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.

This seminar is an applied study of deep learning methods for extracting information from geospatial data, such as aerial imagery, multispectral imagery, digital terrain data, and other digital cartographic representations. We first provide an introduction and conceptualization of artificial neural networks (ANNs). Next, we explore appropriate loss and assessment metrics for different use cases followed by the tensor data model, which is central to applying deep learning methods. Convolutional neural networks (CNNs) are then conceptualized with scene classification use cases. Lastly, we explore semantic segmentation, object detection, and instance segmentation. The primary focus of this course is semantic segmenation for pixel-level classification. The associated GitHub repo provides a series of applied examples. We hope to continue to add examples as methods and technologies further develop. These examples make use of a vareity of datasets (e.g., SAT-6, topoDL, Inria, LandCover.ai, vfillDL, and wvlcDL). Please see the repo for links to the data and associated papers. All examples have associated videos that walk through the process, which are also linked to the repo. A variety of deep learning architectures are explored including UNet, UNet++, DeepLabv3+, and Mask R-CNN. Currenlty, two examples use ArcGIS Pro and require no coding. The remaining five examples require coding and make use of PyTorch, Python, and R within the RStudio IDE. It is assumed that you have prior knowledge of coding in the Python and R enviroinments. If you do not have experience coding, please take a look at our Open-Source GIScience and Open-Source Spatial Analytics (R) courses, which explore coding in Python and R, respectively. After completing this seminar you will be able to: explain how ANNs work including weights, bias, activation, and optimization. describe and explain different loss and assessment metrics and determine appropriate use cases. use the tensor data model to represent data as input for deep learning. explain how CNNs work including convolutional operations/layers, kernel size, stride, padding, max pooling, activation, and batch normalization. use PyTorch, Python, and R to prepare data, produce and assess scene classification models, and infer to new data. explain common semantic segmentation architectures and how these methods allow for pixel-level classification and how they are different from traditional CNNs. use PyTorch, Python, and R (or ArcGIS Pro) to prepare data, produce and assess semantic segmentation models, and infer to new data.

In this course, you will learn to work within the free and open-source R environment with a specific focus on working with and analyzing geospatial data. We will cover a wide variety of data and spatial data analytics topics, and you will learn how to code in R along the way. The Introduction module provides more background info about the course and course set up. This course is designed for someone with some prior GIS knowledge. For example, you should know the basics of working with maps, map projections, and vector and raster data. You should be able to perform common spatial analysis tasks and make map layouts. If you do not have a GIS background, we would recommend checking out the West Virginia View GIScience class. We do not assume that you have any prior experience with R or with coding. So, don't worry if you haven't developed these skill sets yet. That is a major goal in this course. Background material will be provided using code examples, videos, and presentations. We have provided assignments to offer hands-on learning opportunities. Data links for the lecture modules are provided within each module while data for the assignments are linked to the assignment buttons below. Please see the sequencing document for our suggested order in which to work through the material. After completing this course you will be able to: prepare, manipulate, query, and generally work with data in R. perform data summarization, comparisons, and statistical tests. create quality graphs, map layouts, and interactive web maps to visualize data and findings. present your research, methods, results, and code as web pages to foster reproducible research. work with spatial data in R. analyze vector and raster geospatial data to answer a question with a spatial component. make spatial models and predictions using regression and machine learning. code in the R language at an intermediate level.

