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DD 1-0099
Data for Decision Makers
Data for Decision Makers reports can be generated by visiting the ISU Extension and Outreach Indicators Portal.
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GISTP 5
ArcGIS Pro: XY Data Points
XY data saved in several different formats can be brought into ArcGIS Pro. Some of these include: Excel (.xlsx), Excel 97-2003 (.xls), comma-separated values (.csv), or text (.txt). In this tasksheet you will turn a CSV file containing UTM coordinates into points on the map. The Universal Transverse Mercator (UTM) Coordinate System is used in this example because of its detailed accuracy, no negative values, and values are measured in meters. UTM divides Earth into 60 zones where Iowa is located in Zone 15N, you will be using NAD 1983 UTM zone 15N to locate your points on the map.
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GISTP 7
ArcGIS Pro: Creating Random Points
Creating random points in ArcGIS Pro is a tool useful especially for researchers. Imagine wanting to run a model for testing, but you don’t have the data to do so; or, you have the data but have confidentiality and privacy concerns to take into account; or, you need random points to represent a survey sample for which the georeferenced data is classified. In this tutorial, you will work with polygon data of Iowa watersheds and create random points from the polygon border.
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GISTP 13
ArcGIS Pro: Working with Lidar Data
For this tutorial you will be processing lidar (light detection and ranging) data from an area near Ledges State Park in Boone, Iowa. Lidar is a remote sensing technique similar to radar, however lidar uses light waves instead of sound waves to create more accurate and detailed measurements. You will be downloading several LAS lidar files and using ArcGIS Pro tools to convert them into a single raster image. To learn how to source your own Iowa lidar data, we suggest reading the task sheet GISTP 0016 - Downloading Iowa Lidar Data.
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GISTP 16
Downloading Iowa Lidar Data
Lidar (light detection and ranging) is a remote-sensing technology which uses lasers and GPS (global positioning system) receivers affixed to airplanes to generate precise measurements of the Earth’s surface. By measuring how long a pulse of light from a laser takes to strike the ground or an object and return to the sensor, a computer is able to accurately calculate the distance the pulse travelled. When this is done over a wide area, it is possible to generate very accurate elevation profiles. In this tutorial, you will learn how to navigate and use the GeoInformatics Training Research Education and Extension (GeoTREE) website hosted by the University of Northern Iowa to download lidar data for locations within the state of Iowa.
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GISTP 18
Downloading and Preparing U.S. Census Data for Mapping
The U.S. Census Bureau provides many different datasets on topics such as population, income, housing, and education. Data.census.gov is the platform to access demographic and economic data collected through the decennial census, American Community Survey (ACS), and other surveys. This is the replacement for the American Fact Finder website. This task sheet will take you through the steps of downloading U.S. Census Bureau data and preparing the file for mapping using ArcGIS Pro.
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GISTP 19
ArcGIS Pro: Joining U.S. Census Data to a Shapefile
The U.S. Census Bureau provides many different datasets on topics such as population, income, housing, and education. This tutorial builds on GISTP 0018 - Downloading and Preparing U.S. Census Data for Mapping and will take you through the steps of joining county-level income data to a shapefile of Iowa counties. After joining the data, we will export the data to a geodatabase making the join permanent.
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GISTP 21
Spatial Data Science with R: Getting Started with R and RStudio
R is a free software environment for statistical computing and graphics. It compiles and runs on Windows, MacOS, and a wide variety of Linux platforms. R supports advanced statistics, modeling, and data visualization through downloadable collections of functions called packages. More information can be found at https://www.r-project.org. In this task sheet, you will learn how to install R and the RStudio Integrated Development Environment (IDE) and write some basic R commands. This task sheet is the first in a series of tutorials introducing spatial data science concepts using the R programming language.
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GISTP 22
Spatial Data Science with R: Overview of the RStudio Interface
RStudio (recently re-branded as Posit) is an integrated development environment (IDE) for programming in both the R and Python programming languages. RStudio combines a number of different tools into a single application: source code editor, debugger, versioning control, environment and package managers, and more. This task sheet will give an overview of the user interface and some key features of RStudio. Installing R and RStudio are covered in GISTP 0021 - Getting Started with R and R Studio. Although RStudio works with Python, this series is focused on the R programming language.
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GISTP 23
Spatial Data Science with R: Installing Packages with RStudio
