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 locates. 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 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 InformationSource:Contact: 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

I’d love to begin by saying that I have not “arrived” as I believe I am still on a journey of self-discovery. I have heard people say that they find my journey quite interesting and I hope my story inspires someone out there.I had my first encounter with Geographic Information System (GIS) in the third year of my undergraduate study in Geography at the University of Ibadan, Oyo State Nigeria. I was opportune to be introduced to the essentials of GIS by one of the prominent Environmental and Urban Geographers in person of Dr O.J Taiwo. Even though the whole syllabus and teaching sounded abstract to me due to the little exposure to a practical hands-on approach to GIS software, I developed a keen interest in the theoretical learning and I ended up scoring 70% in my final course exam.

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.

In this course, you will explore a variety of open-source technologies for working with geosptial data, performing spatial analysis, and undertaking general data science. The first component of the class focuses on the use of QGIS and associated technologies (GDAL, PROJ, GRASS, SAGA, and Orfeo Toolbox). The second component of the class introduces Python and associated open-source libraries and modules (NumPy, Pandas, Matplotlib, Seaborn, GeoPandas, Rasterio, WhiteboxTools, and Scikit-Learn) used by geospatial scientists and data scientists. We also provide an introduction to Structured Query Language (SQL) for performing table and spatial queries. This course is designed for individuals that have a background in GIS, such as working in the ArcGIS environment, but no prior experience using open-source software and/or coding. You will be asked to work through a series of lecture modules and videos broken into several topic areas, as outlined below. Fourteen assignments and the required data have been provided as hands-on opportunites to work with data and the discussed technologies and methods. If you have any questions or suggestions, feel free to contact us. We hope to continue to update and improve this course. This course was produced by West Virginia View (http://www.wvview.org/) with support from AmericaView (https://americaview.org/). This material is based upon work supported by the U.S. Geological Survey under Grant/Cooperative Agreement No. G18AP00077. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the opinions or policies of the U.S. Geological Survey. Mention of trade names or commercial products does not constitute their endorsement by the U.S. Geological Survey. After completing this course you will be able to: apply QGIS to visualize, query, and analyze vector and raster spatial data. use available resources to further expand your knowledge of open-source technologies. describe and use a variety of open data formats. code in Python at an intermediate-level. read, summarize, visualize, and analyze data using open Python libraries. create spatial predictive models using Python and associated libraries. use SQL to perform table and spatial queries at an intermediate-level.

The National Hydrography Dataset Plus High Resolution (NHDplus High Resolution) maps the lakes, ponds, streams, rivers and other surface waters of the United States. Created by the US Geological Survey, NHDPlus High Resolution provides mean annual flow and velocity estimates for rivers and streams. Additional attributes provide connections between features facilitating complicated analyses.For more information on the NHDPlus High Resolution dataset see the User’s Guide for the National Hydrography Dataset Plus (NHDPlus) High Resolution.Dataset SummaryPhenomenon Mapped: Surface waters and related features of the United States and associated territoriesGeographic Extent: The Contiguous United States, Hawaii, portions of Alaska, Puerto Rico, Guam, US Virgin Islands, Northern Marianas Islands, and American SamoaProjection: Web Mercator Auxiliary Sphere Visible Scale: Visible at all scales but layer draws best at scales larger than 1:1,000,000Source: USGSUpdate Frequency: AnnualPublication Date: July 2022This layer was symbolized in the ArcGIS Map Viewer and while the features will draw in the Classic Map Viewer the advanced symbology will not. Prior to publication, the network and non-network flowline feature classes were combined into a single flowline layer. Similarly, the Area and Waterbody feature classes were merged under a single schema.Attribute fields were added to the flowline and waterbody layers to simplify symbology and enhance the layer's pop-ups. Fields added include Pop-up Title, Pop-up Subtitle, Esri Symbology (waterbodies only), and Feature Code Description. All other attributes are from the original dataset. No data values -9999 and -9998 were converted to Null values.What can you do with this layer?Feature layers work throughout the ArcGIS system. Generally your work flow with feature layers will begin in ArcGIS Online or ArcGIS Pro. Below are just a few of the things you can do with a feature service in Online and Pro.ArcGIS OnlineAdd this layer to a map in the map viewer. The layer or a map containing it can be used in an application. Change the layer’s transparency and set its visibility rangeOpen the layer’s attribute table and make selections. Selections made in the map or table are reflected in the other. Center on selection allows you to zoom to features selected in the map or table and show selected records allows you to view the selected records in the table.Apply filters. For example you can set a filter to show larger streams and rivers using the mean annual flow attribute or the stream order attribute.Change the layer’s style and symbologyAdd labels and set their propertiesCustomize the pop-upUse as an input to the ArcGIS Online analysis tools. This layer works well as a reference layer with the trace downstream and watershed tools. The buffer tool can be used to draw protective boundaries around streams and the extract data tool can be used to create copies of portions of the data.ArcGIS ProAdd this layer to a 2d or 3d map.Use as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class.Change the symbology and the attribute field used to symbolize the dataOpen table and make interactive selections with the mapModify the pop-upsApply Definition Queries to create sub-sets of the layerThis layer is part of the ArcGIS Living Atlas of the World that provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.

