Retirement Notice: This item is in mature support as of February 2023 and will be retired in December 2025. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version.This layer displays change in pixels of the Sentinel-2 10m Land Use/Land Cover product developed by Esri, Impact Observatory, and Microsoft. Available years to compare with 2021 are 2018, 2019 and 2020. By default, the layer shows all comparisons together, in effect showing what changed 2018-2021. But the layer may be changed to show one of three specific pairs of years, 2018-2021, 2019-2021, or 2020-2021.Showing just one pair of years in ArcGIS Online Map Viewer To show just one pair of years in ArcGIS Online Map viewer, create a filter. 1. Click the filter button. 2. Next, click add expression. 3. In the expression dialogue, specify a pair of years with the ProductName attribute. Use the following example in your expression dialogue to show only places that changed between 2020 and 2021:ProductNameis2020-2021 By default, places that do not change appear as a transparent symbol in ArcGIS Pro. But in ArcGIS Online Map Viewer, a transparent symbol may need to be set for these places after a filter is chosen. To do this: 4. Click the styles button.5. Under unique values click style options. 6. Click the symbol next to No Change at the bottom of the legend. 7. Click the slider next to "enable fill" to turn the symbol off. Showing just one pair of years in ArcGIS Pro To show just one pair of years in ArcGIS Pro, choose one of the layer's processing templates to single out a particular pair of years. The processing template applies a definition query that works in ArcGIS Pro. 1. To choose a processing template, right click the layer in the table of contents for ArcGIS Pro and choose properties. 2. In the dialogue that comes up, choose the tab that says processing templates. 3. On the right where it says processing template, choose the pair of years you would like to display. The processing template will stay applied for any analysis you may want to perform as well. How the change layer was created, combining LULC classes from two yearsImpact Observatory, Esri, and Microsoft used artificial intelligence to classify the world in 10 Land Use/Land Cover (LULC) classes for the years 2017-2021. Mosaics serve the following sets of change rasters in a single global layer: Change between 2018 and 2021Change between 2019 and 2021Change between 2020 and 2021To make this change layer, Esri used an arithmetic operation combining the cells from a source year and 2021 to make a change index value. ((from year * 16) + to year) In the example of the change between 2020 and 2021, the from year (2020) was multiplied by 16, then added to the to year (2021). Then the combined number is served as an index in an 8 bit unsigned mosaic with an attribute table which describes what changed or did not change in that timeframe. Variable mapped: Change in land cover between 2018, 2019, or 2020 and 2021 Data Projection: Universal Transverse Mercator (UTM)Mosaic Projection: WGS84Extent: GlobalSource imagery: Sentinel-2Cell Size: 10m (0.00008983152098239751 degrees)Type: ThematicSource: Esri Inc.Publication date: January 2022 What can you do with this layer?Global LULC maps provide information on conservation planning, food security, and hydrologic modeling, among other things. This dataset can be used to visualize land cover anywhere on Earth. This layer can also be used in analyses that require land cover input. For example, the Zonal Statistics tools allow a user to understand the composition of a specified area by reporting the total estimates for each of the classes. Land Cover processingThis map was produced by a deep learning model trained using over 5 billion hand-labeled Sentinel-2 pixels, sampled from over 20,000 sites distributed across all major biomes of the world. The underlying deep learning model uses 6 bands of Sentinel-2 surface reflectance data: visible blue, green, red, near infrared, and two shortwave infrared bands. To create the final map, the model is run on multiple dates of imagery throughout the year, and the outputs are composited into a final representative map. Processing platformSentinel-2 L2A/B data was accessed via Microsoft’s Planetary Computer and scaled using Microsoft Azure Batch. Class definitions1. WaterAreas where water was predominantly present throughout the year; may not cover areas with sporadic or ephemeral water; contains little to no sparse vegetation, no rock outcrop nor built up features like docks; examples: rivers, ponds, lakes, oceans, flooded salt plains.2. TreesAny significant clustering of tall (~15-m or higher) dense vegetation, typically with a closed or dense canopy; examples: wooded vegetation, clusters of dense tall vegetation within savannas, plantations, swamp or mangroves (dense/tall vegetation with ephemeral water or canopy too thick to detect water underneath).4. Flooded vegetationAreas of any type of vegetation with obvious intermixing of water throughout a majority of the year; seasonally flooded area that is a mix of grass/shrub/trees/bare ground; examples: flooded mangroves, emergent vegetation, rice paddies and other heavily irrigated and inundated agriculture.5. CropsHuman planted/plotted cereals, grasses, and crops not at tree height; examples: corn, wheat, soy, fallow plots of structured land.7. Built AreaHuman made structures; major road and rail networks; large homogenous impervious surfaces including parking structures, office buildings and residential housing; examples: houses, dense villages / towns / cities, paved roads, asphalt.8. Bare groundAreas of rock or soil with very sparse to no vegetation for the entire year; large areas of sand and deserts with no to little vegetation; examples: exposed rock or soil, desert and sand dunes, dry salt flats/pans, dried lake beds, mines.9. Snow/IceLarge homogenous areas of permanent snow or ice, typically only in mountain areas or highest latitudes; examples: glaciers, permanent snowpack, snow fields. 10. CloudsNo land cover information due to persistent cloud cover.11. Rangeland Open areas covered in homogenous grasses with little to no taller vegetation; wild cereals and grasses with no obvious human plotting (i.e., not a plotted field); examples: natural meadows and fields with sparse to no tree cover, open savanna with few to no trees, parks/golf courses/lawns, pastures. Mix of small clusters of plants or single plants dispersed on a landscape that shows exposed soil or rock; scrub-filled clearings within dense forests that are clearly not taller than trees; examples: moderate to sparse cover of bushes, shrubs and tufts of grass, savannas with very sparse grasses, trees or other plants.CitationKarra, Kontgis, et al. “Global land use/land cover with Sentinel-2 and deep learning.” IGARSS 2021-2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021.AcknowledgementsTraining data for this project makes use of the National Geographic Society Dynamic World training dataset, produced for the Dynamic World Project by National Geographic Society in partnership with Google and the World Resources Institute.For questions please email environment@esri.com