An R package is a collection of reusable code, documentation and sample data that can be installed into your R environment to greatly expand it’s functionality. The packages you will want to install and use will vary depending on your project goals or the type data you are using. This task sheet will show you how to install packages from CRAN (Comprehensive R Archive Network) and GitHub, how to load a package for use, and how to determine which packages are currently installed and loaded in your R session. This task sheet builds on content from GISTP 0021 - Getting Started with R and RStudio.
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GISTP 24
Spatial Data Science with R: An Introduction to the Tidyverse
The tidyverse is a collection of open source R packages that are useful for data scientists. With the packages loaded into projects, the common API design provides a coherent system, or toolbox, that makes it easy to perform common data science tasks such as importing, wrangling (tidying and manipulation), modeling data, presenting results, and programming. This task sheet will briefly introduce each of the core packages. For more information on the tidyverse, visit the official tidyverse website. Additional task sheets covering tidyverse packages include GISTP 0025 - Filtering and Selecting Data with dplyr, GISTP 0026 - Mutating and Piping Data with dplyr, and GISTP 0029 - Joining Spatial Data with dplyr.
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GISTP 25
Spatial Data Science with R: Filtering and Selecting Data with dplyr
A part of the data science-oriented Tidyverse collection of packages (see GISTP 0024 - Tidyverse Overvie), dplyr provides a grammar of data manipulation by providing a set of verbs to use when working with data frames. In this task sheet you will 1) install the Tidyverse packages 2) install a dataset containing demographic data on the United States at the county and state level; 3) learn how to use dplyr to subset your data by filtering rows based on column values and selecting specific columns to include in a dataset. You can learn more dplyr functions in the task sheet GISTP 0026 - Mutating and Piping Data with dplyr.
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GISTP 26
Spatial Data Science with R: Mutating and Piping Data with dplyr
In this task sheet you will learn how to add new columns to a dataset with the mutate function. This lesson will also introduce the pipe operator, %>%, which allows you to chain together functions by “piping” the output of one function to the input of the next function. You will continue to work with the usdata dataset used in the task sheet GISTP 0025 - Filtering and Selecting Data with dplyr, which covers installing both dplyr and the usdata package.
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GISTP 27
Spatial Data Science with R: Importing CSV and Excel into Data Frames
In this task sheet you will learn how to import two common data file types into your R environment: comma seperated value (CSV) files and Microsoft Excel spreadsheets. The base R installation can read CSV files without any additional software; for Excel files you can use the package readxl. Both methods produce a data frame as the result. If you are new to programming in R, you may find useful the previous task sheets in the series: GISTP 0021 - Getting Started with R and RStudio, and GISTP 0023 - Installing Packages with RStudio.
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GISTP 28
Spatial Data Science with R: Plotting a Shapefile with sf
In this task sheet you will install and use the sf package in R to work with spatial data. The sf package implements the Simple Features standard – an open-source model for storing and accessing vector data (point, line, and polygon-based geometry) commonly used by geographic information systems. You will install the sf package and visualize spatial data provided by the United States Census Bureau in shapefile format, a file type frequently used in spatial analysis and map-making. This task sheet also uses concepts covered in GISTP 0025 - Filtering and Selecting Data with dplyr and GISTP 0026 - Mutating and Piping Data with dplyr.
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GISTP 29
Spatial Data Science with R: Joining Spatial Data with dplyr
This task sheet will cover a common action in geospatial workflows: using a join operation to enrich spatial data with non-spatial attribute data. When you join two sets of data, you match rows from one data set to another based on a common field. This task sheet will go through the steps of joining county-level demographic data (from the usdata package) to county-level geographic data (from a shapefile) using the dplyr package in R. This task sheet builds on concepts covered in: GISTP 0025 - Filtering and Selecting Data with dplyr, GISTP 0026 - Mutating and Piping Data with dplyr, and GISTP 0028 - Plotting a Shapefile with sf.
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GISTP 33
ArcGIS Pro: Mapping Strava GPX Data
GPS Exchange (GPX) format is an open-source file format for sharing location data between GPS-enabled devices and computer applications. Many popular geo-enabled mobile apps use GPX to track route and way-point information. Strava is a popular exercise-tracking platform and social network popular with both amateur and professional athletes, and is available on iOS and Android. In this task sheet, you will learn how to download a GPX file associated with an activity from Strava and import the data into ArcGIS Pro where you will visualize the location data on a map and create an elevation profile of your route.
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GISTP 40
Essential Data Science: Introduction to Tableau Products
Welcome to the Essential Data Science Task Sheet Series. This series supplements the Iowa State University Geospatial Technology Training Program and is designed to provide quick, easy instructions for creating interactive data visualizations in either Tableau Public or Tableau Desktop.