This dataset contains data on ES clients for each of the twenty-six Local Board (LB) areas in Ontario for the 2015/16 fiscal year, based on data provided to Local Boards and Local Employment Planning Councils (LEPC) in June 2016 (see below for details on Local Boards). This includes all assisted services clients whose service plan was closed in the 2015/16 fiscal year and all unassisted services clients who accessed unassisted services in the 2015/16 fiscal year. These clients have been distributed across Local Board areas based on the address of each client’s service delivery site, not the client’s home address. Note that clients who had multiple service plans close in the 2015/16 fiscal year (i.e. more than one distinct period during which the client was accessing assisted services) will be counted multiple times in this dataset (once for each closed service plan). Assisted services clients who also accessed unassisted services either before or after accessing assisted services would also be included in the count of unassisted clients (in addition to their assisted services data).

Demographic data on ES assisted services clients, including a client’s suitability indicators and barriers to employment, are collected by the service provider when a client registers for ES (i.e. at intake). Outcomes data on ES assisted services clients is collected through surveys at exit (i.e. when the client has completed accessing ES services and the client’s service plan is closed) and at three, six, and twelve months after exit. As demographic and outcomes data is only collected for assisted services clients, all fields in this dataset contain data only on assisted services clients except for the ‘Number of Clients – Unassisted R&I Clients’ field.

Note that ES is the gateway for other Employment Ontario programs and services; the majority of Second Career (SC) clients, some apprentices, and some Literacy and Basic Skills (LBS) clients have also accessed ES. It is standard procedure for SC, LBS and apprenticeship client and outcome data to be entered as ES data if the program is part of ES service plan. However, for this dataset, SC client and outcomes data has been separated from ES, which as a result lowers the client and outcome counts for ES.

Definitions for fields in this layer are available in the abbreviated Technical Dictionary.

This dataset comes from the Biennial City of Tempe Employee Survey questions related to employee engagement. Survey respondents are asked to rate their level of agreement on a scale of 5 to 1, where 5 means "Strongly Agree" and 1 means "Strongly Disagree".This dataset includes responses to the following statements:I have received fair consideration for advancement & promotion, when available, within City of TempeI have been mentored at workThe City's programs related to professional development & career mobility, such as educational partnerships, Tempe Professional Development Network, etc., are useful to meThe following adequately support my work-related needs: City Manager's OfficeThe following adequately support my work-related needs: Strategic Management & Diversity OfficeI believe my opinions seem to countConflict in my work area is resolved effectivelyI believe exceptional job performance is recognized appropriately by managers/supervisors in my work unitThe amount that I pay for health care benefits is reasonableI think the amount I am paid is adequate for the work I doCommunication between my work unit/pision & work units/pisions OUTSIDE my department is goodEmployees in my department take personal accountability for their actions and work performance (starting in 2018 survey)Participation in the survey is voluntary and confidential.This page provides data for the Employee Engagement performance measure. The performance measure dashboard is available at 2.13 Employee Engagement.Additional InformationSource: paper and digital survey submissionsContact: Aaron PetersonContact E-Mail: Aaron_Peterson@tempe.govData Source Type: ExcelPreparation Method: NAPublish Frequency: biennialPublish Method: ManualData Dictionary

Analysis of ‘5.02 New Jobs Created (summary)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/73bc502b-2b3a-4ab7-83ad-4649019064d0 on 11 February 2022.

--- Dataset description provided by original source is as follows ---

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 locates. 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 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:
Contact: Jill Buschbacher
Contact E-Mail: Jill_Buschbacher@tempe.gov
Data Source Type: Excel files
Preparation Method: Extracted from GPEC monthly and annual reports and proprietary excel files
Publish Frequency: Annually
Publish Method: manual
Data Dictionary

--- Original source retains full ownership of the source dataset ---

Pleiades Datasets 3.2 (3 November 2023)

This is a package of data derived from the Pleiades gazetteer of ancient places. It is used for archival and redistribution purposes and is likely to be less up-to-date than the live data at https://pleiades.stoa.org. We encourage use and citation of these numbered releases for scholarly work that will be published in static form.

