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This data shows the regional boundary which indicates the jurisdiction between York Region and neighbouring regions. The municipal boundaries define the limits of the Region's nine area municipalities: Aurora, East Gwillimbury, Georgina, King, Markham, Newmarket, Richmond Hill, Whitchurch-Stouffville and Vaughan. In 2007 an extensive investigation and clean-up of the municipal boundary locations was completed which included: snapping the boundaries to the road centrelines (which were spatially adjusted to the orthophotography in 2005); recalculating the boundary along Highway 404 to follow the 150 feet west of the centre line of the highway as outlined in the 1970 Regional Municipality of York Act; spatially adjusting the boundary along the Holland River to coincide with the centre of the river as illustrated in the 2005 orthophotography; spatially adjusting the boundary along Lake Simcoe according to the recent corrections completed by the Lake Simcoe Regional Conservation Authority according to the 2002 orthophotography; and several areas often questioned by users of the municipal boundary data were clarified in cooperation with the local municipalities.

This archive contains fine spatial-resolution translations of 112 contemporary climate projections over the contiguous United States. The original projections are from the World Climate Research Programme's (WCRP's) Coupled Model Intercomparison Project phase 3 (CMIP3) multi-model dataset, which was referenced in the Intergovernmental Panel on Climate Change Fourth Assessment Report.

a Total study population in the first columns and numbers of cases in subsequent columns.

IntroductionChildhood stunting is a global public health concern, associated with both short and long-term consequences, including high child morbidity and mortality, poor development and learning capacity, increased vulnerability for infectious and non-infectious disease. The prevalence of stunting varies significantly throughout Ethiopian regions. Therefore, this study aimed to assess the geographical variation in predictors of stunting among children under the age of five in Ethiopia using 2019 Ethiopian Demographic and Health Survey.MethodThe current analysis was based on data from the 2019 mini Ethiopian Demographic and Health Survey (EDHS). A total of 5,490 children under the age of five were included in the weighted sample. Descriptive and inferential analysis was done using STATA 17. For the spatial analysis, ArcGIS 10.7 were used. Spatial regression was used to identify the variables associated with stunting hotspots, and adjusted R2 and Corrected Akaike Information Criteria (AICc) were used to compare the models. As the prevalence of stunting was over 10%, a multilevel robust Poisson regression was conducted. In the bivariable analysis, variables having a p-value < 0.2 were considered for the multivariable analysis. In the multivariable multilevel robust Poisson regression analysis, the adjusted prevalence ratio with the 95% confidence interval is presented to show the statistical significance and strength of the association.ResultThe prevalence of stunting was 33.58% (95%CI: 32.34%, 34.84%) with a clustered geographic pattern (Moran’s I = 0.40, p40 (APR = 0.74, 95%CI: 0.55, 0.99). Children whose mother had secondary (APR = 0.74, 95%CI: 0.60, 0.91) and higher (APR = 0.61, 95%CI: 0.44, 0.84) educational status, household wealth status (APR = 0.87, 95%CI: 0.76, 0.99), child aged 6–23 months (APR = 1.87, 95%CI: 1.53, 2.28) were all significantly associated with stunting.ConclusionIn Ethiopia, under-five children suffering from stunting have been found to exhibit a spatially clustered pattern. Maternal education, wealth index, birth interval and child age were determining factors of spatial variation of stunting. As a result, a detailed map of stunting hotspots and determinants among children under the age of five aid program planners and decision-makers in designing targeted public health measures.

File List
varpart2.MEM.R (md5: 21fd3f2e321e83d0ea2cc8bb2a6db8ad) varpart3.MEM.R (md5: 11b0317aaaeaca378578f4fc5de66219) varpart4.MEM.R (md5: 334e5781208523a8e4589fff5034fb1f) varpart.MEM.Documentation.pdf (md5: 75611861dc4efde46c427063a2a7abb0) Description Files varpart2.MEM.R, varpart2.MEM.R, and varpart2.MEM.R are R-language functions. Upload them to the R window through the Files menu.

  Windows clients: Source R Code...
  Mac OS X clients: Source File...


  File varpart.MEM.Documentation.pdf is the documentation file for the three functions.