eXtension Foundation, the University of New Hampshire, and Virginia Tech have developed a mapping and data exploration tool to assist Cooperative Extension staff and administrators in making strategic planning and programming decisions. The tool, called the National Extension Web-mapping Tool (or NEWT), is the key in efforts to make spatial data available within cooperative extension system. NEWT requires no GIS experience to use. NEWT provides access for CES staff and administrators to relevant spatial data at a variety of scales (national, state, county) in useful formats (maps, tables, graphs), all without the need for any experience or technical skills in Geographic Information System (GIS) software. By providing consistent access to relevant spatial data throughout the country in a format useful to CES staff and administrators, NEWT represents a significant advancement for the use of spatial technology in CES. Users of the site will be able to discover the data layers which are of most interest to them by making simple, guided choices about topics related to their work. Once the relevant data layers have been chosen, a mapping interface will allow the exploration of spatial relationships and the creation and export of maps. Extension areas to filter searches include 4-H Youth & Family, Agriculture, Business, Community, Food & Health, and Natural Resources. Users will also be able to explore data by viewing data tables and graphs. This Beta release is open for public use and feedback. Resources in this dataset:Resource Title: Website Pointer to NEWT National Extension Web-mapping Tool Beta. File Name: Web Page, url: https://www.mapasyst.org/newt/ The site leads the user through the process of selecting the data in which they would be most interested, then provides a variety of ways for the user to explore the data (maps, graphs, tables).

Hosted Feature ServiceThe heart of the Idaho Trails Experience web app is the geographic data served by the Idaho Recreation Trails collection of Hosted Feature Layers. The Hosted service can be updated more frequently and on-the-fly than the previous technology used to maintain Idaho Trails-- changes now appear on the App and through the Feature Service in real time. The newest web presentation technology under AGOL, Experience Builder, served by this dataset, will make possible several extended features in the Web App.Under the hood Linear routes, closure routes and areas, and boundary area Hosted Feature Layers can be directly consumed from the Service via REST and are the source of features displayed in the Idaho Trails Web App. In addition to view settings for attributes popups set in the Web Map, additional visibility option not managed directly in the Hosted Feature data or controllable in the Web Map are further processed in the Idaho Trails Experience App presentation.Underlying Classes in the Idaho Recreation Trails dataset: One single linear class "Idaho Routes" contains all road and trail features (57,000+ route segment features): Routes characterized as recreational in nature include: High-clearance (previously "Jeep" treated as a road type, now as a full-width "trail" type), Special Vehicle Designation (mostly OHVs >50"), OHVs 50" and under, and single-track motorized (each width class separated by open year-long and motorized-seasonal); E-Bike (Class noted in Narrative); and, non-motorized and non-mechanized trails.Routes where vehicles either must be highway-legal (i.e., OHVs prohibited, typically paved roads), or routes requiring Restricted plate for legal OHV travel (mostly JURISDICTION = County); combined from previously-separate Layers: Highway-legal, Automobile, Other Roads (each with subcategories for seasonal access restrictions). (Note: Different route types are no longer kept in separate layers as with the legacy Map Service dataset. Route symbology, and selectable visibility is filtered based on the value in the SYMBOL attribute from the above linear class within the Web Map and Experience App. If dynamically consuming the Feature Service (REST), provisions will need to be made to filter to select visibility by road and trail types based on the value in the attribute field SYMBOL.)"Points of Interest" (point type data) is comprised of a layer previously titled "Trailheads" and now includes the flexibility of other types of lat/lon point-based information such as links to external maps and "attractions" information such as site seeing destinations not previously included in IDPR's map presentation. "Emergency Route Closures" contains linear route Closures (overlays any route where a Closure Order applies in web map)"Area Restrictions" is added for areas such as defined by human exclusion Orders (polygon; usually planned annual human or vehicle exclusion areas, but can be emergency closure such as for wildfires as well)Multiple "Boundary" polygon classes contain boundary outlines and attributes information for IDPR Regions (3), Counties (44), Wildernesses (42), National Forests and Ranger Districts (39), and BLM District and Field Offices (12), and BLM land units (700+). These separate classes reduce the data footprint of the Routes data and are joined in App popups by geographic Intersection logic. Bonus Material:Added to the web app are several optional, dynamic layers via publicly-available REST services selectable for visibility:NIFC Current and Historic Fire primetersIdaho Department of Lands- Lands Available for Recreational Use (visible by-default)Idaho Department of Fish & Game Hunting Units boundaries and numbers BLM Surface Management Agency layer for all local, state, and federal agencies which manage public lands (accessible, and not) US Forest Service Motor Vehicle Use Map, National Dataset (mirrors local MVUM paper and GeoPDF maps, where data available, lags local data when changes are made)National Park Service (NPS) Parks and Monuments areas and boundariesNOHRSC Snow Depth Other REST Services to be added based on utility in researching recreational accessThis dataset is published for the use of the individuals who fund this Program. Organizations wishing to consume this Feature Service into their own application should inquire to IDPR to obtain a use agreement and schema information to aid in development.AGOL Experience App here: https://experience.arcgis.com/experience/97a42a2a73c944ba918042faf518c689 Inquire to maps@idpr.idaho.gov