Updates and additions to published content of the Pleiades gazetteer of ancient places between 1 August 2023 and 3 November 2023

What's new since 3.1 (1 August 2023):

• 108 new and 1,629 updated place resources reflecting work by Erin Walcek Averett, Jeffrey Becker, Catherine Bouras, Anne Chen, Niels Christoffersen, Peter Cobb, Tom Elliott, Jonathan Fu, Greta Hawes, Carolin Johansson, Noah Kaye, Brady Kiesling, Thomas Landvatter, Stanisław Ludwiński, Ingrid Luo, Stephan Maurer, Colin McCaffrey, Gabriel McKee, David Meadows, Gabriel Moss, John Muccigrosso, Gifford Quinn, Rune Rattenborg, Enrico Regazzoni, Rosemary Selth, R. Scott Smith, Richard Talbert, Georgios Tsolakis, and Scott Vanderbilt (see data/changelogs/release.html for details).
• Included experimental JSON index of links extracted from Pleiades place resources to "toponym" entries in Veronique Chankowski et al. Chronique Des Fouilles En Ligne = Archaeology in Greece Online. Athens: Ecole française d'Athènes and British School at Athens, 2018, together with links to the associated Chronique archaeological reports. See data/indexes/pids2chronique.json.

About Pleiades

Pleiades is a community-built gazetteer and graph of ancient places. It publishes authoritative information about ancient places and spaces, providing unique services for finding, displaying, and reusing that information under open license. It publishes not just for individual human users, but also for search engines and for the widening array of computational research and visualization tools that support humanities teaching and research.

Pleiades is a continuously published scholarly reference work for the 21st century. We embrace the new paradigm of citizen humanities, encouraging contributions from any knowledgeable person and doing so in a context of pervasive peer review. Pleiades welcomes your contribution, no matter how small, and we have a number of useful tasks suitable for volunteers of every interest.

Credits

Pleiades is brought to you by:

• Our volunteer content contributors (see data/rdf/authors.ttl for complete list and associated identifiers or data).
• Pleiades has received significant, periodic support from the National Endowment for the Humanities since 2006. Grant numbers: HK-230973-15, PA-51873-06, PX-50003-08, and PW-50557-10. Any views, findings, conclusions, or recommendations expressed in this publication do not necessarily reflect those of the National Endowment for the Humanities.
• Additional support has been provided since 2000 by the Ancient World Mapping Center at the University of North Carolina at Chapel Hill. * Development hosting and other project incubation support was provided between 2000 and 2008 by Ross Scaife and the Stoa Consortium.
• Web hosting and additional support has been provided since 2008 by the Institute for the Study of the Ancient World at New York University.

description: This database contains locations of day care centers for 39 states which include the states of AZ, CA, , NV, NY, HI. This is a work in progress and data for remaining states will be added as they become available. The dataset only includes center based day care locations (including schools and religious institutes) and does not include home and family based day cares. All the data was acquired from respective states departments or their open source websites and then geocoded and converted into a spatial database, data for Washington D.C., Puerto Rico, Delaware and Louisiana was obtained in a GIS format. Information on the source of data for each state is available in the database itself. After geocoding the exact spatial location of each point is being verified using high resolution imagery and ancillary dataset and points are being moved to rooftops wherever possible, this is an ongoing work and points which have been physically verified have been labeled "Geocode", "Imagery", "Imagery with other" and "Unverified" depending on the methodology used to move the points. "Unverified" data points have still not being physically examined even though each of the points has been street geocoded as mentioned above. "Unverified" points for Puerto Rico, Washington DC and the states of Louisiana and Delaware may have better positional accuracy as data for these was obtained in GIS format. The "TYPE" attribute has not been populated yet, this will be populated once a common classification of day care for all states has been decided. The "O_TYPE" attribute contains the classification provided by individual states.; abstract: This database contains locations of day care centers for 39 states which include the states of AZ, CA, , NV, NY, HI. This is a work in progress and data for remaining states will be added as they become available. The dataset only includes center based day care locations (including schools and religious institutes) and does not include home and family based day cares. All the data was acquired from respective states departments or their open source websites and then geocoded and converted into a spatial database, data for Washington D.C., Puerto Rico, Delaware and Louisiana was obtained in a GIS format. Information on the source of data for each state is available in the database itself. After geocoding the exact spatial location of each point is being verified using high resolution imagery and ancillary dataset and points are being moved to rooftops wherever possible, this is an ongoing work and points which have been physically verified have been labeled "Geocode", "Imagery", "Imagery with other" and "Unverified" depending on the methodology used to move the points. "Unverified" data points have still not being physically examined even though each of the points has been street geocoded as mentioned above. "Unverified" points for Puerto Rico, Washington DC and the states of Louisiana and Delaware may have better positional accuracy as data for these was obtained in GIS format. The "TYPE" attribute has not been populated yet, this will be populated once a common classification of day care for all states has been decided. The "O_TYPE" attribute contains the classification provided by individual states.