This layer displays change in pixels of the Sentinel-2 10m Land Use/Land Cover product developed by Esri, Impact Observatory, and Microsoft. Available years to compare with 2021 are 2018, 2019 and 2020.By default, the layer shows all comparisons together, in effect showing what changed 2018-2021. But the layer may be changed to show one of three specific pairs of years, 2018-2021, 2019-2021, or 2020-2021.Showing just one pair of years in ArcGIS Online Map ViewerTo show just one pair of years in ArcGIS Online Map viewer, create a filter.1. Click the filter button.2. Next, click add expression.3. In the expression dialogue, specify a pair of years with the ProductName attribute. Use the following example in your expression dialogue to show only places that changed between 2020 and 2021:ProductNameis2020-2021By default, places that do not change appear as a transparent symbol in ArcGIS Pro. But in ArcGIS Online Map Viewer, a transparent symbol may need to be set for these places after a filter is chosen. To do this:4. Click the styles button.5. Under unique values click style options.6. Click the symbol next to No Change at the bottom of the legend.7. Click the slider next to "enable fill" to turn the symbol off.Showing just one pair of years in ArcGIS ProTo show just one pair of years in ArcGIS Pro, choose one of the layer's processing templates to single out a particular pair of years. The processing template applies a definition query that works in ArcGIS Pro.1. To choose a processing template, right click the layer in the table of contents for ArcGIS Pro and choose properties.2. In the dialogue that comes up, choose the tab that says processing templates.3. On the right where it says processing template, choose the pair of years you would like to display.The processing template will stay applied for any analysis you may want to perform as well.How the change layer was created, combining LULC classes from two yearsImpact Observatory, Esri, and Microsoft used artificial intelligence to classify the world in 10 Land Use/Land Cover (LULC) classes for the years 2017-2021. Mosaics serve the following sets of change rasters in a single global layer:Change between 2018 and 2021Change between 2019 and 2021Change between 2020 and 2021To make this change layer, Esri used an arithmetic operation combining the cells from a source year and 2021 to make a change index value. ((from year * 16) + to year) In the example of the change between 2020 and 2021, the from year (2020) was multiplied by 16, then added to the to year (2021). Then the combined number is served as an index in an 8 bit unsigned mosaic with an attribute table which describes what changed or did not change in that timeframe.Variable mapped: Change in land cover between 2018, 2019, or 2020 and 2021Data Projection: Universal Transverse Mercator (UTM)Mosaic Projection: WGS84Extent: GlobalSource imagery: Sentinel-2Cell Size: 10m (0.00008983152098239751 degrees)Type: ThematicSource: Esri Inc.Publication date: January 2022What can you do with this layer?Global LULC maps provide information on conservation planning, food security, and hydrologic modeling, among other things. This dataset can be used to visualize land cover anywhere on Earth. This layer can also be used in analyses that require land cover input. For example, the Zonal Statistics tools allow a user to understand the composition of a specified area by reporting the total estimates for each of the classes.Land Cover processingThis map was produced by a deep learning model trained using over 5 billion hand-labeled Sentinel-2 pixels, sampled from over 20,000 sites distributed across all major biomes of the world. The underlying deep learning model uses 6 bands of Sentinel-2 surface reflectance data: visible blue, green, red, near infrared, and two shortwave infrared bands. To create the final map, the model is run on multiple dates of imagery throughout the year, and the outputs are composited into a final representative map.Processing platformSentinel-2 L2A/B data was accessed via Microsoft’s Planetary Computer and scaled using Microsoft Azure Batch.Class definitions1. WaterAreas where water was predominantly present throughout the year; may not cover areas with sporadic or ephemeral water; contains little to no sparse vegetation, no rock outcrop nor built up features like docks; examples: rivers, ponds, lakes, oceans, flooded salt plains.2. TreesAny significant clustering of tall (~15-m or higher) dense vegetation, typically with a closed or dense canopy; examples: wooded vegetation, clusters of dense tall vegetation within savannas, plantations, swamp or mangroves (dense/tall vegetation with ephemeral water or canopy too thick to detect water underneath).4. Flooded vegetationAreas of any type of vegetation with obvious intermixing of water throughout a majority of the year; seasonally flooded area that is a mix of grass/shrub/trees/bare ground; examples: flooded mangroves, emergent vegetation, rice paddies and other heavily irrigated and inundated agriculture.5. CropsHuman planted/plotted cereals, grasses, and crops not at tree height; examples: corn, wheat, soy, fallow plots of structured land.7. Built AreaHuman made structures; major road and rail networks; large homogenous impervious surfaces including parking structures, office buildings and residential housing; examples: houses, dense villages / towns / cities, paved roads, asphalt.8. Bare groundAreas of rock or soil with very sparse to no vegetation for the entire year; large areas of sand and deserts with no to little vegetation; examples: exposed rock or soil, desert and sand dunes, dry salt flats/pans, dried lake beds, mines.9. Snow/IceLarge homogenous areas of permanent snow or ice, typically only in mountain areas or highest latitudes; examples: glaciers, permanent snowpack, snow fields. 10. CloudsNo land cover information due to persistent cloud cover.11. RangelandOpen areas covered in homogenous grasses with little to no taller vegetation; wild cereals and grasses with no obvious human plotting (i.e., not a plotted field); examples: natural meadows and fields with sparse to no tree cover, open savanna with few to no trees, parks/golf courses/lawns, pastures. Mix of small clusters of plants or single plants dispersed on a landscape that shows exposed soil or rock; scrub-filled clearings within dense forests that are clearly not taller than trees; examples: moderate to sparse cover of bushes, shrubs and tufts of grass, savannas with very sparse grasses, trees or other plants.CitationKarra, Kontgis, et al. “Global land use/land cover with Sentinel-2 and deep learning.” IGARSS 2021-2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021.AcknowledgementsTraining data for this project makes use of the National Geographic Society Dynamic World training dataset, produced for the Dynamic World Project by National Geographic Society in partnership with Google and the World Resources Institute.For questions please email environment@esri.com

