map.social is a fun and engaging map-based outreach platform that allows users to individually or collectively create maps in a common map gallery. map.social allows residents, constituents, community stakeholders, and others to provide map referenced comments – a way for anyone to create a map of "their" community in a gallery that can be viewed by fellow community members. Individual maps can be collectively analyzed or brought into GIS for deeper analysis.

NOTICE TO PROVISIONAL 2023 LAND USE DATA USERS: Please note that on December 6, 2024 the Department of Water Resources (DWR) published the Provisional 2023 Statewide Crop Mapping dataset. The link for the shapefile format of the data mistakenly linked to the wrong dataset. The link was updated with the appropriate data on January 27, 2025. If you downloaded the Provisional 2023 Statewide Crop Mapping dataset in shapefile format between December 6, 2024 and January 27, we encourage you to redownload the data. The Map Service and Geodatabase formats were correct as posted on December 06, 2024.

Thank you for your interest in DWR land use datasets.

The California Department of Water Resources (DWR) has been collecting land use data throughout the state and using it to develop agricultural water use estimates for statewide and regional planning purposes, including water use projections, water use efficiency evaluations, groundwater model developments, climate change mitigation and adaptations, and water transfers. These data are essential for regional analysis and decision making, which has become increasingly important as DWR and other state agencies seek to address resource management issues, regulatory compliances, environmental impacts, ecosystem services, urban and economic development, and other issues. Increased availability of digital satellite imagery, aerial photography, and new analytical tools make remote sensing-based land use surveys possible at a field scale that is comparable to that of DWR’s historical on the ground field surveys. Current technologies allow accurate large-scale crop and land use identifications to be performed at desired time increments and make possible more frequent and comprehensive statewide land use information. Responding to this need, DWR sought expertise and support for identifying crop types and other land uses and quantifying crop acreages statewide using remotely sensed imagery and associated analytical techniques. Currently, Statewide Crop Maps are available for the Water Years 2014, 2016, 2018- 2022 and PROVISIONALLY for 2023.

Historic County Land Use Surveys spanning 1986 - 2015 may also be accessed using the CADWR Land Use Data Viewer: https://gis.water.ca.gov/app/CADWRLandUseViewer.

For Regional Land Use Surveys follow: https://data.cnra.ca.gov/dataset/region-land-use-surveys.

For County Land Use Surveys follow: https://data.cnra.ca.gov/dataset/county-land-use-surveys.

For a collection of ArcGIS Web Applications that provide information on the DWR Land Use Program and our data products in various formats, visit the DWR Land Use Gallery: https://storymaps.arcgis.com/collections/dd14ceff7d754e85ab9c7ec84fb8790a.

Recommended citation for DWR land use data: California Department of Water Resources. (Water Year for the data). Statewide Crop Mapping—California Natural Resources Agency Open Data. Retrieved “Month Day, YEAR,” from https://data.cnra.ca.gov/dataset/statewide-crop-mapping.

aThe part of the assembly covered after anchoring the 1st generation map, calculated on the largest distance between the anchored markers of a linkage group, disregarding order among markers.

Reconstructing past landscapes from historical maps requires quantifying the accuracy and completeness of these sources. The accuracy and completeness of two historical maps of the same period covering the same area in Israel were examined: the 1:63,360 British Palestine Exploration Fund map (1871-1877) and the 1:100,000 French Levés en Galilée (LG) map (1870). These maps cover the mountainous area of the Galilee (northern Israel), a region with significant natural and topographical diversity, and a long history of human presence. Land-cover features from both maps, as well as the contours drawn on the LG map, were digitized. The overall correspondence between land-cover features shown on both maps was 59% and we found that the geo-referencing method employed (transformation type and source of control points) did not significantly affect these correspondence measures. Both maps show that in the 1870s, 35% of the Galilee was covered by Mediterranean maquis, with less than 8% of the area used for permanent agricultural cropland (e.g., plantations). This article presents how the reliability of the maps was assessed by using two spatial historical sources, and how land-cover classes that were mapped with lower certainty and completeness are identified. Some of the causes that led to observed differences between the maps, including mapping scale, time of year, and the interests of the surveyors, are also identified.