How did the City create the Equity IndexWorking with Ohio State University's Kirwan Institute of Race and Social Justice, the City complied the Equity/Opportunity Index to help facilitate data-driven decision-making processes and enable leaders to distribute resources better and plan to fund programs and services, minimize inequities and maximize opportunities.The indicators displayed in the Equity/Opportunity Index have been shown to have a direct correlation to equity. For more information, please reference the additional document on the evidence-based research determinant categories. The data is measured granularly by census block group.To just access the overall equity layer use this url: https://gis.cityoftacoma.org/arcgis/rest/services/General/Equity2020/MapServer/1 The list below comprise the Indicators per index: Accessibility Parks & Open SpaceVoter ParticipationHealthy Food Access IndexAverage Road QualityHome Internet AccessTransit Options & AccessVehicle AccessLivabilityTacoma Crime IndexESRI Crime IndexCost-Burdened HouseholdsAverage Life ExpectancyUrban Tree CanopyTacoma Nuisance IndexMedian Home ValueEducationAverage Student Test RateAverage Student Mobility4-Year High School Graduation RatePercent of 25+-Year-Olds with Bachelor's Degree or MoreEconomyJobs Index (availableness of good paying jobs)Median Household Income200% Below of the Poverty Line or LessUnemployment RateEnvironmental HealthEnvironmental ExposuresNOx- Diesel Emissions (Annual Tons/Km2)Ozone ConcentrationPM2.5 ConcentrationPopulations Near Heavy Traffic RoadwaysToxic Releases from Facilities (RSEI Model)Environmental EffectsLead Risk from Housing (%)Proximity to Hazardous Waste Treatment Storage and Disposal Facilities (TSDFs)Proximity to National Priorities List Facilities (Superfund Sites)Proximity to Risk Management Plan (RMP) FacilitiesWastewater DischargeWhat does Very High or Very Low Equity/Opportunity mean?Very High Equity/Opportunity represents locations that have access to better opportunities to succeed and excel in life. The indicators include high-performing schools, a safe environment, access to adequate transportation, safe neighborhoods, and sustainable employment. In contrast, Low Equity/Opportunity areas have more obstacles and barriers within the area. These communities have limited access to institutional or societal investments with limits their quality of life.Why is the North and West End labeled Red?When looking at data related to equity and social justice, we want to be mindful not to reinforce historical representations of low-income or communities of color as bad or negative. To help visualize the areas of high opportunity and call out the need for more equity, we chose to use red. We flipped the gradient to highlight disparities within the community. Besides, we refrained from using green or positive colors with referring to dominant communities (white communities).Can I download the full dataset and display other variables over the Equity Index?Yes, by downloading the file and uploading it to ArcGIS, you will be able to see all the indicators, Z-Scores, indices, and the index overall value. You can overlay other variables for further analysis and save the output into your database. If your team wants to add new indicators to the Equity Index, contact Bucoda Warren. Can I see additional or multiple map layers?Within the left navigation panel, you can aggregate the index layers by determinate social categories; Accessibility, Education, Economy, Livability and Environmental Health.

4.0.1 is a minor release to correct a deployment problem from Github to Zenodo.org. Content is the same as the 4.0 release:

Pleiades gazetteer datasets

Please report problems and make feature requests via the main Pleiades Gazetteer Issue Tracker.

Content is governed by the copyrights of the individual contributors responsible for its creation. Some rights are reserved. All content is distributed under the terms of a Creative Commons Attribution license (cc-by).

In order to facilitate reproducibility and to comply with license terms, we encourage use and citation of numbered releases for scholarly work that will be published in static form.

Please share notices of data reuse with the Pleiades community via email to pleiades.admin@nyu.edu. These reports help us to justify continued funding and operation of the gazetteer and to prioritize updates and improvements.

Version 4.0 - 6 February 2025

41,200 place resources

Since release 3.2 of pleiades.datasets on 3 November 2023, the Pleiades gazetteer published 876 new and 9,555 updated place resources, reflecting the work of Johan Åhlfeldt, Ella Arnold, Jeffrey Becker, Gabriel Bodard, Sarah Bond, Catherine Bouras, Lucas Butler, Iulian Bîrzescu, Anne Chen, Birgit Christiansen, Niels Christofferson, James Cowey, Francis Deblauwe, Dan Diffendale, Anthony Durham, Denitsa Dzhigova, Tom Elliott, Jordy Didier Orellana Figueroa, Martina Filosa, Jonathan Fu, Ryosuke Furui, Maija Gierhart, Sean Gillies, Matthias Grawehr, Amelia Grissom, Maxime Guénette, Andrew Harris, Greta Hawes, Ryan M. Horne, Carolin Johansson, Daniel C. Browning Jr., Noah Kaye, Philip Kenrick, Brady Kiesling, Yaniv Korman, Mark Krier, Divya Kumar-Dumas, Thomas Landvatter, Chris de Lisle, Yuyao Liu, Stanisław Ludwiński, Sean Manning, Gabriel McKee, John Muccigrosso, Jamie Novotny, Philipp Pilhofer, Jonathan Prag, Adam Rabinowitz, Rune Rattenborg, María Jesús Redondo, Charlotte Roueché, Karen Rubinson, Thomas Seidler, Rosemary Selth, Jason M. Silverman, R. Scott Smith, Néhémie Strupler, Richard Talbert, Francis Tassaux, Clifflena Tiah, Georgios Tsolakis, Scott Vanderbilt, Athanasia Varveri and Valeria Vitale.