In this asynchronous session, you will use some of the free GIS tools from the Teach With GIS website, created and maintained by the Esri UK education team. All of these tools are free to use and accessible as websites from laptops, tablets and mobile devices. We recommend that you view them on a laptop or tablet if possible, to give you plenty of screen space to see every detail. They do not require any logins or subscriptions. We want you to experience using modern, online GIS tools from the perspective of a student before you begin to create your own tools, maps, and lessons. We have chosen a range of tools that let you experience GIS as a tool to examine physical and human geography, and to compare and contrast over space and time.

Data to create the List of Contaminated or Potentially Contaminated Sites - Remediation Division is from historical program information or from new program applications and filings. More information regarding the generation of this list can be found at: https://portal.ct.gov/DEEP/Remediation--Site-Clean-Up/List-of-Contaminated-or-Potentially-Contaminated-Sites-in-Connecticut A seperate dataset is published for: List of Contaminated Sites or Potentially Contaminated - SASU Case Management System and provide a list of Leaking Underground Storage Tank Sites. The two database systems are maintained by different Divisions within the agency. There may be sites in both databases due to an overlap in responsibilities of the two Divisions. https://data.ct.gov/Environment-and-Natural-Resources/List-of-Contaminated-or-Potentially-Contaminated-S/77ya-7twa The data is updated when documents are received for responsible parties conducting site remediation. For more information regarding the individual remedial programs visit: https://portal.ct.gov/DEEP/Remediation--Site-Clean-Up/Remediation-Site-Clean-Up Those seeking additional information about information contained in this dataset may use the DEEP FOIA Process: https://portal.ct.gov/DEEP/About/FOIA-Requests Each Row represents a Remediation project (Property Transfer, Brownfield, Enforcement, Federal Remediation, State Remediation, Landfill Monitoring, RCRA Corrective Action, and Voluntary). Data to compile the list was gathered for each site from information provided to DEEP for requirements within each program. Sites may be in multiple Remediation programs and therefore may be listed more than once. Some sites have been fully cleaned up while others have limited information about the environmental conditions. The list includes only sites that been reported to DEEP or EPA. Additional information for site within the Hazard Notification program can be found at: https://portal.ct.gov/DEEP/Remediation--Site-Clean-Up/Significant-Environmental-Hazard-Program/List-of-Significant-Environmental-Hazards Significant Environmental Hazard Sites GIS Map: https://experience.arcgis.com/experience/9c100aa21fbe4ee180df9942d000f676 Details on columns which reference ELUR: Environmental Land Use Restriction (ELUR) or Notice and Use Limitation (NAUL) are used to minimize the risk of human exposure to pollutants and hazards to the environment by preventing specific uses or activities at a property or a portion of a property. Link to GIS map of ELUR and restriction type: https://ctdeep.maps.arcgis.com/apps/webappviewer/index.html?id=d37eccb2a5c3491d8f0d389a96d9a912 There may be errors in the data although we strive to minimize them. Examples of errors may include: misspelled or incomplete addresses and/or missing data.

A collection of citywide Geographic Information System (GIS) layers that show areas of potential flooding scenarios under varying sea level rise conditions. Please see the New York City Stormwater Resiliency Plan for more information about the methodology applied to develop the maps. Please direct questions or comments to StormwaterResiliency@cityhall.nyc.gov.

