The Early Development Instrument (EDI) is a community-based measure of young children’s developmental health and early learning intended to identify areas, both developmentally and geographically, in which children have the greatest need and require the most supports. It is a population measure that is based on developmental rather than curriculum benchmarks, and it assesses five general areas, or domains, of child development: physical health and well-being, social competence, emotional maturity, language and cognitive development, and communication skills and general knowledge). EDI data are collected through a questionnaire that senior kindergarten teachers complete for all children in their classrooms every three years. Three measures of children’s developmental health are included in the analysis of the York Region EDI results: “scoring low” (or EDI vulnerability), meeting of “few/no” developmental expectations, and domain averages. All measures are assessed separately for each of the five developmental domains. “Scoring low” is the most widely used measure. Data are reported at the regional and neighborhood level.

The United States Public Land Survey (PLS) divided land into one square mile units, termed sections. Surveyors used trees to locate section corners and other locations of interest (witness trees). As a result, a systematic ecological dataset was produced with regular sampling over a large region of the United States, beginning in Ohio in 1786 and continuing westward.
We digitized and georeferenced archival hand drawn maps of these witness trees for 27 counties in Ohio. This dataset consists of a GIS point shapefile with 11,925 points located at section corners, recording 26,028 trees (up to four trees could be recorded at each corner). We retain species names given on each archival map key, resulting in 70 unique species common names. PLS records were obtained from hand-drawn archival maps of original witness trees produced by researchers at The Ohio State University in the 1960’s. Scans of these maps are archived as “The Edgar Nelson Transeau Ohio Vegetation Survey” at The Ohio State University: http://hdl.handle.net/1811/64106.
The 27 counties are: Adams, Allen, Auglaize, Belmont, Brown, Darke, Defiance, Gallia, Guernsey, Hancock, Lawrence, Lucas, Mercer, Miami, Monroe, Montgomery, Morgan, Noble, Ottawa, Paulding, Pike, Putnam, Scioto, Seneca, Shelby, Williams, Wyandot. Coordinate Reference System: North American Datum 1983 (NAD83). This material is based upon work supported by the National Science Foundation under grants #DEB-1241874, 1241868, 1241870, 1241851, 1241891, 1241846, 1241856, 1241930.

TIGER road data for the MSA. When compared to high-resolution imagery and other transportation datasets positional inaccuracies were observed. As a result caution should be taken when using this dataset. TIGER, TIGER/Line, and Census TIGER are registered trademarks of the U.S. Census Bureau. ZCTA is a trademark of the U.S. Census Bureau. The Census 2000 TIGER/Line files are an extract of selected geographic and cartographic information from the Census TIGER data base. The geographic coverage for a single TIGER/Line file is a county or statistical equivalent entity, with the coverage area based on January 1, 2000 legal boundaries. A complete set of census 2000 TIGER/Line files includes all counties and statistically equivalent entities in the United States, Puerto Rico, and the Island Areas. The Census TIGER data base represents a seamless national file with no overlaps or gaps between parts. However, each county-based TIGER/Line file is designed to stand alone as an independent data set or the files can be combined to cover the whole Nation. The Census 2000 TIGER/Line files consist of line segments representing physical features and governmental and statistical boundaries. The boundary information in the TIGER/Line files are for statistical data collection and tabulation purposes only; their depiction and designation for statistical purposes does not constitute a determination of jurisdictional authority or rights of ownership or entitlement. The Census 2000 TIGER/Line files do NOT contain the Census 2000 urban areas which have not yet been delineated. The files contain information distributed over a series of record types for the spatial objects of a county. There are 17 record types, including the basic data record, the shape coordinate points, and geographic codes that can be used with appropriate software to prepare maps. Other geographic information contained in the files includes attributes such as feature identifiers/census feature class codes (CFCC) used to differentiate feature types, address ranges and ZIP Codes, codes for legal and statistical entities, latitude/longitude coordinates of linear and point features, landmark point features, area landmarks, key geographic features, and area boundaries. The Census 2000 TIGER/Line data dictionary contains a complete list of all the fields in the 17 record types.