This dataset contains the planned and existing Hennepin County on and off-street bikeway system as defined by the 2040 Bicycle Transportation Plan. The current Hennepin County and Three rivers Park District bikeway system includes 594 miles of off-street bikeways and 285 miles of on-street bikeways. This dataset includes regional, municipal, state and county bikeways part of the Hennepin County bicycle network (as shown in the Bike Plan) but does not represent all on- and off-street bikeways in Hennepin County. Additionally, this dataset includes pedestrian facilities, and sidewalks located along county roadways.

Link to attribute table information: https://gis.hennepin.us/OpenData/Metadata/Bikeways.pdfQuestions or inquiries about this dataset should be directed to Hennepin County Bicycle and Pedestrian Coordinator

Filtering the dataset:Many facilities accommodate shared bicycle and pedestrian uses. Use the expressions below to filter the system based on facility type.

To filter the dataset so it shows the entire county bikeway system, use the following expression:FACILITY = ‘Bike’ OR FACILITY = 'Shared'To filter the dataset so it shows the entire county pedestrian system, use the following expression:FACILITY = ‘Sidewalk’ OR FACILITY = 'Shared'To filter the dataset so it shows bike-only facilities, use the following expression:FACILITY = ‘Bike’To filter the dataset so it shows sidewalks or other pedestrian-only facilities, use the following expression:FACILITY = ‘Sidewalk'

ArcGIS Pro/QGIS to modify layers R for scripts

An accident near an elementary school in your city has drawn your attention to the topic of pedestrian and bicycle safety. You want to suggest policy actions to your city's local government that will reduce the likelihood of future accidents.

In this lesson, you'll map accident data regarding pedestrians and cyclists struck by vehicles. Then, you'll determine the number of accidents that occurred within each school zone and identify the five most dangerous zones. You'll present your findings with a story map that provides narrative context and helps users understand your position. This lesson is targeted toward city or county employees or any civic-minded individual who wants to make a difference in their community.

In this lesson you will build skills in the these areas:

• Adding data from the Living Atlas- Symbolizing data with Arcade expressions- Configuring pop-ups- Summarizing data within an area- Sharing maps in a Story Map

Learn ArcGIS is a hands-on, problem-based learning website using real-world scenarios. Our mission is to encourage critical thinking, and to develop resources that support STEM education.

This map shows the solar potential of rooftops in Bristol, England. The values are recorded in kWh/yr, and represent PV generation potential. The map uses an Arcade expression to color each polygon.Original data: The data comes from the ArcGIS Online Open Data site and the data/service comes from this associated ArcGIS Online item. Layer updated to this service on 1/9/2018.