This collection contains the following NYC Stormwater Flood Maps:

• NYC Stormwater Flood Map - Extreme Flood (3.66 inches/hr) with 2080 Sea Level Rise
• NYC Stormwater Flood Map - Moderate Flood (2.13 inches/hr) with 2050 Sea Level Rise
• NYC Stormwater Flood Map - Moderate Flood (2.13 inches/hr) with Current Sea Levels
• NYC Stormwater Flood Map - Limited Flood (1.77 inches/hr) with Current Sea Levels

Source Data: http://nyc.gov/stormwater-map

OVERVIEWThis site is dedicated to raising the level of spatial and data literacy used in public policy. We invite you to explore curated content, training, best practices, and datasets that can provide a baseline for your research, analysis, and policy recommendations. Learn about emerging policy questions and how GIS can be used to help come up with solutions to those questions.EXPLOREGo to your area of interest and explore hundreds of maps about various topics such as social equity, economic opportunity, public safety, and more. Browse and view the maps, or collect them and share via a simple URL. Sharing a collection of maps is an easy way to use maps as a tool for understanding. Help policymakers and stakeholders use data as a driving factor for policy decisions in your area.ISSUESBrowse different categories to find data layers, maps, and tools. Use this set of content as a driving force for your GIS workflows related to policy. RESOURCESTo maximize your experience with the Policy Maps, we’ve assembled education, training, best practices, and industry perspectives that help raise your data literacy, provide you with models, and connect you with the work of your peers.

SUMMARYThe area (in hectares) of publicly accessible blue- and green-space per 1000 population within each Middle Layer Super Output Area (MSOA).This dataset was produced to identify how much green/blue space (areas with greenery and/or inland water) people have to opportunity to experience within each MSOA. This includes land that the public can directly access and land they are able to walk/cycle/etc. immediately adjacent to.The area of accessible green/blue space, as a percentage of the total area of the MSOA, is also given.ANALYSIS METHODOLOGYThe following were identified as ‘accessible’ blue and green spaces:A) CRoW Open Access LandB) Doorstep GreensC) Open Greenspace (features described as a ‘play space’, ‘playing field’ or ‘public park or garden’)D) Local Nature ReservesE) Millennium GreensF) National Nature ReservesG) ‘Green’ and ‘blue’ land types – inland water, tidal water, woodland, foreshore, countryside/fields – and Open Greenspace types not identified in Point C that are immediately adjacent to*:G1) Coastal Path RoutesG2) National Cycle Network (traffic-free routes only)G3) National Forest Estate recreation routesG4) National TrailsG5) Path networks within built up areas (OS MasterMap Highways Network Paths)G6) Public Rights of Way*Features G1-6 were buffered by 20 m. All land described in Point G that fell within those 20 m buffers was extracted. Of those areas, any land that was >3m away from features G1-6 in its entirety was assumed to have non-green/blue features between the public path/route/trail and it, and was therefore removed.Population statistics for each MSOA were combined with the statistics re. the area of accessible green/blue space, to calculate the area of accessible green-blue space per 1000 population.LIMITATIONS1. Access to beaches and the sea could not be factored into the analysis, and should be considered when interpreting the results for MSOAs on the coastline.2. This dataset highlights were there are opportunities for the public to experience green/blue space. It does not (and could not) determine the level of accessibility for users with differing levels of mobility.3. Public Right of Way (PRoW) data was not available for the whole of England. While some gaps in the data will have been partially filled in by the OS MasterMap Highways Network Paths dataset, due to overlap between the two, some gaps will still remain. As such, this dataset should be viewed in combination with the ‘Area of accessible green and blue space per 1000 population (England): Missing data’ dataset in ArcGIS Online or, if using the data in desktop GIS, the ‘NoProwData’ field should be consulted. The area of accessible green/blue space in those areas could be slightly under represented in this dataset. TO BE VIEWED IN COMBINATION WITH:Area of accessible green and blue space per 1000 population (England): Missing dataDATA SOURCESCoastal Path Routes; CRoW Act 2000 - Access Layer; Doorstep Greens: Local Nature Reserves; Millennium Greens; National Nature Reserves; National Trails: © Natural England copyright 2021. Contains Ordnance Survey data © Crown copyright and database right 2021. Contains public sector information licensed under the Open Government Licence v3.0. Available from the Natural England Open Data Geoportal.OS Open Greenspace; OS VectorMap® District: Contains Ordnance Survey data © Crown copyright and database right 2021. Contains public sector information licensed under the Open Government Licence v3.0.OS MasterMap Highways Network Paths: Contains Ordnance Survey data © Crown copyright and database right 2021. National Cycle Network © Sustrans 2021, licensed under the Open Government Licence v3.0.National Forest Estate Recreation Routes: © Forestry Commission 2016.Population data: Mid-2019 (June 30) Population Estimates for Middle Layer Super Output Areas in England and Wales. © Office for National Statistics licensed under the Open Government Licence v3.0. © Crown Copyright 2020.MSOA boundaries: © Office for National Statistics licensed under the Open Government Licence v3.0. Contains OS data © Crown copyright and database right 2021.Public Rights of Way: Copyright of various local authorities.COPYRIGHT NOTICEThe reproduction of this data must be accompanied by the following statement:© Ribble Rivers Trust 2021. Produced using data: © Natural England copyright 2021. Contains Ordnance Survey data © Crown copyright and database right 2021. Contains public sector information licensed under the Open Government Licence v3.0.; © Sustrans 2021, licensed under the Open Government Licence v3.0.; © Forestry Commission 2016.; © Office for National Statistics licensed under the Open Government Licence v3.0. © Crown Copyright 2020.CaBA HEALTH & WELLBEING EVIDENCE BASEThis dataset forms part of the wider CaBA Health and Wellbeing Evidence Base.

