The existing raster dataset corresponds to the year 1952, with data obtained from the UCSB Frame Finder Aerials, an online library collection database of aerial photography. The existing raster dataset contains two different flights, ABM-1952 and PAI-ABC, flown by Southwestern Aerial Surveys and Pacific Air Industries respectively, in order to provide a more comprehensive coverage of the city of Roseville. Some areas display apparent constrasts, such as plowed field vs. unplowed field, due to the fact that each flight was taken in different months in 1952. Both flights are displayed at a scale of 1:20:000The following photo frames were used to create the raster dataset: pai-abc_y8-144, pai-abc_y8-146, pai-abc_y8-140, pai-abc_y8-139, pai-abc_y8-141, pai-abc_y8-143, pai-abc_3k-28, pai-abc_3k-106, abm-1952_1k-68, amb-1952_1k-65, abm-1952_1k-28, abm-1952_1k-12, abm-1952_1k-67, abm-1952_1k-82, abm-1952_1k-80, abm-1952_1k-81, abm-1952_9k-84, abm-1952_9k-81.

Access the Data:

Access the REST Service from https://ags.roseville.ca.us/arcgis/rest/services/PublicServices/. View the data in our Historical Imagery Collection.Add data to ArcMap or ArcPro by clicking on “View Metadata” and selecting “Open in ArcGIS Desktop”.

The National Insect and Disease Risk map identifies areas with risk of significant tree mortality due to insects and plant diseases. The layer identifies lands in three classes: areas with risk of tree mortality from insects and disease between 2013 and 2027, areas with lower tree mortality risk, and areas that were formerly at risk but are no longer at risk due to disturbance (human or natural) between 2012 and 2018. Areas with risk of tree mortality are defined as places where at least 25% of standing live basal area greater than one inch in diameter will die over a 15-year time frame (2013 to 2027) due to insects and diseases.The National Insect and Disease Risk map, produced by the US Forest Service FHAAST, is part of a nationwide strategic assessment of potential hazard for tree mortality due to major forest insects and diseases. Dataset Summary Phenomenon Mapped: Risk of tree mortality due to insects and diseaseUnits: MetersCell Size: 30 meters in Hawaii and 240 meters in Alaska and the Contiguous USSource Type: DiscretePixel Type: 2-bit unsigned integerData Coordinate System: NAD 1983 Albers (Contiguous US), WGS 1984 Albers (Alaska), Hawaii Albers (Hawaii)Mosaic Projection: North America Albers Equal Area ConicExtent: Alaska, Hawaii, and the Contiguous United States Source: National Insect Disease Risk MapPublication Date: 2018ArcGIS Server URL: https://landscape11.arcgis.com/arcgis/This layer was created from the 2018 version of the National Insect Disease Risk Map.What can you do with this Layer? This layer is suitable for both visualization and analysis across the ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application.Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map:In ArcGIS Online you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "insects and disease" in the search box and browse to the layer. Select the layer then click Add to Map.In ArcGIS Pro open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "insects and disease" in the search box, browse to the layer then click OK.In ArcGIS Pro you can use raster functions to create your own custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro. For example, Zonal Statistics as Table tool can be used to summarize risk of tree mortality across several watersheds, counties, or other areas that you may be interested in such as areas near homes.In ArcGIS Online you can change then layer's symbology in the image display control, set the layer's transparency, and control the visible scale range.The ArcGIS Living Atlas of the World provides an easy way to explore many other beautiful and authoritative maps on hundreds of topics like this one.

This data set summarized biological and environmental sampling data from Reef Visual Census (RVC) surveys in southern Florida in conjunction with remote-sensed, high-resolution mapping data to take significant strides in moving from qualitative to quantitative habitat characterization of the RVC coral reef sampling frame. The data set contains two GIS shape files, one for the Dry Tortugas region and one for the Florida Keys, of survey sampling grids with habitat-depths quantitatively characterized to a 50 x 50 m resolution. Each sampling grid has region code, grid number, average depth (m), habitat code, zone code indicating onshore-offshore, MPA-code indicating whether inside or outside a protected area, depth strata code, rugosity strata code, fish strata code, and coral strata code. There is a dictionary file which describes the details of each habitat code categories. The refined sampling grid will have significant improvements to the accuracy, precision, and cost-effectiveness of RVC surveys in the Florida Keys and Tortugas regions. The study findings suggest some clear mapping priorities for fully characterizing the RVC sampling grid for the Florida Keys and Tortugas regions.

