give your resulting layer an appropriate name. vii. Select the appropriate projected coordinate system. viii. Run the tool. ix. Remove the initial LA GeoHub layer from the map. 7. Inspect the National Land Cover Dataset (NLCD) layers and their source. The NLCD is an important collection of land surface information for the US, and it has wide utility across many USC Spatial Sciences Institute © 2024 6 SSCI 581: Concepts in Spatial Thinking Project 3 domains. It is created by the Multi-Resolution Land Characteristics Consortium (MRLC). Read about the MRLC here: (https://www.mrlc.gov/about). If you explore the site, you can see that the data include more than the well-known land cover/land use dataset. For this project, I chose to acquire tree canopy and impervious surface information, ultimately creating the following four layers: Tree_Canopy_USC_SPCS_2011, Tree_Canopy_USC_SPCS_2016, Impervious_USC_SPCS_2006, and Impervious_USC_SPCS_2016. MLRC offers a map viewer from which you can inspect and request downloads of data (click the Data tab > Downloads, then click the small link to the Tools page at the end of the first sentence and then the NLCD viewer link in the first sentence on the Tools page). You can also acquire the data for the entire US, but the map viewer lets you focus in on your area of interest for faster downloads (Figure 4). Play around with the map and open up the tools window to the right of the screen to get a sense of how it works. Figure 4. MLRC Data Download Map Viewer TIP: Every data portal is set up in its own way. Part of the job of a GIS practitioner is learning to navigate data portals. This will take flexibility and patience, as many times these sites are not terribly intuitive. For this project, I typed in a lat/lon range close to the USC UPC campus to focus the map on the correct location. A blue window appears on the area of focus that highlights the areal extent of data to be downloaded (Figure 5). You can also click on the download icon in the map viewer and this will allow you to draw a box for the area of the data download. You then need to choose the categories of the data to be downloaded. USC Spatial Sciences Institute © 2024 7 SSCI 581: Concepts in Spatial Thinking Project 3 This map viewer is not the only method for acquiring MRLC data. You can choose data layers by year from the MRLC website, but you get the whole continental US (or AK or other noncontiguous regions) rather than a user-specified extent. Figure 5. MRLC Data Download Mapper, focused on LA area. NOTE: if you draw the rectangular bounding polygon like the blue one, you downloaded data will be too large to be saved in G drive- if you run Pro locally then you don’t have that issue; alternatively, you can draw a smaller bounding polygon, like the orange one, just covering the center of LA so the data size will be manageable in your G drive. After you submit your request, the MRLC will email you a link to a zipped file for download. Because I wanted more than one year of data, I chose the All Years option, rather than just 2016 or 2021. With more than one year, you can study the change over time. The MRLC offers its own total change rasters for each topic also. I went through the following workflow to prepare the data once I submitted the request for download. i. Access the MRLC email from the USC VM, so you can easily download the data on the VM and save it to your G: drive. ii. Move the data to the Project3 folder in which the Project3.aprx is saved. iii. Extract the data from the zipped folder. iv. In ArcGIS Pro, in the Catalog page, expand the Project3 folder if it is not already open. TIP: If something has been moved recently into a folder in ArcGIS but it is not showing in the folder, you can try right-clicking on the folder name and selecting Refresh. If it still does not appear, double-check that you extracted it and that it is in saved in the correct folder. v. Add each NLCD layer to the map by dragging and dropping onto the map or by rightclicking and selecting Add to Current Map. USC Spatial Sciences Institute © 2024 8 SSCI 581: Concepts in Spatial Thinking Project 3 vi. Project each layer into the appropriate projected coordinate system using the Project Raster tool. Choose appropriate layer names for each result. vii. Remove initial layers from the map by right-clicking on each layer name and choosing Remove. This does not delete the layers – you can confirm they are still in your Project3 folder in the Catalog pane. viii. Clip each layer to the correct extent using the Extract by Mask tool. (The Clip tool and the Extract by Mask tool have somewhat overlapping utility. I tend to use Clip for vector data and Extract by Mask for raster data.) Again, choose appropriate layer names for each result. ix. Remove the pre-clipped layers from the map by right-clicking on each layer name and choosing Remove. This does not delete the layers – you can confirm they are still in your Project3.gdb in the Catalog pane. x. Change the symbology of the layer as necessary. The resulting layers were on a black-towhite color ramp. For the tree canopy layers, I chose a yellow-to-green color ramp, and for the impervious surfaces, I chose a white-to-black color ramp. Access the Symbology pane by right-clicking the layer name and clicking Symbology. 