An interactive map used to determine the zoning and future land use designation of Brevard County properties. Cities have their own zoning and land use designations, and should be contacted directly for that information.

Table from the American Community Survey (ACS) B17004 of poverty status in the past 12 months of individuals by sex by work experience. These are multiple, nonoverlapping vintages of the 5-year ACS estimates of population and housing attributes starting in 2010 shown by the corresponding census tract vintage. Also includes the most recent release annually.King County, Washington census tracts with nonoverlapping vintages of the 5-year American Community Survey (ACS) estimates starting in 2010. Vintage identified in the "ACS Vintage" field.The census tract boundaries match the vintage of the ACS data (currently 2010 and 2020) so please note the geographic changes between the decades. Tracts have been coded as being within the City of Seattle as well as assigned to neighborhood groups called "Community Reporting Areas". These areas were created after the 2000 census to provide geographically consistent neighborhoods through time for reporting U.S. Census Bureau data. This is not an attempt to identify neighborhood boundaries as defined by neighborhoods themselves.Vintages: 2010, 2015, 2020, 2021, 2022, 2023ACS Table(s): B17004Data downloaded from: Census Bureau's Explore Census Data The United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2020 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

The NJDEP's Solar Siting Analysis Version 3.0 (SSA v3.0) builds upon the foundation of the 2017 Solar Siting Analysis Update, and provides a more robust and up-to-date representation of the Department's solar siting preference statewide. This new revision incorporated 20 unique datasets, each of which were individually scored before being combined in a quantitative analysis to generate a composite solar siting score for each 5 ft by 5 ft pixel in the State. This web mapping "experience" contains an overview of the NJDEP's Solar Siting Analysis v3.0, including the purpose, methodology, and analysis results, as well as an interactive web mapping application that provides users with access to the Solar Siting Analysis v3.0 composite solar siting score raster, as well as each of the 20 component data layers that went into the analysis and other tools and functions to assist users with:Identifying the most preferred lands for siting solar PV,Evaluating potential lands for their solar development based on their siting preference scores,Understanding how lands were scored based on a sum of each of the overlapping component layers at a given site.

An ArcGIS Experience Builder app used by GIS managers to plan, manage, and gain insights into GIS program effectiveness.