Highlights

• Updated gazetteer data in this release: see "Contents" below.
• Removed deprecated "legacy CSV" serialization. JSON or "CSV for GIS" are the recommended packages for most third-party reuse.
• Added new "indexes" dataset: Pleiades places that reference certain external resources.
• Improved serialization of vocabulary terms in "CSV for GIS" serialization and added the previously omitted "Time Periods" vocabulary.
• Added new "sidebar" dataset: assertions by external datasets of relationships to Pleiades places.

Overview

This is a package of data derived from the Pleiades gazetteer of ancient places. It is used for archival and redistribution purposes and is likely to be less up-to-date than the live data at https://pleiades.stoa.org.

Pleiades is a community-built gazetteer and graph of ancient places. It publishes authoritative information about ancient places and spaces, providing unique services for finding, displaying, and reusing that information under open license. It publishes not just for individual human users, but also for search engines and for the widening array of computational research and visualization tools that support humanities teaching and research.

Pleiades is a continuously published scholarly reference work for the 21st century. We embrace the new paradigm of citizen humanities, encouraging contributions from any knowledgeable person and doing so in a context of pervasive peer review. Pleiades welcomes your contribution, no matter how small, and we have a number of useful tasks suitable for volunteers of every interest.

Access and Archiving

The latest versions of this package can be had by fork or download from the main branch at https://github.com/isawnyu/pleiades-datasets. Numbered releases are created periodically at GitHub. These are archived at:

• zenodo.org using the DOI 10.5281/zenodo.1193921
• archive.nyu.edu using the Handle 2451/34305
• archive.org using the URI https://archive.org/details/pleiades.datasets-{version_number}

Credits

Pleiades is brought to you by:

• Our volunteer content contributors (see data/rdf/authors.ttl for complete list and associated identifiers or data).
• Pleiades has received significant, periodic support from the National Endowment for the Humanities since 2006. Grant numbers: HK-230973-15, PA-51873-06, PX-50003-08, and PW-50557-10. Any views, findings, conclusions, or recommendations expressed in this publication do not necessarily reflect those of the National Endowment for the Humanities.
• Web hosting and additional support has been provided since 2008 by the Institute for the Study of the Ancient World at New York University.
• Additional support has been provided since 2000 by the Ancient World Mapping Center at the University of North Carolina at Chapel Hill.
• Development hosting and other project incubation support was provided between 2000 and 2008 by Ross Scaife and the Stoa Consortium.

Summary:

The files contained herein represent green roof footprints in NYC visible in 2016 high-resolution orthoimagery of NYC (described at https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_AerialImagery.md). Previously documented green roofs were aggregated in 2016 from multiple data sources including from NYC Department of Parks and Recreation and the NYC Department of Environmental Protection, greenroofs.com, and greenhomenyc.org. Footprints of the green roof surfaces were manually digitized based on the 2016 imagery, and a sample of other roof types were digitized to create a set of training data for classification of the imagery. A Mahalanobis distance classifier was employed in Google Earth Engine, and results were manually corrected, removing non-green roofs that were classified and adjusting shape/outlines of the classified green roofs to remove significant errors based on visual inspection with imagery across multiple time points. Ultimately, these initial data represent an estimate of where green roofs existed as of the imagery used, in 2016.

These data are associated with an existing GitHub Repository, https://github.com/tnc-ny-science/NYC_GreenRoofMapping, and as needed and appropriate pending future work, versioned updates will be released here.

Terms of Use:

The Nature Conservancy and co-authors of this work shall not be held liable for improper or incorrect use of the data described and/or contained herein. Any sale, distribution, loan, or offering for use of these digital data, in whole or in part, is prohibited without the approval of The Nature Conservancy and co-authors. The use of these data to produce other GIS products and services with the intent to sell for a profit is prohibited without the written consent of The Nature Conservancy and co-authors. All parties receiving these data must be informed of these restrictions. Authors of this work shall be acknowledged as data contributors to any reports or other products derived from these data.