   This is part of a collection of 221 Baltimore Ecosystem Study metadata records that point to a geodatabase.


   The geodatabase is available online and is considerably large. Upon request, and under certain arrangements, it can be shipped on media, such as a usb hard drive.


   The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders.


   Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful.

64 EDI neighbourhoods have been delineated in York Region. Neighbourhoods are identified by socio-demographic similarities and distinctive characteristics throughout a geographic area, and they reflect the communities in which children live. Several criteria were assessed when delineating the EDI Neighbourhood boundaries: an extensive community consultation process was conducted that identified geographic areas which reflected residents’ perceptions of the communities in which they lived; natural boundaries such as highways, railways and watercourses were taken into consideration; it was required that the EDI sample size be large enough in each neighbourhood to support statistically sound data analysis and to ensure that no child could be identified; it was necessary to limit the EDI neighbourhoods to a reasonable number for data analysis purposes; EDI neighbourhood boundaries were based on dissemination areas (DAs) to facilitate the use of Census/NHS data which are available at the DA level. Each EDI neighbourhood consists of a complete set of DAs; EDI results for each neighbourhood include all children that live within the neighbourhood regardless of which school they attend.

This layer is a high-resolution tree canopy change-detection layer for Baltimore City, MD. It contains three tree-canopy classes for the period 2007-2015: (1) No Change; (2) Gain; and (3) Loss. It was created by extracting tree canopy from existing high-resolution land-cover maps for 2007 and 2015 and then comparing the mapped trees directly. Tree canopy that existed during both time periods was assigned to the No Change category while trees removed by development, storms, or disease were assigned to the Loss class. Trees planted during the interval were assigned to the Gain category, as were the edges of existing trees that expanded noticeably. Direct comparison was possible because both the 2007 and 2015 maps were created using object-based image analysis (OBIA) and included similar source datasets (LiDAR-derived surface models, multispectral imagery, and thematic GIS inputs). OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. No accuracy assessment was conducted, but the dataset will be subjected to manual review and correction. 2006 LiDAR and 2014 LiDAR data was also used to assist in tree canopy change.

ArcGIS Dashboards useful links (GeoNet). ArcGIS Dashboards is a configurable web app that provides location-aware data visualization and analytics for a real-time operational view of people, services, assets, and events. You can monitor the activities and key performance indicators that are vital to meeting your organization’s objectives within a dynamic dashboard._Communities around the world are taking strides in mitigating the threat that COVID-19 (coronavirus) poses. Geography and location analysis have a crucial role in better understanding this evolving pandemic.When you need help quickly, Esri can provide data, software, configurable applications, and technical support for your emergency GIS operations. Use GIS to rapidly access and visualize mission-critical information. Get the information you need quickly, in a way that’s easy to understand, to make better decisions during a crisis.Esri’s Disaster Response Program (DRP) assists with disasters worldwide as part of our corporate citizenship. We support response and relief efforts with GIS technology and expertise.More information...Edi

We constructed a time-series spatial dataset of parcel boundaries for the period 1962-2005, in roughly 4-year intervals, by digitizing historical plat maps for Dane County and combining them with the 2005 GIS digital parcel dataset. The resulting datasets enable the consistent tracking of subdivision and development for all parcels over a given time frame. The process involved 1) dissolving and merging the 2005 digital Dane County parcel dataset based on contiguity and name, 2) further merging 2005 parcels based on the hard copy 2005 Plat book, and then 3) the reverse chronological merging of parcels to reconstruct previous years, at 4-year intervals, based on historical plat books. Additional land use information such as 1) whether a structure was actually constructed (using the companion digitized aerial photo dataset), 2) cover crop, and 3) permeable surface area, can be added to these datasets at a later date.

This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429).
The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection.