This dataset contains gridded adjustment factors for correction of the Quantitative Precipitation Estimations (QPE) of the two operational C-band weather radars operated by the Royal Netherlands Meteorological Institute (KNMI). The factors are based on the CARROTS (Climatology-based Adjustments for Radar Rainfall in an OperaTional Setting) method, described in Imhoff et al. (2021).

The factors are available for every yearday (temporal resolution of one day) and are based on ten years (2009 - 2018) of radar and reference rainfall data, as distributed by KNMI.

For the derivation of the factors, both the operational radar QPE (https://doi.org/10.4121/uuid:05a7abc4-8f74-43f4-b8b1-7ed7f5629a01) and a reference rainfall dataset of KNMI (https://dataplatform.knmi.nl/catalog/datasets/index.html?x-dataset=rad_nl25_rac_mfbs_em_5min&&x-dataset-version=2.0) are used. The reference is not available in real time, but becomes available with a one to two month delay and was therefore available for this climatological factor derivation.
The derivation method was as follows per grid cell in the radar domain (Imhoff et al., 2021):
1. For every day in the period 2009--2018, an accumulation took place of all 5-min rainfall sums (of both the unadjusted radar QPE and the reference) within a moving window of 15 days prior to and 15 days after the day of interest.

2. For every yearday, the accumulations (per day) from the previous step were averaged over the ten years.
3. Gridded climatological adjustment factors (Fclim) were calculated per yearday as: Fclim(i,j) = RA(i,j) / RU(i,j). In this equation, RA(i,j) is the reference rainfall sum for the ten years and RU(i,j) the operationally available unadjusted radar QPE sum, based on the previous two steps, at grid cell (i, j).

For more details about the method, see Imhoff et al. (2021). For more information about the reference dataset, which consists of the radar QPE spatially adjusted with observations from 31 automatic and 325 manual rain gauges, see Overeem et al. (2009a,b).

The United States Section of the International Boundary and Water Commission (IBWC) has 15 miles of flood control levees in the Presidio area. A project was initiated in calendar year 2002 between the IBWC and ERDC-WES to perform a condition assessment of their levees using airborne geophysics and detailed geological mapping of the flood plain. The project includes electromagnetic-induction (EM) data, Light Detection and Ranging data (LIDAR), historical and current aerial photography, geologic and geomorphologic interpretations from historical photography, digital videography, and soils data. These data are part of an enterprise Geographical Information System (eGIS). The eGIS organizes and manipulates all pertinent data for the condition assessment of levees in addition to containing modeling and data visualization tools. Levee segments that were not captured in the IBWC and ERDC-WES project were digitized by GIT using engineering maps provided by IBWC and spatially adjusted with 2008 orthoimagery from 3001, Inc. or ESRI ArcGIS Online World Imagery. Some segments from the IBWC and ERDC-WES project were also field verified by GIT. The following layer iidentifies the levee centerline as digitized from 1996 aerial photography.

This archive contains 234 projections of monthly BCSD CMIP5 projections of precipitation and monthly means of daily-average, daily maximum and daily minimum temperature over the contiguous United States. For more information visit http://gdo-dcp.ucllnl.org/downscaled_cmip_projections/

This dataset contains both GESMAR (Geodetic Survey Mark Register database) survey marks and Non-Geodetic control points that are used by Landgate to maintain and improve the spatial accuracy of the Spatial Cadastral Database (SCDB) which is the official digital cadastral map base of all crown and freehold land parcels within the State of Western Australia. Non-geodetic control points (referred to as Cadastral Control), is a set of non GESMAR survey marks that have been spatially adjusted against the GESMAR network. Connections between Cadastral Control points and cadastral marks are used to improve spatial accuracy of the SCDB. The dataset can also be used to assist with the spatial upgrade or improvement of other SCDB datasets. Like cadastral point coordinates, the spatial location (coordinates) of Cadastral Control points is dynamic and may change as a result of adjustments to the GESMAR (Geodetic) network. This dataset should not be confused with the Geodetic Survey Control (LGATE-076) layer also available in SLIP, which contains detailed information relating to Geodetic Survey Control marks (GESMAR).

