Document outlining Open Spatial Data Sources in New Zealand with instructions on how to add them into ArcGIS Online for use in the NZ school classroom. This document has been specially written to assist teachers who are creating their own spatial analysis lessons. Please ensure that you peruse the use constraints applied to the individual items of spatial data before utilising them in the classroom.

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.

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.

Background

Preventive and health-promoting policies can guide (place and space-specific) factors influencing human health, such as the physical and social environment. Required is data that can lead to a more nuanced decision-making process and identify both, existing and future challenges. Along with the rise of new technologies, and thus the multiple opportunities to use and process data, new options have emerged to measure and monitor factors that affect health. Thus, in recent years, several gateways for open data (including governmental and geospatial data) became available. At present, an increasing number of research institutions as well as (state and private) companies and citizens' initiatives provide data. However, there is a lack of overviews covering the range of such offerings regarding health. In particular, for geographically differentiated analyses, there are challenges related to data availability at different spatial levels and the growing number of data providers. ...

This is a connection to the Chester County GIS Open Data portal. Chester County incorporates the use of Geographic Information Systems (GIS) in several departments and agencies that use geographic data in their key business functions. Geographic Information Systems integrate spatial data (maps) and tabular data (databases) through computer technology.

Contact Chester County GIS Phone: 610-344-6096 Email: gis@chesco.org

As part of our ongoing commitment to promote transparency, accountability, innovation and citizen engagement, Montgomery County has designed this data site to provide information on county initiatives and as a place to access the County's official published GIS data and applications.

The dataset provides the usage statistics (covering both number of downloads and number of API requests) of open data (spatial data included) of the Open Data Portal per data provider in a specific time period.

This is a connection to Philadelphia's open data portal - OpenDataPhilly.org - built by Azavea, a Philadelphia-based geospatial software firm. OpenDataPhilly is based on the idea that providing free and easy access to data information encourages better and more transparent government and a more engaged and knowledgeable citizenry.

OpenDataPhilly is a catalog of open data about the Philadelphia region. It includes more than 300 data sets, applications and APIs from many organizations in the region, including from City government. A full list of datasets shared by Philadelphia’s municipal government can be found here: https://www.opendataphilly.org/organization/city-of-philadelphia

The website enables users to search for and locate data sets based on keyword and category searches. For each data set, application, or API, the website includes accompanying information about the origins, update frequency, and other specifics of the data. The record for each data source also includes links for downloading the data or accessing the application or API.

What do you think of OpenDataPhilly? Let us know your ideas, suggestions, questions, or how you’ve used data in useful and inspiring ways at info@opendataphilly.org.

Contact

If you want to know when City government releases new datasets, follow @PHLInnovation on twitter.

This is a connection to the City of Reading, Pennsylvania's Open Data Portal. Welcome to the City of Reading's open data platform, where public data sets are published for free use by the community to research, remix, and recreate.

This dataset contains processed and spatially structured open data for building stock modeling in Norway. It includes harmonized municipal statistics, building point data, energy use profiles, and archetype parameters prepared for urban building energy modeling (UBEM) workflows. The data has been curated to align with national standards and facilitate information for energy simulations at multiple spatial scales.

Colorado's open spatial data portal

The CT Geodata Portal is an open data site for all geospatial data in Connecticut. Users can find spatial datasets directly administered by the GIS Office as well as those shared by the Department of Transportation, the Department of Energy and Environmental Protection, CT ECO, and other partners.

Data record audit of feature classes within the MD iMAP spatial database.

Includes geodatabase with statewide address points, street centerlines, parcels, and county boundaries for Indiana, 2020. Also includes zipped shapefiles for individual counties, state geocoder, and real property geodatabase. See inventory file for full description of geodatabase layers, and metadata... file for more information. [more]

Data record audit fields in feature classes within the MD DoIT iMap spatial database.

This dataset is about books. It has 1 row and is filtered where the book is Learning GIS using open source software : an applied guide for geo-spatial analysis. It features 7 columns including author, publication date, language, and book publisher.

Includes geodatabase with statewide address points, street centerlines, parcels, and county boundaries for Indiana, 2023. Also includes zipped shapefiles for individual counties, state geocoder, and real property geodatabase.

The REE and Coal Open Database is an online collection of subsurface contextual data from publicly available geological, geochemical, and geospatial resources. These data align to and support execution of NETL’s REE coal assessment method. The database includes basin- and national-level spatial datasets, in addition to other non-spatial data that support the assessment approach. Data in this collection are sourced from a range of authoritative, public sources, including NETL, U.S. Geological Survey (USGS), Energy Information Administration (EIA), and state geological surveys.

Graffiti presents serious urban concerns, often signaling urban decay. This study uses open spatial data to analyze and model graffiti occurrences in terms of street network centrality measures. In particular, betweenness centrality, closeness centrality, and degree centrality are evaluated using San Francisco, California, as the case study area, with data from OpenStreetMap and reported graffiti from 2008 to 2023 from the San Francisco nonemergency municipal service (denoted as 311) as the data sets. The spatial error model was found to outperform both ordinary least squares tests and the spatial lag model. The model could further explain graffiti spatiality. Graffiti writers were observed to favor street segments that are close to the downtown and well-connected to other streets, often having high accessibility, visibility, and accommodating street furniture. The results indicate that bridges and highway segments that are difficult to stop and tag were typically avoided. In addition, for a given street, the model error in adjacent streets significantly (p 

Often times that hardest part about writing your own lesson in GIS is finding the appropriate spatial data. This video takes you through some of the sources of spatial data that you have in New Zealand. URLs for the data sources mentioned in the video are:Living Atlas of The Worldhttps://livingatlas.arcgis.comNZ Government Data Portalhttps://data.govt.nz/ LINZ Data Servicehttps://data.linz.govt.nz/Wellington City Council Open Data Portalhttps://data-wcc.opendata.arcgis.com/Koordinates https://koordinates.com/data/And some addition Open Data Sites for our main NZ CitiesAuckland Council Open Data Portalhttps://data-aucklandcouncil.opendata.arcgis.com/ Canterbury Maps Data Portal https://opendata.canterburymaps.govt.nz/Video Recorded April 2020.