Tableau’s products are software applications that allow users to create, visualize, and display data visualizations (or “vizzes”). Users can work with both tabular (.xls, .xlsx, .txt, .csv, .tab, .tsv, .json, .rdata) and geospatial (.shp, .gdb, .geojson, .topojson, .mif, and .kml) file formats and most Tableau programs can be used in-browser or can be downloaded onto Linux, Windows, or Mac operating systems. Tableau’s software offerings can be confusing. In this tutorial, you will learn about several popular Tableau products to help you choose which will be most helpful for your use cases.
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PM 2082-08A
Getting Started with ArcGIS Explorer: Opening ArcGIS Explorer and Adding Map Data
ArcGIS Explorer is a free GIS application produced by ESRI that can be used to access, view and interact with online and local GIS content. The software is available from: www.esri.com

To add shapefiles for this task sheet, download a sample file from the NRGIS web site by consulting task sheet PM2082-09Q regarding downloading from the Iowa DNR.
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PM 2082-08B
Getting Started with DNR Garmin: Saving Data from GPS Devices
The DNR GARMIN program is a free application from the Minnesota DNR. The software is available from: www.dnr.state.mn.us/mis/gis
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PM 2082-09A
Customizing ArcGIS settings for improved productivity: Setting Mouse and Data Source Options
The default zoom and pan features are quite different between ArcMap and other applications such as Google Maps and Google Earth. This can make workflow, at the very least, annoying, but options can be changed within ArcMap for similar zoom and pan features that may allow improved productivity when using multiple programs.

This task sheet will cover customization of mouse settings and data source options.
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PM 2082-09C
Getting Started with Google Maps: Adding KML data
A KML file is a Google Earth format which is supported by servered mapping applications including Google Maps, a free online resource from Google which allows for personalized maps and sharing of spatial data.

To get started, visit: www.google.com/accounts/NewAccount for a new account or visit: www.google.com/maps if an account has already been made.
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PM 2082-09Q
Working with External Data: Downloading from the Iowa DNR
The Iowa Department of Natural Resources provides shapefile data from Iowa free to download. Data is available by theme at state and county levels at: www.igsb.uiowa.edu/nrgislibx/gishome.htm.
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PM 2082-13B
Building Map Books with Data Driven Pages
A map book is a collection of maps and text that belong in a series. ArcGIS provides all the tools necessary to create automated map books in printed or PDF format through the combination of Data Driven Pages and Python scripts. This task sheet will use Data Driven Pages to create a map book showing incorporated cities, water-bodies, and roads for each county in Iowa.
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PM 2082-14F
Web Mapping: Getting started with JSON data
An alternative to XML, JavaScript Object Notation or JSON is a popular data-interchange that is now commonly used by web-mapping tools and web-based APIs. Based on a subset of the JavaScript Programming Language, JSON is an open standard format that has become the data workhorse for transferring data between server and web applications and is used by a variety of programming languages.

This task sheet will provide a quick overview of the JSON syntax.
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PM 2082-14I
XY Data to Points
XY data saved in several different formats can be brought into the ArcGIS software suite, these include: Excel 97-2003 (.xls), comma-separated values (.csv) or text (.txt).

This task sheet will show an example with a text file containing coordinates in decimal degrees. The X represents the longitude and the Y represents latitude.
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PM 2082-14L
Geocoding with ArcGIS for Desktop: Esri Data & Maps Locator
Geocoding is the process of converting addresses or other identifiers (cities, postal codes) to x, y coordinates on a map. This task is pretty easy and straight forward when using a robust locator.

This task sheet will cover geocoding in ArcGIS for Desktop using the address locator provided in the Data & Maps DVD that comes with an ArcGIS software subscription. You must have access to the data and tools provided in the Data & Maps DVD to follow this task sheet.
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PM 2082-15D
Google Earth Pro: Exporting Geocoded Data to ArcGIS or QGIS
This task sheet acts as a continuation from Google Earth Pro: Batch Geocoding, publication number: PM2082-15C and focuses on how to save a file from Google Earth and open it in other GIS applications.
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PM 2082-15X
Data Interoperability Extension: Import and Export GeoJSON
The Data Interoperability extension tool, Quick Import, converts any data source supported by the Data Interoperability extension (http://www.esri.com/software/arcgis/extensions/datainteroperability/supported-formats) and loads it into a geodatabase. The Quick Export tool converts geodatabase feature classes or feature layers to any external dataset supported by the Data Interoperability extension.

This task sheet will cover how to import and export GeoJSON data using the Data Interoperability extension tools for ArcGIS and will show you how to test the exported GeoJSON file using geojson.io.
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PM 2082-16B
Importing Data into Google Web Maps
Google Maps offers a free, easy-to-use platform for creating maps that can be shared Online and embedded in web pages. Users can create spatial data with drawing tools, as well as import a variety of data formats to be viewed and styled using Google Maps.