NOTE: This product is for information purposes only and is not guaranteed. The information may be out of date and should not be relied upon without further verification from the original documents. Where the information is being used for legal purposes then the original documents must be searched for all legal requirements.

© Western Australian Land Information Authority (Landgate). Use of Landgate data is subject to Personal Use License terms and conditions unless otherwise authorised under approved License terms and conditions.

This is an ESRI polygon shapefile of tracts for Valley Forge NHP (VAFO). Tracts shown on inset maps A, B, and C were spatially adjusted (i.e., rubbersheeted) to correspond to the adjacent tract boundaries shown on the main VAFO Land Status Map. In addition, the entire tracts data set was spatially adjusted (i.e., rubbersheeted) to the VAFO orthophoto mosaic referenced below.

This data set of polygon feature represents Riverside County's City Sphere of Influence. Areas that are affected by a neighboring City, but are not annexed to them. Topology has been run and all gaps and overlaps have been fixed. The data has been adjusted to match Riverside County Parcel Boundaries. Data was spatially adjusted in 2020. Maintained by Adam Grim: 12/2020

Scheme of sub-blocking model effects, variance-covariance structures, and spatial-statistic adjustment of y using multi-environmental trial (MET) analysis as an example.

A new relationship-estimation model to perform a frequency-dispersion-normalized estimation and reduce the unwanted effects of ecological errors, Ecologically Corrected Spatial Relationship Estimator (ECSRE).

This dataset shows the areas of biological significance (ABS) on Pohnpei. The original dataset was created by The Nature Conservancy. A subset to show only Pohnpei was created by the Island Research & Education Initiative (iREi). These data are intended to capture those areas that represent the wide range of biodiversity features in the marine and terrestial areas of FSM. They are used to guide conservation planning and projects in FSM, and ultimately to help establish conservation areas. Polygons capturing expert knowledge from FSM Blueprint project. This version of ABS features has been spatially adjusted to line up with FSM Base Target features. Original version was based off a variety of available data and spatial registration was a bit loose. The dataset is included in the Digital Atlas of Micronesia, module Pohnpei, created by Island Research & Education Initiative (iREi), in collaboration with Water and Environmental Research Institute of the Western Pacific (WERI) University of Guam and partial funding from United States Geological Survey (USGS), under WRRI 104-B Program, project # 2016GU302B.

This dataset is a spatially corrected version of the 1991 Canadian Census Tract Cartographic Boundary File (CBF). The original boundary file from Statistics Canada contained substantial spatial mismatch error compared to boundaries of other census years. We corrected this mismatch error via a conflation procedure which is further described here: https://github.com/jamaps/census_canada_conflation

Imagery CaptureSensor - A3 35Focal Length - 300 mmImagery Type - RGBAcquisition Date(s) - 2020-11-05Flying Height (above MSL) - 8300 feetNumber of Images - 233Capture Methodology - Primary Runs - 11, Secondary/Tie Runs - 0Forward / Side Overlap (%) - 56 / 65.5OrthosmosaicA digital orthophoto is a raster image of remotely sensed data in which displacement in the image due to sensor orientation and terrain relief have been corrected (orthorectification). Orthophotos combine the image characteristics of a photograph with the geometric qualities of a map and can be used as a backdrop layer in conjunction with other spatial information.Spatial Resolution - 6 cmSpatial Accuracy - 3 pixels @ 68% confidence (Sigma 1)Spatial Accuracy Notes - RMSE values of 0.059m in X and 0.003m in Y to existing control.Map Projection - Web Mercator Auxiliary SphereRectification Surface - Rectification processes are via Visionmap LightspeedTile Size / Grid - SW_123000_4567000_1km SW - Refers to the south-west coordinate of the tile (bottom left); 123000 - Coordinate easting of south-west tile corner; 4567000 - Coordinate northing of south-west tile cornerLimitations of DataThis dataset contains imagery which has been aero-triangulated, spatially adjusted and rectified using a digital ground surface model. The stated spatial accuracy of this product is relevant for features present at ground level only. Elevated structures (roof-tops, tree canopies, etc) will be affected by relief displacement and cannot be reliably measured from this product. Spatial accuracy is reduced in areas of dense vegetation.