This dataset is a series of digital map-posters accompanying the AdaptNRM Guide: Helping Biodiversity Adapt: supporting climate adaptation planning using a community-level modelling approach.

These represent supporting materials and information about the community-level biodiversity models applied to climate change. Map posters are organised by four biological groups (vascular plants, mammals, reptiles and amphibians), two climate change scenario (1990-2050 MIROC5 and CanESM2 for RCP8.5), and five measures of change in biodiversity.

The map-posters present the nationally consistent data at locally relevant resolutions in eight parts – representing broad groupings of NRM regions based on the cluster boundaries used for climate adaptation planning (http://www.environment.gov.au/climate-change/adaptation) and also Nationally.

Map-posters are provided in PNG image format at moderate resolution (300dpi) to suit A0 printing. The posters were designed to meet A0 print size and digital viewing resolution of map detail. An additional set in PDF image format has been created for ease of download for initial exploration and printing on A3 paper. Some text elements and map features may be fuzzy at this resolution.

Each map-poster contains four dataset images coloured using standard legends encompassing the potential range of the measure, even if that range is not represented in the dataset itself or across the map extent.

Most map series are provided in two parts: part 1 shows the two climate scenarios for vascular plants and mammals and part 2 shows reptiles and amphibians. Eight cluster maps for each series have a different colour theme and map extent. A national series is also provided. Annotation briefly outlines the topics presented in the Guide so that each poster stands alone for quick reference.

An additional 77 National maps presenting the probability distributions of each of 77 vegetation types – NVIS 4.1 major vegetation subgroups (NVIS subgroups) - are currently in preparation.

Example citations:

Williams KJ, Raisbeck-Brown N, Prober S, Harwood T (2015) Generalised projected distribution of vegetation types – NVIS 4.1 major vegetation subgroups (1990 and 2050), A0 map-poster 8.1 - East Coast NRM regions. CSIRO Land and Water Flagship, Canberra. Available online at www.AdaptNRM.org and https://data.csiro.au/dap/.

Williams KJ, Raisbeck-Brown N, Harwood T, Prober S (2015) Revegetation benefit (cleared natural areas) for vascular plants and mammals (1990-2050), A0 map-poster 9.1 - East Coast NRM regions. CSIRO Land and Water Flagship, Canberra. Available online at www.AdaptNRM.org and https://data.csiro.au/dap/.

This dataset has been delivered incrementally. Please check that you are accessing the latest version of the dataset. Lineage: The map posters show case the scientific data. The data layers have been developed at approximately 250m resolution (9 second) across the Australian continent to incorporate the interaction between climate and topography, and are best viewed using a geographic information system (GIS). Each data layers is 1Gb, and inaccessible to non-GIS users. The map posters provide easy access to the scientific data, enabling the outputs to be viewed at high resolution with geographical context information provided.

Maps were generated using layout and drawing tools in ArcGIS 10.2.2

A check list of map posters and datasets is provided with the collection.

Map Series: 7.(1-77) National probability distribution of vegetation type – NVIS 4.1 major vegetation subgroup pre-1750 #0x