This feature dataset contains a snapshot of all King County parcels from September 2020, with all of the "additional relevant criteria" data used in Method 2 of the LCI opportunity area determination described below.There are two methods by which a property may qualify as being in an opportunity area:Method 1. Property meets all three of the following "specified criteria" in King County code 26.12.003.(a) Areas "located in a census tract in which the median household income is in the lowest one-third for median household income for census tracts in King County; (b) "located in a ZIP code in which hospitalization rates for asthma, diabetes, and heart disease are in the highest one-third for ZIP codes in King County; and (c) "are within the Urban Growth Boundary and do not have a publicly owned and accessible park or open space within one-quarter mile of a residence, or are outside the Urban Growth Boundary and do not have a publicly owned and accessible park or open space within two miles of a residence." (King County Code 26.12.003)Data results related to Method 1 are shown in the LCI Opportunity Areas dataset on the King County GIS Open Data site. In this dataset, the parcels where the "CriteriaAllYN" column is equal to "Y" also represents those parcels.Method 2. If a property does not qualify under Method #1, a project may qualify if: "the project proponent or proponents can demonstrate, and the advisory committee determines, that residents living in the area, or populations the project is intended to serve, disproportionately experience limited access to public open spaces and experience demonstrated hardships including, but not limited to, low income, poor health and social and environmental factors that reflect a lack of one or more conditions for a fair and just society as defined as "determinants of equity" in KCC 2.10.210." (King County Code 26.12.003)Conservation Futures (CFT) values the use of multiple sources of data and information to demonstrate that a property is in an opportunity area. Applicants are welcome to provide additional criteria and data sources not identified in this report to demonstrate that a property is in an opportunity area. These sources are provided in the document here: Understanding the Data Report.