Associated Files:

As of this release, the specific files included here are:

• GreenRoofData2016_20180917.geojson is in the human-readable, GeoJSON format, in geographic coordinates (Lat/Long, WGS84; EPSG 4263).
• GreenRoofData2016_20180917.gpkg is in the GeoPackage format, which is an Open Standard readable by most GIS software including Esri products (tested on ArcMap 10.3.1 and multiple versions of QGIS). This dataset is in the New York State Plan Coordinate System (units in feet) for the Long Island Zone, North American Datum 1983, EPSG 2263.
• GreenRoofData2016_20180917_Shapefile.zip is a zipped folder containing a Shapefile and associated files. Please note that some field names were truncated due to limitations of Shapefiles, but columns are in the same order as for other files and in the same order as listed below. This dataset is in the New York State Plan Coordinate System (units in feet) for the Long Island Zone, North American Datum 1983, EPSG 2263.
• GreenRoofData2016_20180917.csv is a comma-separated values file (CSV) with coordinates for centroids for the green roofs stored in the table itself. This allows for easily opening the data in a tool like spreadsheet software (e.g., Microsoft Excel) or a text editor.

Column Information for the datasets:

Some, but not all fields were joined to the green roof footprint data based on building footprint and tax lot data; those datasets are embedded as hyperlinks below.

• fid - Unique identifier
• bin - NYC Building ID Number based on overlap between green roof areas and a building footprint dataset for NYC from August, 2017. (Newer building footprint datasets do not have linkages to the tax lot identifier (bbl), thus this older dataset was used). The most current building footprint dataset should be available at: https://data.cityofnewyork.us/Housing-Development/Building-Footprints/nqwf-w8eh. Associated metadata for fields from that dataset are available at https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_BuildingFootprints.md.
• bbl - Boro Block and Lot number as a single string. This field is a tax lot identifier for NYC, which can be tied to the Digital Tax Map (http://gis.nyc.gov/taxmap/map.htm) and PLUTO/MapPLUTO (https://www1.nyc.gov/site/planning/data-maps/open-data/dwn-pluto-mappluto.page). Metadata for fields pulled from PLUTO/MapPLUTO can be found in the PLUTO Data Dictionary found on the aforementioned page. All joins to this bbl were based on MapPLUTO version 18v1.
• gr_area - Total area of the footprint of the green roof as per this data layer, in square feet, calculated using the projected coordinate system (EPSG 2263).
• bldg_area - Total area of the footprint of the associated building, in square feet, calculated using the projected coordinate system (EPSG 2263).
• prop_gr - Proportion of the building covered by green roof according to this layer (gr_area/bldg_area).
• cnstrct_yr - Year the building was constructed, pulled from the Building Footprint data.
• doitt_id - An identifier for the building assigned by the NYC Dept. of Information Technology and Telecommunications, pulled from the Building Footprint Data.
• heightroof - Height of the roof of the associated building, pulled from the Building Footprint Data.
• feat_code - Code describing the type of building, pulled from the Building Footprint Data.
• groundelev - Lowest elevation at the building level, pulled from the Building Footprint Data.
• qa - Flag indicating a positive QA/QC check (using multiple types of imagery); all data in this dataset should have 'Good'
• notes - Any notes about the green roof taken during visual inspection of imagery; for example, it was noted if the green roof appeared to be missing in newer imagery, or if there were parts of the roof for which it was unclear whether there was green roof area or potted plants.
• classified - Flag indicating whether the green roof was detected image classification. (1 for yes, 0 for no)
• digitized - Flag indicating whether the green roof was digitized prior to image classification and used as training data. (1 for yes, 0 for no)
• newlyadded - Flag indicating whether the green roof was detected solely by visual inspection after the image classification and added. (1 for yes, 0 for no)
• original_source - Indication of what the original data source was, whether a specific website, agency such as NYC Dept. of Parks and Recreation (DPR), or NYC Dept. of Environmental Protection (DEP). Multiple sources are separated by a slash.
• address - Address based on MapPLUTO, joined to the dataset based on bbl.
• borough - Borough abbreviation pulled from MapPLUTO.
• ownertype - Owner type field pulled from MapPLUTO.
• zonedist1 - Zoning District 1 type pulled from MapPLUTO.
• spdist1 - Special District 1 pulled from MapPLUTO.
• bbl_fixed - Flag to indicate whether bbl was manually fixed. Since tax lot data may have changed slightly since the release of the building footprint data used in this work, a small percentage of bbl codes had to be manually updated based on overlay between the green roof footprint and the MapPLUTO data, when no join was feasible based on the bbl code from the building footprint data. (1 for yes, 0 for no)

For GreenRoofData2016_20180917.csv there are two additional columns, representing the coordinates of centroids in geographic coordinates (Lat/Long, WGS84; EPSG 4263):

• xcoord - Longitude in decimal degrees.
• ycoord - Latitude in decimal degrees.