  No Abstract Available

Tags

   Social system, socio-economic resources, justice, BES, Environmental Justice, Environmental disamentities, Zoning Board of Appeals




   Summary


   For use in the environmental injustices study of Baltimore relating to patterns of environmental disamenties in relation to low income/minority communities.


   Description


   This feature class layer is a point dataset of appeals to the Zoning Board of Appeals (ZBA) from 1938 to 1999 concerning identified environmental disamentities. The data was gathered from records from the Zoning Board of Appeals decisions since 1931 relating to environmental disamentities and to be used to examine environmental injustices involving low income/minority communities in Baltimore. To see if environmental injustices exist in Baltimore, this point layer will be overlayed with race/income data to determine if patterns of inequity exist. Points were placed manually using the associated addresses from the ZBA_master dataset. The ID number associated with each point is related to its appeal number from the Zoning Board of Appeals. Multiple points on the data layer have the same ZBA_ID number, making it a one-to-many relationship. This layer can be joined with the ZBA_master table using the "ZBA_point_relationship" and the field "ZBA_ID".


   Credits


   UVM Spatial Analysis Lab


   Use limitations


   None. There are no restrictions on the use of this dataset. The authors of this dataset make no representations of any kind, including but not limited to the warranties of merchantability or fitness for a particular use, nor are any such warranties to be implied with respect to the data.


   Extent



   West -76.708848  East -76.527906 

   North 39.371642  South 39.199548 



   This is part of a collection of 221 Baltimore Ecosystem Study metadata records that point to a geodatabase.


   The geodatabase is available online and is considerably large. Upon request, and under certain arrangements, it can be shipped on media, such as a usb hard drive.


   The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders.


   Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful.

In 2000, the Maryland Geological Survey (MGS) was awarded a Coastal ZoneManagement grant to complete the acquisition of a recent (ca. 1990) digital shoreline for the coastal regions of Maryland - the Chesapeake Bay, its tributaries, the coastal bays, and the Atlantic coast.MGS contracted the services of EarthData International, Inc. (EDI) of Gaithersburg, Md., to extract shorelines from an existing wetlands delineation, which was based on photo interpretation of 3.75-minute digital orthophoto quarter quads (DOQQs). In areas where a wetlands coverage was not yet available, EDI interpreted shorelines directly from the orthophotography. DOQQ registration (Maryland State Plane Coordinate System, NAD 83, meters) was transferred automatically to the shoreline vectors. Following shoreline extraction or interpretation, EDI assigned attributes to the vectors based on shoreline type: beach, vegetated, structure, or water's edge. This data set has been merged into a state wide file for ease of processing and decision making.This is a MD iMAP hosted service. Find more information at https://imap.maryland.gov.Feature Service Link:https://mdgeodata.md.gov/imap/rest/services/Boundaries/MD_MarineBoundaries/FeatureServer/0**Please note, due to the size of this dataset, you may receive an error message when trying to download the dataset. You can download this dataset directly from MD iMAP Services at: https://mdgeodata.md.gov/imap/rest/services/Boundaries/MD_MarineBoundaries/MapServer/exts/MDiMAPDataDownload/customLayers/0**

These data depict the elevation features of Konza Prairie. Record type 1 is a 2 meter resolution digital elevation model (DEM) of Konza Prairie, generated from 2006 LiDAR DEM data collected to standard USGS specifications (GIS200). Record type 3 is a 2010 10 meter (1/3 arc second) resolution National Elevation Dataset (NED) DEM of Konza Prairie (GIS202). Record type 4 is a 10 meter resolution NED DEM of Konza Prairie with a modified 3 kilometer buffer (GIS203). Record type 5 is a USGS topographic map of Konza Prairie (GIS204). These data are available to download as zipped shapefiles (.zip), compressed Google Earth KML layers (.kmz), and associated EML metadata (.xml).

