Quick User Guide for using Clark County Map Services with ArcGIS Online

As you might have already heard, after 20+ years of serving the GIS community ArcMap is finally retiring.The desktop role of the ArcGIS system is being replaced by ArcGIS Pro, which is more integrated with other ArcGIS solutions like ArcGIS Online, The Living Atlas, fieldwork and web applications.

To do more than the very basics of GIS you will need to sign up for a FREE Schools ArcGIS Online subscription. To sign up for a subscription contact gisinschools@eagle.co.nz

Contents: This is an ArcGIS Pro zip file that you can download and use for creating map books based on United States National Grid (USNG). It contains a geodatabase, layouts, and tasks designed to teach you how to create a basic map book.Version 1.0.0 Uploaded on May 24th and created with ArcGIS Pro 2.1.3 - Please see the README below before getting started!Updated to 1.1.0 on August 20thUpdated to 1.2.0 on September 7thUpdated to 2.0.0 on October 12thUpdate to 2.1.0 on December 29thBack to 1.2.0 due to breaking changes in the templateBack to 1.0.0 due to breaking changes in the template as of June 11th 2019Updated to 2.1.1 on October 8th 2019Audience: GIS Professionals and new users of ArcGIS Pro who support Public Safety agencies with map books. If you are looking for apps that can be used by any public safety professional, see the USNG Lookup Viewer.Purpose: To teach you how to make a map book with critical infrastructure and a basemap, based on USNG. You NEED to follow the steps in the task and not try to take shortcuts the first time you use this task in order to receive the full benefits. Background: This ArcGIS Pro template is meant to be a starting point for your map book projects and is based on best practices by the USNG National Implementation Center (TUNIC) at Delta State University and is hosted by the NAPSG Foundation. This does not replace previous templates created in ArcMap, but is a new experimental approach to making map books. We will continue to refine this template and work with other organizations to make improvements over time. So please send us your feedback admin@publicsafetygis.org and comments below. Instructions: Download the zip file by clicking on the thumbnail or the Download button.Unzip the file to an appropriate location on your computer (C:\Users\YourUsername\Documents\ArcGIS\Projects is a common location for ArcGIS Pro Projects).Open the USNG Map book Project File (APRX).If the Task is not already open by default, navigate to Catalog > Tasks > and open 'Create a US National Grid Map Book' Follow the instructions! This task will have some automated processes and models that run in the background but you should pay close attention to the instructions so you also learn all of the steps. This will allow you to innovate and customize the template for your own use.FAQsWhat is US National Grid? The US National Grid (USNG) is a point and area reference system that provides for actionable location information in a uniform format. Its use helps achieve consistent situational awareness across all levels of government, disciplines, and threats & hazards – regardless of your role in an incident.One of the key resources NAPSG makes available to support emergency responders is a basic USNG situational awareness application. See the NAPSG Foundation and USNG Center websites for more information.What is an ArcGIS Pro Task? A task is a set of preconfigured steps that guide you and others through a workflow or business process. A task can be used to implement a best-practice workflow, improve the efficiency of a workflow, or create a series of interactive tutorial steps. See "What is a Task?" for more information.Do I need to be proficient in ArcGIS Pro to use this template? We feel that this is a good starting point if you have already taken the ArcGIS Pro QuickStart Tutorials. While the task will automate many steps, you will want to get comfortable with the map layouts and other new features in ArcGIS Pro.Is this template free? This resources is provided at no-cost, but also with no guarantees of quality assurance or support at this time. Can't I just use ArcMap? Ok - here you go. USNG 1:24K Map Template for ArcMapKnown Limitations and BugsZoom To: It appears there may be a bug or limitation with automatically zooming the map to the proper extent, so get comfortable with navigation or zoom to feature via the attribute table.FGDC Compliance: We are seeking feedback from experts in the field to make sure that this meets minimum requirements. At this point in time we do not claim to have any official endorsement of standardization. File Size: Highly detailed basemaps can really add up and contribute to your overall file size, especially over a large area / many pages. Consider making a simple "Basemap" of street centerlines and building footprints.We will do the best we can to address limitations and are very open to feedback!

Thinking Spatially Using GIS

Thinking Spatially Using GIS is a 1:1 set of instructional materials for students that use ArcGIS Online to teach basic geography concepts found in upper elementary school and above.
Each module has both a teacher and student file.

The zoo in your community is so popular and successful that it has decided to expand. After careful research, zookeepers have decided to add an exotic animal to the zoo population. They are holding a contest for visitors to guess what the new animal will be. You will use skills you have learned in classification and analysis to find what part of the world the new animal is from and then identify it.