description: The 2015 Mississippi coastal shorelines were originally extracted from 2015 Landsat imagery and published within United States Geological Survey (USGS) Open-File Report (OFR) 2015-1179 (https://doi.org/10.3133/ofr20151179). Shoreline files for Ship, Horn, and Petit Bois Islands were merged to a single shapefile and spatially adjusted using 2015/2016 USGS bathymetric survey tracklines (Dewitt and others, 2017) to more closely match island shoreline positions during USGS surveys.; abstract: The 2015 Mississippi coastal shorelines were originally extracted from 2015 Landsat imagery and published within United States Geological Survey (USGS) Open-File Report (OFR) 2015-1179 (https://doi.org/10.3133/ofr20151179). Shoreline files for Ship, Horn, and Petit Bois Islands were merged to a single shapefile and spatially adjusted using 2015/2016 USGS bathymetric survey tracklines (Dewitt and others, 2017) to more closely match island shoreline positions during USGS surveys.

Imagery CaptureSensor - A3 35Focal Length - 300 mmImagery Type - RGBAcquisition Date(s) - 2020-11-05Flying Height (above MSL) - 8300 feetNumber of Images - 253Capture Methodology - Primary Runs - 10, Secondary/Tie Runs - 0Forward / Side Overlap (%) - 56 / 65.5OrthosmosaicA digital orthophoto is a raster image of remotely sensed data in which displacement in the image due to sensor orientation and terrain relief have been corrected (orthorectification). Orthophotos combine the image characteristics of a photograph with the geometric qualities of a map and can be used as a backdrop layer in conjunction with other spatial information.Spatial Resolution - 6 cmSpatial Accuracy - 3 pixels @ 68% confidence (Sigma 1)Spatial Accuracy Notes - RMSE values of 0.023m in X and 0.072m in Y to existing controlMap Projection - Web Mercator Auxiliary SphereRectification Surface - Rectification processes are via Visionmap LightspeedTile Size / Grid - SW_123000_4567000_1km SW - Refers to the south-west coordinate of the tile (bottom left); 123000 - Coordinate easting of south-west tile corner; 4567000 - Coordinate northing of south-west tile cornerLimitations of DataThis dataset contains imagery which has been aero-triangulated, spatially adjusted and rectified using a digital ground surface model. The stated spatial accuracy of this product is relevant for features present at ground level only. Elevated structures (roof-tops, tree canopies, etc) will be affected by relief displacement and cannot be reliably measured from this product. Spatial accuracy is reduced in areas of dense vegetation.

IMAGERY CAPTURE Sensor - A3 35 Focal Length - 300 mm Imagery Type - RGB Acquisition Date(s) - 2020-10-30 Flying Height (above MSL) - 8300 feet Number of Images - 40 Capture Methodology - Primary Runs - 4 Secondary/Tie Runs - 0 Forward / Side Overlap (%) - 56 / 65.5 ORTHOMOSAICA digital orthophoto is a raster image of remotely sensed data in which displacement in the image due to sensor orientation and terrain relief have been corrected (orthorectification). Orthophotos combine the image characteristics of a photograph with the geometric qualities of a map and can be used as a backdrop layer in conjunction with other spatial information.Spatial Resolution - 6 cmSpatial Accuracy - 3 pixels @ 68% confidence (Sigma 1)Spatial Accuracy Notes - No existing control held over the siteMap Projection - Web Mercator Auxiliary SphereRectification Surface - Yandaran_3m_Ortho_DTM.img. The DNRME DEM for Yandaran was used without any edits. No alterations were made.Description of Rectification - Rectification processes are via Visionmap LightspeedTile Size / Grid - SW_123000_4567000_1km SW - Refers to the south-west coordinate of the tile (bottom left); 123000 - Coordinate easting of south-west tile corner; 4567000 - Coordinate northing of south-west tile cornerLimitations of Data This dataset contains imagery which has been aero-triangulated, spatially adjusted and rectified using a digital ground surface model. The stated spatial accuracy of this product is relevant for features present at ground level only. Elevated structures (roof-tops, tree canopies, etc) will be affected by relief displacement and cannot be reliably measured from this product.