Acknowledgements:

This work was primarily supported through funding from the J.M. Kaplan Fund, awarded to the New York City Program of The Nature Conservancy, with additional support from the New York Community Trust, through New York City Audubon and the Green Roof Researchers Alliance.

The Digital Geologic-GIS Map of Wilson's Creek National Battlefield and Vicinity, Missouri is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (wicr_geology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (wicr_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (wicr_geology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) this file (wicr_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (wicr_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (wicr_geology_metadata_faq.pdf). Please read the wicr_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: Missouri Department of Natural Resources, Division of Geology and Land Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (wicr_geology_metadata.txt or wicr_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

How did the City create the Equity IndexWorking with Ohio State University's Kirwan Institute of Race and Social Justice, the City complied the Equity/Opportunity Index to help facilitate data-driven decision-making processes and enable leaders to distribute resources better and plan to fund programs and services, minimize inequities and maximize opportunities.The indicators displayed in the Equity/Opportunity Index have been shown to have a direct correlation to equity. For more information, please reference the additional document on the evidence-based research determinant categories. The data is measured granularly by census block group.The list below comprise the Indicators per index: Accessibility Parks & Open SpaceVoter ParticipationHealthy Food Access IndexAverage Road QualityHome Internet AccessTransit Options & AccessVehicle AccessLivabilityTacoma Crime IndexESRI Crime IndexCost-Burdened HouseholdsAverage Life ExpectancyUrban Tree CanopyTacoma Nuisance IndexMedian Home ValueEducationAverage Student Test RateAverage Student Mobility4-Year High School Graduation RatePercent of 25+-Year-Olds with Bachelor's Degree or MoreEconomyPierce County Jobs IndexMedian Household Income200% of the Poverty line or LessUnemployment RateEnvironmental HealthEnvironmental ExposuresNOx- Diesel Emissions (Annual Tons/Km2)Ozone ConcentrationPM2.5 ConcentrationPopulations Near Heavy Traffic RoadwaysToxic Releases from Facilities (RSEI Model)Environmental EffectsLead Risk from Housing (%)Proximity to Hazardous Waste Treatment Storage and Disposal Facilities (TSDFs)Proximity to National Priorities List Facilities (Superfund Sites)Proximity to Risk Management Plan (RMP) FacilitiesWastewater DischargeWhat does Very High or Very Low Equity/Opportunity mean?Very High Equity/Opportunity represents locations that have access to better opportunities to succeed and excel in life. The data indicators would include high-performing schools, a safe environment, access to adequate transportation, safe neighborhoods, and sustainable employment. In contrast, Low Equity/Opportunity areas have more obstacles and barriers within the area. These communities have limited access to institutional or societal investments with limits their quality of life.Why is the North and West End labeled Red?When looking at data related to equity and social justice, we want to be mindful not to reinforce historical representations of low-income or communities of color as bad or negative. To help visualize the areas of high opportunity and call out the need for more equity, we chose to use red. We flipped the gradient to highlight disparities within the community. Besides, we refrained from using green or positive colors with referring to dominant communities (white communities).Can I add more data and indicators to the Equity Index?Yes, by downloading the file and uploading it to ArcGIS, you can add data and indicators to the Index, and you can import the shapefiles into your database. The indicators and standard deviations are available on ArcGIS online.Can I see additional or multiple map layers?Within the left navigation panel, you can aggregate the index layers by determinate social categories; Accessibility, Education, Economy, Livability

The United States Geological Survey has published "An assessment of the representation of ecosystems in global protected areas using new maps of World Climate Regions and World Ecosystems" in Global Ecology and Conservation Journal. This work was produced by a team led by Roger Sayre, Ph.D., Senior Scientist for Ecosytems at the USGS Land Change Science Program with the support from The Nature Conservancy and Esri. We described this work using two introduction story maps, Introduction to World Ecosystems Map and Introduction to World Climate Regions Map. This story map is an introduction for World Climate Regions Map. You can have more information by accessing the published paper and you can access the dataset by downloading the pro package.