8. Inspect the geocoded point layer and explore the geocoding tool. Geocoding is the process of assigning a pair of lat/long coordinates to an address location. While you do not need to geocode a different set of addresses for this project, you may choose to. For this project, I wanted to study buildings at USC UPC. Previously, USC did not publicly share a ready-to-go spatial layer of building footprints. I acquired building footprints from two other sources. I thought I could then compare those building footprints with an USCcreated list of buildings at UPC to assess their completeness of the footprint information. At time of prepping, USC also did not publicly share a spreadsheet of UPC buildings. The best I could find was a list of buildings within the Schedule of Classes web pages (Figure 6). Figure 6. USC web page with building list USC Spatial Sciences Institute © 2024 9 SSCI 581: Concepts in Spatial Thinking Project 3 ArcGIS Pro (and other GIS platforms) cannot import text from a web page so I needed to get the information into a format that ArcGIS can work with. I copied the building list from the web page and pasted it into an Excel spreadsheet. I then worked with the spreadsheet to ensure that it could be properly geocoded. First, I separated the address information from the nonaddress information (abbreviations and names of buildings) into different columns. Luckily, when I pasted the information, the abbreviations were added in as a separate column, and the building names were separated from the addresses by commas. Excel can separate text into separate columns using commas or other delimiters. Second, I added a title row (Figure 7). Figure 7. Excel spreadsheet with USC buildings I went through the following workflow to import and geocode the building information and to prepare the resulting point layer, USC_Geocoded_Buildings_SPCS_Clip, for study. i. Save the Excel spreadsheet to the Project3 folder in the G: drive. ii. In the ArcGIS Pro Catalog pane, find the spreadsheet in the Project3 folder (right-click on the Project3 folder name and click Refresh if the folder was open prior to the spreadsheet being added). Add the spreadsheet to the map. iii. Right-click on the file name in the Contents pane and select Geocode Table. iv. Complete the geocoding wizard in the Geoprocessing pane, following instructions on the Esri help page “Convert a table to locations on the map” (URL in Sources and Resources below). USC Spatial Sciences Institute © 2024 10 SSCI 581: Concepts in Spatial Thinking Project 3 v. Click in the pop-up window to begin the Rematching process for addresses that were unmatched or tied in the Geocoding wizard. vi. Use the Project tool to project the resulting point layer into the appropriate projected coordinate system. vii. Use the Clip tool to limit the extent of the layer to the defined study area (many buildings on the list are associated with UPC but are not actually on the campus, such as buildings on Catalina Island). TIP: USC has updated the building information and does provide addresses for some buildings, however the quality (accuracy) of these varies so I recommend using the USC_Building_Addresses.xlsx sheet that is provided for this exercise. If you decide to geocode any addresses, please refer to the current Esri Geocoding workflow and tutorial. 9. Inspect the photo point layer. I hoped to add a layer of visual VGI data to this project, to give another perspective on the space of the UPC campus. Flickr is a web site that lets users upload geotagged photos and upload non-geotagged photos and manually associate a location with them. Users can search for others’ photos by location. My hope was to batch download a fair number of the many photos tagged to the UPC campus and map them. Two large stumbling blocks presented themselves. First, Flickr does not make it easy to batch download photos. A number of third-party apps exist which do this but they have limited functionality, and I was not able to run a batch download of other users’ photos. Instead, I individually downloaded approximately 50 photos at UPC. The second stumbling block presented itself after I downloaded these photos. It turns out that Flickr extracts location information prior to download, so I was left with a series of photos without geotags. I used the Apple Photos app to manually add location information to the photos that were taken at obvious locations (the Tommy Trojan statue, etc.). This meant I could have geotagged photos as part of the project but it also meant I could not study the quality of the VGI’s location information. The photos still added a nice visual component to the various layers, so I kept them. I made sure that all the photos were in one folder, and I went through the following workflow to import and create points of the photos, and prepare the resulting layer, Photos_USC_SPCS, for study. i. Save the folder of photos to the Project3 folder in the G: drive. ii. In the ArcGIS Pro Catalog pane, right-click on the Project3 folder name and click Refresh if the folder was open prior to the folder of photos being added. iii. Run the Geotagged Photos to Points tool, directing the input to the folder of photos. iv. Run the Project tool to project the resulting point layer into the appropriate projected coordinate system. v. Remove the initial layer of points from the map. The result was a layer of points with photos as attachments. You can see the attachments in pop-ups when you click on the points on the map (Figure 8). ArcGIS offers options for customizing pop-ups, and in ArcGIS Online, you can find templates to create mapping applications that share photos in a better way than they do in ArcGIS Pro. For purposes of this project, I was satisfied with the current result. NOTE: you are not required to use photographs, this is simply a type of data that I selected. USC Spatial Sciences Institute © 2024 11 SSCI 581: Concepts in Spatial Thinking Project 3 Figure 8. Pop-up of a point created from a geotagged photo in ArcGIS Pro 10. Consider the collected data in relation to each other. In the end, I had a variety of datasets representing different themes and types of information from a variety of data sources, each of which offer different perspectives on the same place. Review the data with the following questions in mind (you will be asked to comment on these questions in your final report for this project). a. How do the OSM and LA Geohub layers of building footprints differ from each other? Do they have different ontologies (categories which distinguish features)? b. How do the geotagged points of addresses compare to the building footprints? c. How do the layers of tree canopy and impervious surfaces relate to each other, and how has each changed over time? How might temporal information about the building layers, such as date of building, be related? d. How could the visual information of the photos be useful in analyzing the relationship