Context

I'm trying to make a Choropleth map over time of home sale prices by block in Brooklyn for the last 15 years to visualize gentrification. I have the entire dataset for all 5 boroughs of New York, but am starting with Brooklyn.

Content and Acknowledgements

Primary dataset is the NYC Housing Sales Data Found in this Link: http://www1.nyc.gov/site/finance/taxes/property-rolling-sales-data.page

The data in all the separate excel spreadsheets for 2003-2017 was merged via VBA scripting in Excel and further cleaned & de-duped in R

Additionally, in my hunt for shapefiles I discovered these wonderful shapefiles from NYCPluto: https://www1.nyc.gov/site/planning/data-maps/open-data/dwn-pluto-mappluto.page

I left joined it by "Block" & "Lot" onto the primary data frame, but 25% of the block/lot combo's ended up not having a corresponding entry in the Pluto shapefile and are NAs.

Note that as in other uploaded datasets of NYC housing on Kaggle, many of these transactions have a sale_price of $0 or only a nominal amount far less than market value. These are likely property transfers to relatives and should be excluded from any analysis of market prices.

Inspiration

Can you model Brooklyn home prices accurately?

Data frame layers that incude the data and interpretations for the surficial geology of Lac Qui Parle County and went into the publication of the Lac Qui Parle County Geologic Atlas, Part A.This map depicts the natural surface materials found in Lac qui Parle County beneath the soil horizon and within a few meters of the land surface, not including anthropogenically modified material. These materials are predominantly Quaternary glacial sediment distinguished from each other by texture, lithology of the very coarse-grained (1-2 millimeter) sand fraction, and stratigraphic and landscape position. Many of these deposits are assigned to the lithostratigraphic units defined in Johnson and others (2016). Quaternary sediment is absent or thin in areas along the Minnesota River valley where Precambrian bedrock is exposed. Map unit delineation and landform distribution were based on 1-meter resolution lidar elevation imagery collected in 2010 (Fig. 1; available from the Minnesota Geospatial Commons https://gisdata.mn.gov). Samples and observations were collected from exposures of sediment including gravel pits and road cuts. Five rotary-sonic cores drilled to an average depth of 157 feet (48 meters) and 128 Giddings soil-auger borings drilled to a maximum depth of 15 feet (5 meters) were drilled in 2018 and 2020 for this project (see Plate 1, Database Map). A total of 519 samples collected from soil-auger borings and exposures were analyzed for this plate. Samples were analyzed for texture using the hydrometer method (Bouyoucos, 1962). All analytical results were digitized and compiled geospatially for mapping. Data from previous mapping in and adjacent to Lac qui Parle County (Patterson and others, 1999; see Index to Previous Mapping) were also used to assist in determination of map units. This was supplemented by well logs from the County Well Index (CWI) database, digitized soils maps and datasets (Natural Resources Conservation Service, 2014), aerial photographic imagery, drill cuttings sets from water wells at the Minnesota Geological Survey, and the Minnesota Geological Survey Quaternary Data Index (QDI), an internal working database that also includes soil and engineering test borings from the Minnesota Department of Transportation.

This dataset is used for status assessment, tidal habitat conservation, restoration, and planning for coastal Maine. This data represents low-lying areas of the non-tidal landscape adjacent to current tidal wetlands that could become marsh migration space as sea levels rise. Each marsh migration scenario represents the extent to which highest astronomical tide intersects undeveloped lands if sea level is increased by 1.2 feet. Predictions for the amount of sea level rise in the next 50-100 years vary, but the fact that sea level is rising is well documented (https://www.maine.gov/dacf/mgs/hazards/slr_ticker/index.html). Tidal marshes are ecologically and economically significant natural systems. Planning for their continued functional existence given various sea level rise scenarios is important for sustaining biodiversity and maintaining ecosystem services. Identifying these areas creates the opportunity for government agencies, municipalities, private conservation organizations, and land managers to plan for compatible uses of the lands and avoid impacts to future tidal marsh or buffers to that marsh space. These data can be paired with similarly created data that provides for scenarios with 0, 1.2, 1.6, 3.9, 6.1, 8.8, and 10.9 foot increases in sea level. Together, these datasets provide frames of reference for incremental increases of predicted sea level rise, to better serve planning purposes at different time frames.