This task sheet will demonstrate how to import various data formats. This task sheet builds on the skills learned in PM 2082-16A.
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PM 2082-16G
Simplifying and Editing Vector Data with mapshaper.org
MapShaper.org is a free-to-use web service that allows users to simplify vector data and export to a variety of file types. Simplifying shapes can be beneficial because it lessens the number of vertices in a shape, which ultimately reduces the file size. This might be desirable if you’re working with a very large dataset, such as zip codes for the entire United States. Simplifying vector will make your file size smaller, which is great if you’re working with the GeoJSON format to create web maps. Mapshaper also offers a console for running tools through a command-line interface (CLI) which allows users to edit data and instantly visualize the results.
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PM 2082-16O
Downloading and Preparing U.S. Census Bureau Data for Mapping
The U.S. Census Bureau provides many different datasets on topics such as population, demographics, housing and income. The American Fact Finder website allows users to find and download data collected through the decennial census and other surveys.

This task sheet will take you through the steps of downloading U.S. Census Bureau data from the American Fact Finder website and preparing the file for mapping in ArcGIS.
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PM 2082-16P
Joining and Mapping U.S. Census Bureau Data
The U.S. Census Bureau provides many different datasets on topics such as population, and demographics. The American Community Survey (ACS) is a nationwide survey carried out by the U.S. Census Bureau that measures and describes the social, economic, housing, and demographic characteristics of the U.S. population, including states, regions, counties, and local areas.

This task sheet builds on the Downloading and Preparing U.S. Census Bureau Data for Mapping task sheet and will take you through the steps of joining U.S. Census Bureau data to a shapefile for mapping and displaying data at the county level.
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PM 2082-16Q
QGIS: Downloading and Preparing U.S. Census Bureau Data
The U.S. Census Bureau provides many different datasets on topics such as population, demographics, housing and income. The American Fact Finder website allows users to find and download data collected through the decennial census and other surveys.

This task sheet will take you through the steps of downloading U.S. Census Bureau data from the American Fact Finder website and preparing the file for mapping in QGIS.
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PM 2082-16R
QGIS: Joining and Mapping U.S. Census Bureau Data
The U.S. Census Bureau provides many different datasets on topics such as population, and demographics. The American Community Survey (ACS) is a nationwide survey carried out by the U.S. Census Bureau that measures and describes the social, economic, housing, and demographic characteristics of the U.S. population, including states, regions, counties, and local areas.

This task sheet builds on the QGIS: Downloading and Preparing U.S. Census Bureau Data task sheet and will take you through the steps of joining U.S. Census Bureau data to a shapefile for mapping and displaying data at the county level.
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PM 2082-16T
Creating Geospatial Data with geojson.io
Geojson.io is a quick, simple tool for creating, viewing and sharing maps.

This task sheet will demonstrate how to use geojson.io to create a GeoJSON file, add properties to the GeoJSON, and edit styling within the properties of the GeoJSON file. For more information on the GeoJSON file format, please refer to the publication, Shapefile to GeoJSON: Conversion and Visualization PM 2082-15G.
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PM 2082-17C
Adding Raster Data to Mapbox Studio
Mapbox is a versatile mapping platform that allows users to build maps and apps. This task sheet will focus on Mapbox Studio, an interface for styling custom basemaps.

This task sheet will build upon skills developed in a previous task sheet, Custom Basemaps with Mapbox Studio. This task sheet will guide you in adding a raster dataset to your basemap in Mapbox Studio. We will build a basemap that depicts how local businesses relate to municipal zoning in Ames, Iowa.
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PM 2082-17E
Adding Vector Data to Mapbox Studio
Mapbox is a versatile mapping platform that allows users to build maps and apps. This task sheet will focus on Mapbox Studio, an interface for styling custom basemaps. This task sheet will guide you in adding vector datasets to your basemap in Mapbox Studio and will build upon skills developed in a previous task sheet, Custom Basemaps with Mapbox Studio PM2082-17C. We will build a basemap that depicts the last ten years of RAGBRAI routes across Iowa.
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PM 2082-18C
QGIS: Download and Export OpenStreetMap Data
This task sheet presents an open source workflow that uses crowd-sourced data from OpenStreetMap (OSM) to create GeoJSON files that can be used on web mapping platforms like leaflet or mapbox. Using free QGIS software, users can download data from OSM, and then filter and edit it within the QGIS environment. The data can be used for analysis, in map layouts, or exported to various file formats, including GeoJSON.
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