B.1 Buildings InventoryThe Building Footprints data layer is an inventory of buildings in Southeast Michigan representing both the shape of the building and attributes related to the location, size, and use of the structure. The layer was first developed in 2010 using heads-up digitizing to trace the outlines of buildings from 2010 one foot resolution aerial photography. This process was later repeated using six inch resolution imagery in 2015, 2020, and 2024 to add recently constructed buildings to the inventory. Due to differences in spatial accuracy between the 2010 imagery and later imagery sources, footprint polygons delineated in 2010 may appear shifted compared with imagery that is more recent.Building DefinitionFor the purposes of this data layer, a building is defined as a structure containing one or more housing units AND/OR at least 250 square feet of nonresidential job space. Detached garages, pole barns, utility sheds, and most structures on agricultural or recreational land uses are therefore not considered buildings as they do not contain housing units or dedicated nonresidential job space.How Current is the Buildings Footprints LayerThe building footprints data layer is current as of December 31, 2024. This date was chose to align with the timing of the 2020 Decennial Census, so that accurate comparisons of housing unit change can be made to evaluate the quality of Census data.Temporal AspectsThe building footprints data layer is designed to be temporal in nature, so that an accurate inventory of buildings at any point in time since the origination of the layer in April 2010 can be visualized. To facilitate this, when existing buildings are demolished the demolition date is recorded but they are not removed from the inventory. To view only current buildings, you must filter the data layer using the expression, WHERE DEMOLISHED IS NULL.B.2 Building Footprints AttributesTable B-1 list the current attributes of the building footprints data layer. Additional information about certain fields follows the attribute list.Table B-1 Building Footprints AttributesFIELDTYPEDESCRIPTIONBUILDING_IDLong IntegerUnique identification number assigned to each building.PARCEL_IDLong IntegerIdentification number of the parcel on which the building is located.APNVarchar(24)Tax assessing parcel number of the parcel on which the building is located.CITY_IDIntegerSEMCOG identification number of the municipality, or for Detroit, masterplan neighborhood, in which the building is located.BUILD_TYPEIntegerBuilding type. Please see section B.3 for a detailed description of the types.RES_SQFTLong IntegerSquare footage devoted to residential use.NONRES_SQFTLong IntegerSquare footage devoted to nonresidential activity.YEAR_BUILTIntegerYear structure was built. A value of 0 indicates the year built is unknown.DEMOLISHEDDateDate structure was demolished.STORIESFloat(5.2)Number of stories. For single-family residential this number is expressed inquarter fractions from 1 to 3 stories: 1.00, 1.25, 1.50, etc.MEDIAN_HGTIntegerMedian height of the building from LiDAR surveys, NULL if unknown.HOUSING_UNITSIntegerNumber of residential housing units in the building.GQCAPIntegerMaximum number of group quarters residents, if any.SOURCEVarchar(10)Source of footprint polygon: NEARMAP, OAKLAND, SANBORN,SEMCOG or AUTOMATIC.ADDRESSVarchar(100)Street address of the building.ZIPCODEVarchar(5)USPS postal code for the building address.REF_NAMEVarchar(40)Owner or business name of the building, if known.CITY_IDPlease refer to the SEMCOG CITY_ID Code List for a list identifying the code for each municipality AND City of Detroit master plan neighborhood.RES_SQFT and NONRES_SQFTSquare footage evenly divisible by 100 is an estimate, based on size and/or type of building, where the true value is unknown.SOURCEFootprints from OAKLAND County are derived from 2016 EagleView imagery. Footprints from SEMCOG are edits of shapes from another source. AUTOMATIC footprints are those created by algorithm to represent mobile homes in manufactured housing parks.ADDRESSBuildings with addresses on multiple streets will have each street address separated by the “ | “ symbol within the field.B.3 Building TypesEach building footprint is assigned one of 26 building types to represent how the structure is currently being used. The overwhelming majority of buildings (86.9%) are single-family residential.Nonresidential TypesThe 22 nonresidential building types are based on the 2017 North American Industrial Classification System (NAICS), which classifies establishments based on economic activity. Each of the building types represent one or more 3-digit NAICS sector groupings, except for five special building types that do not fit well within this system. These special types (movie theaters, libraries, dormitories, funeral services, and parking garages) have land use AND/OR trip generation characteristics that differ substantially from other types of activity in their respective NAICS sector and are therefore classified individually. More information on NAICS, including descriptions of the economic activity sectors, can be found at the Census Bureau’s NAICS web site.Residential TypesThe four residential building types are based on a combination of units in structure, and ownership.Single-Family Housing are homes and site condominiums that contain one housing unit. Most are owned individually, but there are instances of groups of homes owned by a housing commission for rent. Traditional duplexes where each unit is owned by the same entity are also classified as single-family housing.Attached Condo Housing are buildings which contain two or more housing units, each of which shares one or more walls with an adjoining unit and can be independently owned.Multi-Family Apartment buildings contain three or more units, all of which are owned by a single entity and intended as rental units.Mobile Homes are pre-manufactured housing located primarily in manufactured housing parks.Building Type AssignmentsBuildings are classified primarily on the economic sector of the property owner. Therefore, each building on a site will typically have the same building type assigned. For example, all buildings at an automotive plant will be classified as Manufacturing, even though some small buildings might be typically used as storage. Government owned utility buildings will usually be classified as Utility buildings rather than Governmental. While not ideal for every purpose, it would not be feasible to attempt to uniquely classify nearly two million individual structures, and this method corresponds better to the use of buildings in developing parcel based land use. A description of SEMCOG building types, including the relevant NAICS codes for the type, are found in Table B-2. The RGB color values match SEMCOG’s layer symbology for building types.Table B-2 Building TypesBUILD TYPEDESCRIPTIONNAICS SECTORSRGB11Educational611190-232-25513Religious and Civic8130-132-16814Governmental921-9280-76-11521Retail Building441-454, 811-812255-190-19023Office Building511-551, 561255-0-031Manufacturing311-339194-158-21532Wholesale Trade423-425170-102-20533Warehouse and Storage493112-68-13741Transportation and Utility221, 481-488, 562204-204-20442Delivery Services491-492156-156-15651Health and Social Services621, 624214-157-18852Hospital622245-122-18253Residential Care Facility623137-68-10161Leisure Activity711-713137-205-10263Hotel and Motel721209-255-11565Eating and Drinking72285-255-071Agricultural111-213255-255-081Single-Family HousingResidential255-211-12782Attached Condo BuildingResidential230-152-083Multi-Family ApartmentResidential137-112-6884Mobile HomeResidential137-68-6891Movie Theater51213168-0-092Library519120-197-25593Dormitory Quarters7213192-137-6894Funeral Services81220255-127-12795Parking Garage8129378-78-78

This layer shows the countries of Africa. You can click on the map to get info on each country, including its name and flag, as well as links to detailed information in The World Factbook and UN Human Development Reports.The Africa Countries layer was created by joining country population data from The World Factbook to the World Countries (Generalized) layer, using ArcGIS Online analysis tools. The popup for the map uses Arcade expressions to reference other online resources based on the country code for the selected country.The Flags of countries are provided by reference to Flagpedia, which provides flags of countries of the world and the U.S. states for display and download.