8.1 Generalised projected distribution of vegetation types (NVIS subgroups) (1990 and 2050)

9.1 Revegetation benefit (cleared natural areas) for plants and mammals (1990-2050)

9.2 Revegetation benefit (cleared natural areas) for reptiles and amphibians (1990-2050)

10.1 Need for assisted dispersal for vascular plants and mammals (1990-2050)

10.2 Need for assisted dispersal for reptiles and amphibians (1990-2050)

11.1 Refugial potential for vascular plants and mammals (1990-2050)

11.1 Refugial potential for reptiles and amphibians (1990-2050)

12.1 Climate-driven future revegetation benefit for vascular plants and mammals (1990-2050)

12.2 Climate-driven future revegetation benefit for vascular reptiles and amphibians (1990-2050)

The Access Network Map of England is a national composite dataset of Access layers, showing analysis of extent of Access provision for each Lower Super Output Area (LSOA), as a percentage or area coverage of access in England. The ‘Access Network Map’ was developed by Natural England to inform its work to improve opportunities for people to enjoy the natural environment. This map shows, across England, the relative abundance of accessible land in relation to where people live. Due to issues explained below, the map does not, and cannot, provide a definitive statement of where intervention is necessary. Rather, it should be used to identify areas of interest which require further exploration. Natural England believes that places where people can enjoy the natural environment should be improved and created where they are most wanted. Access Network Maps help support this work by providing means to assess the amount of accessible land available in relation to where people live. They combine all the available good quality data on access provision into a single dataset and relate this to population. This provides a common foundation for regional and national teams to use when targeting resources to improve public access to greenspace, or projects that rely on this resource. The Access Network Maps are compiled from the datasets available to Natural England which contain robust, nationally consistent data on land and routes that are normally available to the public and are free of charge. Datasets contained in the aggregated data:•
Agri-environment scheme permissive access (routes and open access)•
CROW access land (including registered common land and Section 16)•
Country Parks•
Cycleways (Sustrans Routes) including Local/Regional/National and Link Routes•
Doorstep Greens•
Local Nature Reserves•
Millennium Greens•
National Nature Reserves (accessible sites only)•
National Trails•
Public Rights of Way•
Forestry Commission ‘Woods for People’ data•
Village Greens – point data only Due to the quantity and complexity of data used, it is not possible to display clearly on a single map the precise boundary of accessible land for all areas. We therefore selected a unit which would be clearly visible at a variety of scales and calculated the total area (in hectares) of accessible land in each. The units we selected are ‘Lower Super Output Areas’ (LSOAs), which represent where approximately 1,500 people live based on postcode. To calculate the total area of accessible land for each we gave the linear routes a notional width of 3 metres so they could be measured in hectares. We then combined together all the datasets and calculated the total hectares of accessible land in each LSOA. For further information about this data see the following links:Access Network Mapping GuidanceAccess Network Mapping Metadata Full metadata can be viewed on data.gov.uk.

Using Story Actions you can create links from text in the side or floating panel that will jump to a specific section. This can be useful if you want to create a table of contents, or otherwise want to provide the ability to quickly navigate to a specific section. Here’s how you can use story actions to create links to sections in your Map Journal.

ProWest's LINK application. For PUBLIC use. All public GIS layers supplemented with aerial imagery and contours.

Organized for consumption in desktop and web applications.

* Raw data from the RADseq libraries are available under NCBI BioProject accession number PRJNA771889 (see related dataset section).
* SNP genotype call files (VCF format) are available at doi:10.6084/m9.figshare.19521955.v1 (see related dataset section) and as supplemental files to this dataset.

Although linkage maps are important tools in evolutionary biology, their availability for wild populations is limited. The population of song sparrows (Melospiza melodia) on Mandarte Island, Canada, is among the more intensively studied wild animal populations. Its long-term pedigree data, together with extensive genetic sampling, have allowed the study of a range of questions in evolutionary biology and ecology. However, the availability of genetic markers has been limited. We here describe 191 new microsatellite loci, including 160 high-quality polymorphic autosomal, 7 Z-linked and 1 W-linked markers. We used these markers to construct a linkage map for song sparrows with a total sex-averaged map length of 1731 cM and covering 35 linkage groups, and hence, these markers cover most of the 38–40 chromosomes. Female and male map lengths did not differ significantly. We then bioinformatically mapped these loci to the zebra finch (Taeniopygia guttata) genome and found that linkage groups were conserved between song sparrows and zebra finches. Compared to the zebra finch, marker order within small linkage groups was well conserved, whereas the larger linkage groups showed some intrachromosomal rearrangements. Finally, we show that as expected, recombination frequency between linked loci explained the majority of variation in gametic phase disequilibrium. Yet, there was substantial overlap in gametic phase disequilibrium between pairs of linked and unlinked loci. Given that the microsatellites described here lie on 35 of the 38–40 chromosomes, these markers will be useful for studies in this species, as well as for comparative genomics studies with other species.

This study aimed to compare genetic maps constructed using SNP markers derived from genotyping-by-sequencing, based on three distinct alfalfa reference genomes: (1) the monoploid assembly of ZhongmuNo.1 (Map 1), (2) the first homolog from the allele-aware XinJiang DaYe assembly (Map 2), and (3) a stable FASTA representation of a graph-based pangenome built using ZhongmuNo.1 as the backbone, incorporating four additional assemblies (Map 3). The alfalfa mapping population included 165 F1 individuals, and each final map was resolved into four haplotypes spanning eight linkage groups.

The mapping contains links to

- AGROVOC

- DDC

- DIGIZAURUS

- EUROVOC

- GEMET

- IATE

- ICD10

- KABA

- LCSH

- MESH

- RAMEAU

- STERNIK

- STW

- UDC

- UMLS

- WIKIPEDIA

This dataset contains the least-cost paths produced by our habitat connectivity model for the coastal marten, as described below in greater detail: After developing the coastal marten landscape resistance surface (i.e. movement model) and mapping habitat cores, we used Linkage Mapper to identify Least-Cost Paths (LCPs) between cores and to map broader mosaicked corridors around these single-pixel width paths. Linkage Mapper proceeds through several steps to complete these tasks (McRae and Kavanagh 2016). First, it identifies and lists which habitat cores are nearest neighbors using both the Euclidean distance (the “as-the-crow-flies” straight-line distance between nearest points on the edges of a pair of cores regardless of the character of the intervening landscape) and the cost-weighted distance (CWD). Second, it creates a “stick map” using straight-line linkages to connect core area pairs that are candidates for corridor mapping. Third, it locates the LCPs through the resistance surface between these pairs of cores and calculates their cost-weighted distance. This LCP is a single 30m pixel wide. A single pixel’s cost-weighted distance is the cell’s resistance value multiplied by the size of the cell, and the cost-weighted distance of an LCP is the sum of the cost-weighted distances of the pixels it runs through. This allows CWD to be reported in units that are directly comparable to the LCP length and the Euclidean distance between habitat cores (i.e. normalized to meters or kilometers), and all three of these metrics are included in the Linkage Mapper output for each linked pair of cores. Finally, Linkage Mapper creates least-cost corridors, which are wider swathes surrounding the LCPs that have only slightly higher movement costs and are more biologically realistic for conservation planning. It does this by calculating for each pixel on the landscape how much more costly a pathway passing through it between two cores would be than the LCP. Pixels closest to the LCP tend to be relatively close to it in CWD value, with the potential contribution of pixels to connectivity tending to decrease further away from the LCP. Linkage Mapper then creates a composite linkage map by assigning each pixel its minimum value relative to the nearest LCP (WHCWG 2010). Thus, the final map is a mosaic of normalized least-cost corridors around the LCPs. These corridors will vary in width depending on the resistance values surrounding the LCP. The creation of mosaicked corridors is the most fundamentally important function of Linkage Mapper in providing an informative depiction of habitat connectivity on the landscape, and is a significant advance over the simple LCP estimation that is a basic function available in ArcMap. This is an abbreviated and incomplete description of the dataset. Please refer to the spatial metadata for a more thorough description of the methods used to produce this dataset, and a discussion of any assumptions or caveats that should be taken into consideration.

This study describes the development of the medium density linkage map for pacu, which will be used as a framework to study relevant traits to the production of this species.

MapViewer is a graphical tool for viewing and comparing Gossypium spp. genetic maps. It includes dynamically scrollable maps, correspondence matrices, dot plots, links to details about map features, and exporting functionality. It was developed by the MainLab at Washington State University and is available for download for use in other Tripal databases. The query interface allows the user to select Species, Map, and Linkage Group options. Help information includes a video tutorial, user manual, and sample map, correspondence matrix, dot plot, and exported figures. Resources in this dataset:Resource Title: Website Pointer for CottonGen Map Viewer. File Name: Web Page, url: https://www.cottongen.org/MapViewer MapViewer is a graphical tool for viewing and comparing Gossypium spp. genetic maps. It includes dynamically scrollable maps, correspondence matrices, dot plots, links to details about map features, and exporting functionality. It was developed by the MainLab at Washington State University and is available for download for use in other Tripal databases. The query interface allows the user to select Species, Map, and Linkage Group options. Help information includes a video tutorial, user manual, and sample map, correspondence matrix, dot plot, and exported figures.

A clickable interface linking the user to the Rare Map Digitisation Project at the National Library of Australia

In species with large and complex genomes such as conifers, dense linkage maps are a useful for supporting genome assembly and laying the genomic groundwork at the structural, populational and functional levels. However, most of the 600+ extant conifer species still lack extensive genotyping resources, which hampers the development of high-density linkage maps. In this study, we developed a linkage map relying on 21,570 SNP makers in Sitka spruce (Picea sitchensis [Bong.] Carr.), a long-lived conifer from western North America that is widely planted for productive forestry in the British Isles. We used a single-step mapping approach to efficiently combine RAD-Seq and genotyping array SNP data for 528 individuals from two full-sib families. As expected for spruce taxa, the saturated map contained 12 linkages groups with a total length of 2,142 cM. The positioning of 5,414 unique gene coding sequences allowed us to compare our map with that of other Pinaceae species, which provided eviden..., The data included in this dataset is genotypic data for two full-sib families of Sitka spruce (Picea sitchensis) in the United Kingdom and resulting linkage maps for the species. Samples for DNA extraction and genotyping were collected from two full-sib genetic field trials as described in the accompanying publication. A SNP Chip array was developed for this work using exome capture. A subset of the samples had been genotyped using RAD Seq from a previous project (Fuentes-Utrilla et al 2017). The dataset includes information on the SNP array developed for the project and genotype data that has been filtered for missingness and minor allele frequency. Final results are in the form of linkage maps stored in csv files. Further information on collection methods and processing are detailed in the accompanying manuscript and scripts for data processing are available on GitHub (https://github.com/HayleyTumas/SitkaLinkageMap)., All files should be able to be opened using open access, freely available software. All tabular data are CSV or text files for the larger genotype data files. Code files are stored as bash script and can be opened using any text editor or in .R files that can be opened using the freely available R software., File generated 01-17-2024 by Hayley Tumas

#####GENERAL INFORMATION#####

Title: High-density genetic linkage mapping in Sitka spruce advances the integration of genomic resources in conifers

This is data and code accompanying the publication "High-density genetic linkage mapping in Sitka spruce advances the integration of genomic resources in conifers" in G3 (Tumas et al. 2024, Accepted, DOI pending).

PROJECT: The project developed a high-density genetic linkage map for Sitka spruce using genotype data for two full sib families from the UK Sitka spruce breeding population. Samples were genotyped using both a SNP array developed for the project and RAD-Seq. These two genotype datasets were used separately to generate genetic maps and together in a single combined map using the software LepMap-3. This combined genetic map (RADChip map) was used along with a genetic map for white spruce (Pavy et al 2017) to create an integrated species map. The data includes information on the SNP arra...

Additional file 14: Figure S6. Heat maps of integrated map.

Additional file 4: Anonymized experimental mapping data sets from agricultural plants, used to produce the maps presented in Supplementary Table S5 and Supplementary Figures S1 to S5: BC_ano_segData.raw. BC_ano_phyMap.txt. F2_ano_segData.raw. F2_ano_phyMap.txt. RIL1_ano_segData.raw. RIL1_ano_phyMap.txt. RIL2_ano_segData.raw. RIL2_ano_phyMap.txt. CP_ano_segData.gen. CP_ano_phyMap.txt.

Genetic linkage maps are essential for comparative genomics, high quality genome sequence assembly and fine scale quantitative trait locus (QTL) mapping. In the present study we identified and genotyped markers via restriction-site associated DNA (RAD) sequencing and constructed a genetic linkage map based on 1,597 SNP markers of an interspecific F2 cross of two closely related Lake Victoria cichlids (Pundamilia pundamilia and P. sp. 'red head'). The SNP markers were distributed on 22 linkage groups and the total map size was 1,594 cM with an average marker distance of 1.01 cM. This high-resolution genetic linkage map was used to anchor the scaffolds of the Pundamilia genome and estimate recombination rates along the genome. Via QTL mapping we identified a major QTL for sex in a ∼1.9 Mb region on Pun-LG10, which is homologous to Oreochromis niloticus LG 23 (Ore-LG23) and includes a well-known vertebrate sex-determination gene (amh).