between buildings, tree coverage, and impervious surfaces? e. What other type of information would you like to add to this (albeit very vague) study? USC Spatial Sciences Institute © 2024 12 SSCI 581: Concepts in Spatial Thinking Project 3 11. Acquire and prepare datasets of your choosing for your own chosen location and study. Now, it is your turn! Your task is to choose a place and bring together a variety of data in ArcGIS Pro, such that the data are ready for study with each other. Do NOT merely replicate the datasets that are already provided for your location of interest. You may go through similar types of workflows as described in the sections above. Your requirements are as follows: a. Choose a bounded location. You can use an administrative boundary, such as a neighborhood, city, or county boundary, or you can draw your own boundary, as was done in the above workflow. b. Choose your location carefully. If you choose a very large area, your processes with take longer than they would for smaller areas. Similarly, you can choose anywhere in the world, but your work will be much more streamlined in you choose a place with good data availability. c. Find four sets of data from at least four data sources. Do NOT merely replicate the datasets that are already provided for your location of interest. d. Choose at least one raster layer. e. You can choose your data as if you were going to conduct a study, such that all the data layers are relevant to your given topic, but you are not required to do so. If you propose a study, it need be only generally stated, as the study described herein is a broadly described study of buildings and land cover at the UPC campus. f. Choose an appropriate projected coordinate system for a spatial analysis in your study area. g. Ensure that none of your data exceeds your chosen boundary. 12. Write up your results in a short report. The report should have your name, a title, and section headings. The report should have high-quality language, free of grammatical and spelling errors. Figures should have captions and be referred to in your text, as done in these instructions. Write out prose in complete sentences for each section; do not just use bullet points. All language must be in your own words – no quotations, even properly cited ones. Citations to your sources of information, such as for information about your chosen datasets and projected coordinate system, should be in the Author-Date format of Chicago Manual Style and properly referenced at the end of the written report. The report need only be long enough to clearly respond to the listed topics for each of the following sections: a. Introduction. Briefly summarize the data, methods, and goal of your data acquisition and preparation project. (1 pt.) b. Assessment of exemplar project. Briefly summarize the exemplar project and respond to the questions posed in step 10 in prose – NO bullet points or pasting of the prompt/questions. (1 pt.) c. Study area and coordinate system. Describe the geographic location of your data collection and preparation project. Describe the boundary that defines the study area. Describe and explain your choice of coordinate system for your study. (1 pt.) d. Data Acquisition and Preparation. Go through each of your (at least) four chosen data sets, and provide: (1.5 pts. per data set; 6 points total) i. A description of the data, including what aspect of the real world it purports to represent; USC Spatial Sciences Institute © 2024 13 SSCI 581: Concepts in Spatial Thinking Project 3 ii. The creator of the data AND, if different from the creator, the source from which you acquired the data; include a URL for the source; iii. The file format and the coordinate system of the data when you acquired it; iv. A brief description of the ontology of the data (the categories by which one feature or pixel is distinguished from another); v. The steps you took to acquire the data, bring it into ArcGIS Pro, and prepare it for your study, including the tools that you used, screenshots of data visualized in the ArcGIS Pro map before and after you prepared it for study. e. Discussion. (The results of this project are your final prepared data layers, so no need for a separate “Results” section). Consider your methodology overall and assess what you might have done differently in hindsight. Consider all your data in relation to each other and ask what you learn about the data and/or the location when bringing these data together visually. Provide additional screenshots to emphasize your points. (1 pt.) DELIVERABLES 1. Submit your report into the Project 3 Dropbox by the above-listed due date. SOURCES AND RESOURCES City of Los Angeles, LA GeoHub. http://geohub.lacity.org Esri. Clip (analysis). https://pro.arcgis.com/en/pro-app/tool-reference/analysis/clip.htm Esri. Convert a table to locations on the map. https://pro.arcgis.com/en/proapp/help/data/geocoding/convert-a-table-to-locations-on-the-map.htm Esri. Extract by Mask (Spatial Analyst). https://pro.arcgis.com/en/pro-app/tool-reference/spatialanalyst/extract-by-mask.htm Esri. Geocode Addresses (Geocode). https://pro.arcgis.com/en/pro-app/latest/toolreference/geocoding/geocode-addresses.htm Esri. GeoTagged Photos To Points (Data Management). https://pro.arcgis.com/en/pro-app/toolreference/data-management/geotagged-photos-to-points.htm Esri. Get started editing. https://pro.arcgis.com/en/pro-app/help/editing/get-started-editing.htm Esri. Minimum Bounding Geometry (Data Management). https://pro.arcgis.com/EN/PROAPP/TOOL-REFERENCE/DATA-MANAGEMENT/minimum-bounding-geometry.htm Esri. Tutorial: Geocode a Table of Addresses: https://pro.arcgis.com/en/proapp/latest/help/data/geocoding/tutorial-geocode-a-table-of-addresses.htm Esri. Quick Import (Data Interoperability). https://pro.arcgis.com/en/pro-app/toolreference/data-interoperability/quick-import.htm Flickr. https://www.flickr.com Multi-Resolution Land Characteristics Consortium. https://www.mrlc.gov/about OpenStreetMap. https://www.openstreetmap.org/ USC Spatial Sciences Institute © 2024 14
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