This layer visualizes over 60,000 commercial flight paths. The data was obtained from openflights.org, and was last updated in June 2014. The site states, "The third-party that OpenFlights uses for route data ceased providing updates in June 2014. The current data is of historical value only. As of June 2014, the OpenFlights/Airline Route Mapper Route Database contains 67,663 routes between 3,321 airports on 548 airlines spanning the globe. Creating and maintaining this database has required and continues to require an immense amount of work. We need your support to keep this database up-to-date."To donate, visit the site and click the PayPal link.Routes were created using the XY-to-line tool in ArcGIS Pro, inspired by Kenneth Field's work, and following a modified methodology from Michael Markieta (www.spatialanalysis.ca/2011/global-connectivity-mapping-out-flight-routes).Some cleanup was required in the original data, including adding missing location data for several airports and some missing IATA codes. Before performing the point to line conversion, the key to preserving attributes in the original data is a combination of the INDEX and MATCH functions in Microsoft Excel. Example function: =INDEX(Airlines!$B$2:$B$6200,MATCH(Routes!$A2,Airlines!$D$2:Airlines!$D$6200,0))                                                

Python is a free computer language that prioritizes readability for humans and general application. It is one of the easier computer languages to learn and start especially with no prior programming knowledge. I have been using Python for Excel spreadsheet automation, data analysis, and data visualization. It has allowed me to better focus on learning how to automate my data analysis workload. I am currently examining the North Carolina Department of Environmental Quality (NCDEQ) database for water quality sampling for the Town of Nags Head, NC. It spans over 26 years (1997-2023) and lists a total of currently 41 different testing site locations. You can see at the bottom of image 2 below that I have 148,204 testing data points for the entirety of the NCDEQ testing for the state. From this large dataset 34,759 data points are from Dare County (Nags Head) specifically with this subdivided into testing sites.

This dataset contains data on SC clients for each of the twenty-six Local Board (LB) areas in Ontario for the 2015/16 fiscal year, based on data provided to Local Boards and Local Employment Planning Councils (LEPC) in June 2016 (see below for details on Local Boards). These clients have been distributed across Local Board areas based on the client’s home address, not the address of their training institution(s).Different variables in this dataset cover different groups of Second Career clients, as follows:Demographic and skills training variables are composed of all SC clients that started in 2015/16.At exit outcome variables are composed of all SC clients that completed their program in 2015/16.12-month outcome variables are composed of all SC clients that completed a 12-month survey in 2015/16.The specific variables that fall into each of the above categories are detailed in the Technical Dictionary. As a result of these differences, not all variables in this dataset are comparable to the other variables in this dataset; for example, the outcomes at exit data is not the outcomes for the clients described by the demographic variables.Definitions for fields in this layer are available in the abbreviated Technical Dictionary.

Classifying trees from point cloud data is useful in applications such as high-quality 3D basemap creation, urban planning, and forestry workflows. Trees have a complex geometrical structure that is hard to capture using traditional means. Deep learning models are highly capable of learning these complex structures and giving superior results.Using the modelFollow the guide to use the model. The model can be used with the 3D Basemaps solution and ArcGIS Pro's Classify Point Cloud Using Trained Model tool. Before using this model, ensure that the supported deep learning frameworks libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.InputThe model accepts unclassified point clouds with the attributes: X, Y, Z, and Number of Returns.Note: This model is trained to work on unclassified point clouds that are in a projected coordinate system, where the units of X, Y, and Z are based on the metric system of measurement. If the dataset is in degrees or feet, it needs to be re-projected accordingly. The provided deep learning model was trained using a training dataset with the full set of points. Therefore, it is important to make the full set of points available to the neural network while predicting - allowing it to better discriminate points of 'class of interest' versus background points. It is recommended to use 'selective/target classification' and 'class preservation' functionalities during prediction to have better control over the classification.This model was trained on airborne lidar datasets and is expected to perform best with similar datasets. Classification of terrestrial point cloud datasets may work but has not been validated. For such cases, this pre-trained model may be fine-tuned to save on cost, time and compute resources while improving accuracy. When fine-tuning this model, the target training data characteristics such as class structure, maximum number of points per block, and extra attributes should match those of the data originally used for training this model (see Training data section below).OutputThe model will classify the point cloud into the following 2 classes with their meaning as defined by the American Society for Photogrammetry and Remote Sensing (ASPRS) described below: 0 Background 5 Trees / High-vegetationApplicable geographiesThis model is expected to work well in all regions globally, with an exception of mountainous regions. However, results can vary for datasets that are statistically dissimilar to training data.Model architectureThis model uses the PointCNN model architecture implemented in ArcGIS API for Python.Accuracy metricsThe table below summarizes the accuracy of the predictions on the validation dataset. Class Precision Recall F1-score Trees / High-vegetation (5) 0.975374 0.965929 0.970628Training dataThis model is trained on a subset of UK Environment Agency's open dataset. The training data used has the following characteristics: X, Y and Z linear unit meter Z range -19.29 m to 314.23 m Number of Returns 1 to 5 Intensity 1 to 4092 Point spacing 0.6 ± 0.3 Scan angle -23 to +23 Maximum points per block 8192 Extra attributes Number of Returns Class structure [0, 5]Sample resultsHere are a few results from the model.