To help you get started, the zoo has provided a list of possible animals. A list of clues will help you choose the correct answers. You will combine information you have in multiple layers of maps to find your answer.

The Thinking Spatially Using GIS home is at: http://esriurl.com/TSG

All Esri GeoInquiries can be found at: http://www.esri.com/geoinquiries

Querying BigQuery tables You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.github_repos.[TABLENAME].

• Project: "bigquery-public-data"
• Table: "utility_us"

Fork this kernel to get started to learn how to safely manage analyzing large BigQuery datasets.

If you're using Python, you can start with this code:

import pandas as pd
from bq_helper import BigQueryHelper
bq_assistant = BigQueryHelper("bigquery-public-data", "utility_us")

Have you ever wanted to create your own maps, or integrate and visualize spatial datasets to examine changes in trends between locations and over time? Follow along with these training tutorials on QGIS, an open source geographic information system (GIS) and learn key concepts, procedures and skills for performing common GIS tasks – such as creating maps, as well as joining, overlaying and visualizing spatial datasets. These tutorials are geared towards new GIS users. We’ll start with foundational concepts, and build towards more advanced topics throughout – demonstrating how with a few relatively easy steps you can get quite a lot out of GIS. You can then extend these skills to datasets of thematic relevance to you in addressing tasks faced in your day-to-day work.

Learn about geodatabase components and functionality as well as steps to create and add data to a file geodatabase. This course prepares you for courses that focus on advanced geodatabase components.Exercises can be completed with either ArcGIS Pro or ArcMap.

Here is a concise, visual guide to getting to the StoryMaps platform and beginning to learn some of the tools that we will be working with over the semester. Don't hesitate to reach out if you have trouble with any of the steps along the way. This can also be a great opportunity to use the ESRI (Environmental Systems Research Institute) website and/or the [INSERT YOUR LIBRARY/IT LINK HERE].

Origo is a geospatial spreadsheet dataset documenting ancient migrants attested in the Epigraphic Database Heidelberg (EDH). It is derived from a subset of individuals in the EDH People dataset (available at: https://edh.ub.uni-heidelberg.de/data/download/edh_data_pers.csv) who explicitly declare their geographic origin in the inscriptions. Based on the data curated by the EDH team, we have geocoded the stated places of origin and further enriched the dataset with additional metadata, prioritizing machine readability. We have developed the dataset for the purpose of a quantitative study of migration trends in the Roman Empire as part of the Social Dynamics in the Ancient Mediterranean Project (SDAM, http://sdam.au.dk). The scripts used for producing the dataset and for our related publications are available from here: https://github.com/sdam-au/LI_origo/tree/master.

The dataset includes two point geometries per individual:

• Geographic origin (origo_geometry) – representing the individual’s place of origin or birth.

• Findspot (findspot_geometry) – indicating the location where the inscription was discovered, which often approximates the place of death, as approximately 70% of the inscriptions are funerary.

Scope and Structure:

The dataset covers 2,313 individuals, described through 36 attributes. For a detailed explanation of these attributes, please refer to the accompanying file origo_variable_dictionary.csv.

File Formats:

We provide the dataset in two formats for download and analysis:

1. CSV – for general spreadsheet use.

2. GeoParquet (v1.0.0) – optimized for geospatial data handling.

In the GeoParquet version, the default geometry is defined by the origo_line attribute, a linestring connecting the origo_geometry (place of origin) and the findspot_geometry (findspot of the inscription). This allows for immediate visualization and analysis of migration patterns in GIS environments.

Getting Started with Python:

To load and explore the GeoParquet dataset in Python, you can use the following code:

import geopandas as gpd
import fsspec
origo = gpd.read_parquet(fsspec.open("https://zenodo.org/records/14604222/files/origo_geo.parquet?download=1").open())

The arrangement of water in the landscape affects the distribution of many species including the distribution of humans. This layer provides a landscape-scale estimate of the distance from large water bodies. This layer provides access to a 250m cell-sized raster of distance to surface water. To facilitate mapping, the values are in units of pixels. To convert this value to meters multiply by 250. The layer was created by extracting surface water values from the World Lithology and World Land Cover layers to produce a surface water layer. The distance from water was calculated using the ArcGIS Euclidian Distance Tool. The layer was created by Esri in 2014. Dataset SummaryAnalysis: Restricted single source analysis. Maximum size of analysis is 16,000 x 16,000 pixels. What can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop. Restricted single source analysis means this layer has size constraints for analysis and it is not recommended for use with other layers in multisource analysis. This layer has query, identify, and export image services available. This layer is restricted to a maximum area of 16,000 x 16,000 pixels - an area 4,000 kilometers on a side or an area approximately the size of Europe. This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks. The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics. Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started see the Living Atlas Discussion Group. The Esri Insider Blog provides an introduction to the Ecophysiographic Mapping project.

OpenStreetMap (OSM) is a free, editable map & spatial database of the whole world. This dataset is an extract of OpenStreetMap data for the Northern Mariana Islands in a GIS-friendly format.

The OSM data has been split into separate layers based on themes (buildings, roads, points of interest, etc), and it comes bundled with a QGIS project and styles, to help you get started with using the data in your maps. This OSM product will be updated weekly.

The goal is to increase awareness among Pacific GIS users of the richness of OpenStreetMap data in Pacific countries, as well as the gaps, so that they can take advantage of this free resource, become interested in contributing to OSM, and perhaps join the global OSM community.

Tempe is among Arizona's most educated cities, lending to a creative, smart atmosphere. With more than a dozen colleges, trade schools, and universities, about 40 percent of our residents over the age of 25 have Bachelor's degrees or higher. Having such an educated and accessible workforce is a driving factor in attracting and growing jobs for residents in the region.The City of Tempe is a member of the Greater Phoenix Economic Council (GPEC), and with the membership, staff tracks collaborative efforts to recruit business prospects and locations. The Greater Phoenix Economic Council (GPEC) is a performance-driven, public-private partnership. GPEC partners with the City of Tempe, Maricopa County, 22 other communities, and more than 170 private-sector investors to promote the region’s competitive position and attract quality jobs that enable strategic economic growth and provide increased tax revenue for Tempe. This dataset provides the target and actual job creation numbers for the City of Tempe and the Greater Phoenix Economic Council (GPEC). The job creation target for Tempe is calculated by multiplying GPEC's target by twice Tempe's proportion of the population. This page provides data for the New Jobs Created performance measure.The performance measure dashboard is available at 5.02 New Jobs Created. Additional Information Source: Extracted from GPEC monthly and annual reports and proprietary excel filesContact: Madalaine McConvilleContact Phone: 480-350-2927Data Source Type: Excel filesPreparation Method: Extracted from GPEC monthly and annual reports and proprietary Excel filesPublish Frequency: AnnuallyPublish Method: ManualData Dictionary

MapMaker is an online GIS tool, developed by National Geographic in partnership with Esri, is easy to use and provides your students with interactive data on a variety of important topics. Explore the world in both two-dimensional (2D "flat map") and three-dimensional (3D "globe") displays with an intuitive interface. Designed for teachers, no login is required and lesson resources are available to help you get started.Skills builder activities

A vector GIS dataset of candidate areas for terrestrial ecological restoration based on landscape context. The dataset was created using NLCD 2011 (www.mrlc.gov) and morphological spatial pattern analysis (MSPA) (http://forest.jrc.ec.europa.eu/download/software/guidos/mspa/). There are 13 attributes for the polygons in the dataset, including presence and length of roads, candidate area size, size of surround contiguous natural areas, soil productivity, presence and length of road, areas suitable for wetland restoration, and others. This dataset is associated with the following publication: Wickham, J., K. Riiters, P. Vogt, J. Costanza, and A. Neale. An inventory of continental U.S. terrestrial candidate ecological restoration areas based on landscape context. RESTORATION ECOLOGY. Blackwell Publishing, Malden, MA, USA, 25(6): 894-902, (2017).

Dealing with large Notebooks can be daunting. Wouldn’t it be nice to import existing logic from other Modules and Code Libraries just like you can with standalone installs of Python? Well, now you can! The Manage Notebook Code Dependencies (MNCD) tool allows you to manage a cache of Python ‘Code Sample’ and ‘Notebook’ item content in your user home folder. Once cached, the content from these items can be imported into your Notebook using the Python import statement, just like your standalone scripts do. Once you import the Python modules from these items, they become ‘Code Dependencies’ to your Notebook and local scripts.This Python logic contains Functions that manage caching Python 'Code Sample' and 'Notebook' item types from ArcGIS Online or Enterprise Portal, unpacking and storing their contents in your Notebook home directory. This makes them accessible to the Notebook Kernel and Python.Once the Python objects are stored, their locations are then added to Python's import path, allowing Python to import the locally cached Modules, Classes, and Functions right to your logic.This enables you to build or leverage existing libraries of reusable code or just offload the bulk of your Notebook logic as a modular Python Script, greatly simplifying your Notebook. Share your IDE developed Python code as a 'Python Code Sample' item, call the manageDependents function to load the item contents, and then import what you need. Later, when updating your Code Sample, the manageDependents function will automatically update the cached Module next time you run the Notebook or call the function.During the MNCD import process, the logic will check for and apply updates to the MNCD logic automatically, just like it does for any managed item.RevisionsOct 20, 2020: Version 1.1 provides the abilty to import logic from other Pyton Notebooks. By default, this will extract any Function or Class Code Block it finds, focusing on modular use. Disable this option when caching a Notebook item to allow full import of available code.May 23, 2023: Version 1.2 includes a new 'getDependentCode' function to handle loading code into the local namespace or scope. It also includes improvements that allow you to provide a gis connection or it can discover an active connection when managing items.Jun 23, 2023: Version 1.3 includes updates that allow support for ArcGIS Enterprise Portal, ArcGIS Pro, and local Python use. Added 'setCachePath' function to support custom cache storage locations.To get started, launch the Installer Notebook and install this Python logic in your user home, accessible to the Notebook Kernel. A ReadMe document has been provided in the install folder. Be sure to review the examples available in the Installer Notebook.Review the MNCD documentation before you installTo get started, use the Installer Notebook to deploy the MNCD tool to ArcGIS Online or Enterprise PortalUsage:Run Installer to add the MNCD logic to your account home folder. Or you can download and store MNCD locally for use in ArcGIS Pro and Python.Import the MNCD module in your Notebook or Python logic using: import mncdWhen ready, call mncd.manageDependents function and provide the Online Item Id(s) you wish MNCD to cache and manageNow use standard Python import command to import the Functions and Classes from the cached Module(s), just like you would from a standalone script.If a cached item is no longer needed, call mncd.removeDependents function and incude the Item Id(s) you wish to remove from the cacheIf the Managed Notebook Code Dependencies logic is no longer required, run the Installer once again to remove

Done S1 7 Ge Afternoon

## Overview

Done S1 7 Ge Afternoon is a dataset for object detection tasks - it contains Person Car annotations for 374 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This data set contains a variety of easements that are captured from recorded subdivision plats and easement plats. Once a document has been recorded then the GIS Division receives that document and creates any features that are shown on the recorded document. This layer contains the easement type by category and the name of the easement as stated on the plat. For newer easements there is also the deed book and deed page. This is used to perform research on the original document in the Courts system.

This layer does not contain all easements. There are many easements before the GIS Division started capturing all easements that did not get into the layer because they were not on the original mylars where this data was captured from. Also, in 2016 the GIS Division stopped capturing stormwater easements into this layer as there is different layer that contains the stormwater related easements.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. Using the National Vegetation Classification System (NVCS) developed by Natureserve, with additional classes and modifiers, overstory vegetation communities for each park were interpreted from stereo color infrared aerial photographs using manual interpretation methods. Using a minimum mapping unit of 0.5 hectares (MMU = 0.5 ha), polygons representing areas of relatively uniform vegetation were delineated and annotated on clear plastic overlays registered to the aerial photographs. Polygons were labeled according to the dominant vegetation community. Where the polygons were not uniform, second and third vegetation classes were added. Further, a number of modifier codes were employed to indicate important aspects of the polygon that could be interpreted from the photograph (for example, burn condition). The polygons on the plastic overlays were then corrected using photogrammetric procedures and converted to vector format for use in creating a geographic information system (GIS) database for each park. In addition, high resolution color orthophotographs were created from the original aerial photographs for use in the GIS. Upon completion of the GIS database (including vegetation, orthophotos and updated roads and hydrology layers), both hardcopy and softcopy maps were produced for delivery. Metadata for each database includes a description of the vegetation classification system used for each park, summary statistics and documentation of the sources, procedures and spatial accuracies of the data. At the time of this writing, an accuracy assessment of the vegetation mapping has not been performed for most of these parks.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. An ArcInfo(tm) (ESRI) GIS database was designed for WICA using the National Park GIS Database Design, Layout, and Procedures created by the BOR. This was created through Arc Macro Language (AML) scripts that helped automate the transfer process and ensure that all spatial and attribute data was consistent and stored properly. Actual transfer of information from the interpreted aerial photographs to a digital, geo-referenced format involved two techniques, scanning (for the vegetation classes) and on-screen digitizing (for the land-use classes). Both techniques required the use of 14 digital black-and-white orthophoto quarter quadrangles (DOQQ's) covering the study area. Transferred information was used to create vegetation polygon coverages and ancillary linear coverages in ArcInfo(tm) for each WICA DOQQ. Attribute information including vegetation map unit, location, and aerial photo number was subsequently entered for all polygons.