This United States Environmental Protection Agency (US EPA) feature layer represents monitoring site data, updated hourly concentrations and Air Quality Index (AQI) values for the latest hour received from monitoring sites that report to AirNow.Map and forecast data are collected using federal reference or equivalent monitoring techniques or techniques approved by the state, local or tribal monitoring agencies. To maintain "real-time" maps, the data are displayed after the end of each hour. Although preliminary data quality assessments are performed, the data in AirNow are not fully verified and validated through the quality assurance procedures monitoring organizations used to officially submit and certify data on the EPA Air Quality System (AQS).This data sharing, and centralization creates a one-stop source for real-time and forecast air quality data. The benefits include quality control, national reporting consistency, access to automated mapping methods, and data distribution to the public and other data systems. The U.S. Environmental Protection Agency, National Oceanic and Atmospheric Administration, National Park Service, tribal, state, and local agencies developed the AirNow system to provide the public with easy access to national air quality information. State and local agencies report the Air Quality Index (AQI) for cities across the US and parts of Canada and Mexico. AirNow data are used only to report the AQI, not to formulate or support regulation, guidance or any other EPA decision or position.About the AQIThe Air Quality Index (AQI) is an index for reporting daily air quality. It tells you how clean or polluted your air is, and what associated health effects might be a concern for you. The AQI focuses on health effects you may experience within a few hours or days after breathing polluted air. EPA calculates the AQI for five major air pollutants regulated by the Clean Air Act: ground-level ozone, particle pollution (also known as particulate matter), carbon monoxide, sulfur dioxide, and nitrogen dioxide. For each of these pollutants, EPA has established national air quality standards to protect public health. Ground-level ozone and airborne particles (often referred to as "particulate matter") are the two pollutants that pose the greatest threat to human health in this country.A number of factors influence ozone formation, including emissions from cars, trucks, buses, power plants, and industries, along with weather conditions. Weather is especially favorable for ozone formation when it’s hot, dry and sunny, and winds are calm and light. Federal and state regulations, including regulations for power plants, vehicles and fuels, are helping reduce ozone pollution nationwide.Fine particle pollution (or "particulate matter") can be emitted directly from cars, trucks, buses, power plants and industries, along with wildfires and woodstoves. But it also forms from chemical reactions of other pollutants in the air. Particle pollution can be high at different times of year, depending on where you live. In some areas, for example, colder winters can lead to increased particle pollution emissions from woodstove use, and stagnant weather conditions with calm and light winds can trap PM2.5 pollution near emission sources. Federal and state rules are helping reduce fine particle pollution, including clean diesel rules for vehicles and fuels, and rules to reduce pollution from power plants, industries, locomotives, and marine vessels, among others.How Does the AQI Work?Think of the AQI as a yardstick that runs from 0 to 500. The higher the AQI value, the greater the level of air pollution and the greater the health concern. For example, an AQI value of 50 represents good air quality with little potential to affect public health, while an AQI value over 300 represents hazardous air quality.An AQI value of 100 generally corresponds to the national air quality standard for the pollutant, which is the level EPA has set to protect public health. AQI values below 100 are generally thought of as satisfactory. When AQI values are above 100, air quality is considered to be unhealthy-at first for certain sensitive groups of people, then for everyone as AQI values get higher.Understanding the AQIThe purpose of the AQI is to help you understand what local air quality means to your health. To make it easier to understand, the AQI is divided into six categories:Air Quality Index(AQI) ValuesLevels of Health ConcernColorsWhen the AQI is in this range:..air quality conditions are:...as symbolized by this color:0 to 50GoodGreen51 to 100ModerateYellow101 to 150Unhealthy for Sensitive GroupsOrange151 to 200UnhealthyRed201 to 300Very UnhealthyPurple301 to 500HazardousMaroonNote: Values above 500 are considered Beyond the AQI. Follow recommendations for the Hazardous category. Additional information on reducing exposure to extremely high levels of particle pollution is available here.Each category corresponds to a different level of health concern. The six levels of health concern and what they mean are:"Good" AQI is 0 to 50. Air quality is considered satisfactory, and air pollution poses little or no risk."Moderate" AQI is 51 to 100. Air quality is acceptable; however, for some pollutants there may be a moderate health concern for a very small number of people. For example, people who are unusually sensitive to ozone may experience respiratory symptoms."Unhealthy for Sensitive Groups" AQI is 101 to 150. Although general public is not likely to be affected at this AQI range, people with lung disease, older adults and children are at a greater risk from exposure to ozone, whereas persons with heart and lung disease, older adults and children are at greater risk from the presence of particles in the air."Unhealthy" AQI is 151 to 200. Everyone may begin to experience some adverse health effects, and members of the sensitive groups may experience more serious effects."Very Unhealthy" AQI is 201 to 300. This would trigger a health alert signifying that everyone may experience more serious health effects."Hazardous" AQI greater than 300. This would trigger a health warnings of emergency conditions. The entire population is more likely to be affected.AQI colorsEPA has assigned a specific color to each AQI category to make it easier for people to understand quickly whether air pollution is reaching unhealthy levels in their communities. For example, the color orange means that conditions are "unhealthy for sensitive groups," while red means that conditions may be "unhealthy for everyone," and so on.Air Quality Index Levels of Health ConcernNumericalValueMeaningGood0 to 50Air quality is considered satisfactory, and air pollution poses little or no risk.Moderate51 to 100Air quality is acceptable; however, for some pollutants there may be a moderate health concern for a very small number of people who are unusually sensitive to air pollution.Unhealthy for Sensitive Groups101 to 150Members of sensitive groups may experience health effects. The general public is not likely to be affected.Unhealthy151 to 200Everyone may begin to experience health effects; members of sensitive groups may experience more serious health effects.Very Unhealthy201 to 300Health alert: everyone may experience more serious health effects.Hazardous301 to 500Health warnings of emergency conditions. The entire population is more likely to be affected.Note: Values above 500 are considered Beyond the AQI. Follow recommendations for the "Hazardous category." Additional information on reducing exposure to extremely high levels of particle pollution is available here.

The Ocean and Coast Information System (OCIS) contains a wide variety of web GIS data layer to support mapping and analysis workflows. OCIS is an integrating portal, and while it does serve some novel datasets and information products, it is meant to collect the most important web GIS resources from across the internet and create a more simplified browsing and access experience. One stop instead of twenty. Many of the datasets found in OCIS come from federal sources, including NOAA, the Bureau for Ocean Energy Management (BOEM), and the US Coast Guard. But supporting a vibrant blue economy needs more than just federal data, so OCIS also integrates information from non-governmental sources. And we hope other NGOs will help contribute their information to OCIS.