This dataset represent the ice on and ice off dates for Douglas Lake in Pellston, Michigan. The first observations are from the 1930 and were intermittently documented until the mid 1970s. The observations are complete since then to current.

This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429).
The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection.

  No Abstract Available

In this project we established a network of forest inventory plots to gather the data needed to forecast future forest performance under global change. Data collected from forest inventory plots, i.e., size and location of individual trees from all ages and species, have been shown to be particularly useful to link tree species demographic rates (survival, growth, age at maturity, fecundity) with community characteristics (assemblages and species turnovers), and are also widely used to estimate biomass removal (logging) and biomass production (carbon sequestration).

Mayor's Order 2017-115 establishes a comprehensive data policy for the District government. The data created and managed by the District government are valuable assets and are independent of the information systems in which the data reside. As such, the District government shall: maintain an inventory of its enterprise datasets; classify enterprise datasets by level of sensitivity; regularly publish the inventory, including the classifications, as an open dataset; and strategically plan and manage its investment in data.The greatest value from the District’s investment in data can only be realized when enterprise datasets are freely shared among District agencies, with federal and regional governments, and with the public to the fullest extent consistent with safety, privacy, and security. For more information, please visit https://opendata.dc.gov/pages/edi-overview. Previous years of EDI can be found on Open Data.

A complete survey of overstory vegetation and saplings in the Bob Farmer plots within the UM Biological Station clearut and burn chronosequence. Surveys were completed in 1979 and 1998 to measure the change in biomass and forest composition in the burn plots with forest succession.

This data package contains the streams lines generated for 63,360 Digital Line Grafics (DLG) in and near our research areas in the Bonanza Creek Experimental forest. Geospatial_Data_Presentation_Form: vector digital data.

We performed this work for the (Great Lakes) Lake States, which comprise 6% of the land area, but 7% of the forest and 9% of the forest SOC in the U.S., as the second in a series of ecoregional SOC assessments. Most importantly, our analysis indicates that natural factors, such as soil texture and parent material, exert more control over SOC stocks than land use or management. With that for context, our analysis also indicates which natural factors most influence management impacts on SOC storage. We report an overall trend of significantly diminished topsoil SOC stocks with harvesting, consistent across all three datasets, while also demonstrating how certain sites and soils diverge from this pattern, including some that show opposite trends. Impacts of fire grossly mirror those of harvesting, with declines near the top of the profile, but potential gains at depth and no net change when considering the whole profile. Land use changes showing significant SOC impacts are limited to reforestation on barren mining substrates (large and variable gains) and conversion of native forest to cultivation (losses). We describe patterns within the observational data that reveal the physical basis for preferential land use, e.g., cultivation of soils with the most favorable physical properties, and forest plantation establishment on the most marginal soils, and use these patterns to identify management opportunities and considerations. We also qualify our results with ratings of confidence, based on their degree of support across approaches, and offer concise, defensible tactics for adapting management operations to site-specific criteria and SOC vulnerability. The three distinct data sources used in this analysis are archived as part of this dataset.

In this data, you’ll find crayfish movement data (walking speeds and times spent in different sections of the test arena) as a response to differing concentrations and types of fear cues (predatory and alarm cues) as well as differing levels of safety cues. The organization of the fear cues and safety cues in the arena were orthogonal to each other which allows a user to examine movements along each of the different stimuli levels separately or in combination. The movement of the crayfish was digitized at 1 point per second and at a single location (center of the carapace) of the animal.

Inventory of Historic Properties for Carroll County. The Maryland Inventory of Historic Properties vector layers are depictions of the approximate locations of historic structures, monuments, districts, and other properties that are listed on the Maryland Inventory of Historic Properties. No attribute information is available for this dataset.

   This is part of a collection of 221 Baltimore Ecosystem Study metadata records that point to a geodatabase.


   The geodatabase is available online and is considerably large. Upon request, and under certain arrangements, it can be shipped on media, such as a usb hard drive.


   The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders.


   Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful.