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.

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 mapping contains links to

- AGROVOC

- DDC

- DIGIZAURUS

- EUROVOC

- GEMET

- IATE

- ICD10

- KABA

- LCSH

- MESH

- RAMEAU

- STERNIK

- STW

- UDC

- UMLS

- WIKIPEDIA

* 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.

Passengers are our Passion and Purpose!We Provide Reliable Public Transit ServicesCasper Transit is premier resource for public transportation service in Casper, Evansville, Mills, Bar Nunn and parts of Natrona County. We can get you to a doctor’s appointment, to the grocery store or hair salon by using ASSIST’s reliable bus system or stay with the dependable fixed routes by using LINK.CONTACT USContact us if you have any questions about our transportation services by visiting us on the web here or call (307) 235-8273, or (307) 235-8287, or visit us at 1715 E. 4th Street, Casper, WY 82601. We look forward to helping you get where you need to go, reliably!Follow this link for a Printable LINK Map & Schedule (To read PDF files, you need the Adobe Acrobat Reader 6.0 or higher. Click here to download it for free from Adobe's site.)The City of Casper's transit program operates without regard to race, color or national origin. If you have questions about the City's Civil Rights obligations, please contact the City of Casper at (307) 235-8255.

The User Guide V2.4 of the SinoLC-1 land-cover product. The SinoLC-1 was created by the Low-to-High Network (L2HNet), which can be found at: L2HNet. A more detailed description of the data can be found in the paper. More related work can be found at my homepage.

Click to check all the data versions and download the data (点击查看/下载所有数据版本)

NOTE: If you have any data needs, questions, or technical issues, contact us at ashelee@whu.edu.cn (Zhuohong Li, 李卓鸿).

The land-cover mapping method with Python code is open-access at Code link. You can now update the high-resolution land-cover map by yourself with the code! The updated method is accepted by CVPR 2024 (Paper link).

我们的最新制图算法被计算机视觉顶会CVPR2024接收(Paper link),代码开源在:Code link,您可以利用该代码高效地更新自己数据集的高分土地覆盖图。

Citation format of the paper:
Li, Z., He, W., Cheng, M., Hu, J., Yang, G., and Zhang, H.: SinoLC-1: the first 1 m resolution national-scale land-cover map of China created with a deep learning framework and open-access data, Earth Syst. Sci. Data, 15, 4749–4780, 2023.

Li, Z., Zhang, H., Lu, F., Xue, R., Yang, G. and Zhang, L.: Breaking the resolution barrier: A low-to-high network for large-scale high-resolution land-cover mapping using low-resolution labels, ISPRS Journal of Photogrammetry and Remote Sensing. 192, pp.244-267, 2022.

BibTex format of the paper:

@article{li2023sinolc,
 title={SinoLC-1: the first 1 m resolution national-scale land-cover map of China created with a deep learning framework and open-access data},
 author={Li, Zhuohong and He, Wei and Cheng, Mofan and Hu, Jingxin and Yang, Guangyi and Zhang, Hongyan},
 journal={Earth System Science Data},
 volume={15},
 number={11},
 pages={4749--4780},
 year={2023},
 publisher={Copernicus Publications G{\"o}ttingen, Germany}
}

@article{li2022breaking,
 title={Breaking the resolution barrier: A low-to-high network for large-scale high-resolution land-cover mapping using low-resolution labels},
 author={Li, Zhuohong and Zhang, Hongyan and Lu, Fangxiao and Xue, Ruoyao and Yang, Guangyi and Zhang, Liangpei},
 journal={ISPRS Journal of Photogrammetry and Remote Sensing},
 volume={192},
 pages={244--267},
 year={2022},
 publisher={Elsevier}
}

River interlinking stretches (30 river linking projects) across the country and their attributes

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. We converted the photointerpreted data into a GIS-usable format employing three fundamental processes: (1) orthorectify, (2) digitize, and (3) develop the geodatabase. All digital map automation was projected in Universal Transverse Mercator (UTM) projection, Zone 16, using North American Datum of 1983 (NAD83). To produce a polygon vector layer for use in ArcGIS, we converted each raster-based image mosaic of orthorectified overlays containing the photointerpreted data into a grid format using ArcGIS (Version 9.2, © 2006 Environmental Systems Research Institute, Redlands, California). In ArcGIS, we used the ArcScan extension to trace the raster data and produce ESRI shapefiles. We digitally assigned map attribute codes (both map class codes and physiognomic modifier codes) to the polygons, and checked the digital data against the photointerpreted overlays for line and attribute consistency. Ultimately, we merged the individual layers into a seamless layer of INDU and immediate environs. At this stage, the map layer has only map attribute codes assigned to each polygon. To assign meaningful information to each polygon (e.g., map class names, physiognomic definitions, link to NVC association and alliance codes), we produced a feature class table along with other supportive tables and subsequently related them together via an ArcGIS Geodatabase. This geodatabase also links the map to other feature class layers produced from this project, including vegetation sample plots, accuracy assessment sites, and project boundary extent. A geodatabase provides access to a variety of interlocking data sets, is expandable, and equips resource managers and researchers with a powerful GIS tool.

These are the main layers that were used in the mapping and analysis for the Santa Monica Mountains Local Coastal Plan, which was adopted by the Board of Supervisors on August 26, 2014, and certified by the California Coastal Commission on October 10, 2014. Below are some links to important documents and web mapping applications, as well as a link to the actual GIS data:

Plan Website – This has links to the actual plan, maps, and a link to our online web mapping application known as SMMLCP-NET. Click here for website. Online Web Mapping Application – This is the online web mapping application that shows all the layers associated with the plan. These are the same layers that are available for download below. Click here for the web mapping application. GIS Layers – This is a link to the GIS layers in the form of an ArcGIS Map Package, click here (LINK TO FOLLOW SOON) for ArcGIS Map Package (version 10.3). Also, included are layers in shapefile format. Those are included below.

Below is a list of the GIS Layers provided (shapefile format):

Recreation (Zipped - 5 MB - click here)

Coastal Zone Campground Trails (2012 National Park Service) Backbone Trail Class III Bike Route – Existing Class III Bike Route – Proposed

Scenic Resources (Zipped - 3 MB - click here)

Significant Ridgeline State-Designated Scenic Highway State-Designated Scenic Highway 200-foot buffer Scenic Route Scenic Route 200-foot buffer Scenic Element

Biological Resources (Zipped - 45 MB - click here)

National Hydrography Dataset – Streams H2 Habitat (High Scrutiny) H1 Habitat H1 Habitat 100-foot buffer H1 Habitat Quiet Zone H2 Habitat H3 Habitat

Hazards (Zipped - 8 MB - click here)

FEMA Flood Zone (100-year flood plain) Liquefaction Zone (Earthquake-Induced Liquefaction Potential) Landslide Area (Earthquake-Induced Landslide Potential) Fire Hazard and Responsibility Area

Zoning and Land Use (Zipped - 13 MB - click here)

Malibu LCP – LUP (1986) Malibu LCP – Zoning (1986) Land Use Policy Zoning

Other Layers (Zipped - 38 MB - click here)

Coastal Commission Appeal Jurisdiction Community Names Santa Monica Mountains (SMM) Coastal Zone Boundary Pepperdine University Long Range Development Plan (LRDP) Rural Village

Contact the L.A. County Dept. of Regional Planning's GIS Section if you have questions. Send to our email.

This collection grew out of a prototype case tracking and crime mapping application that was developed for the United States Attorney's Office (USAO), Southern District of New York (SDNY). The purpose of creating the application was to move from the traditionally episodic way of handling cases to a comprehensive and strategic method of collecting case information and linking it to specific geographic locations, and collecting information either not handled at all or not handled with sufficient enough detail by SDNY's existing case management system. The result was an end-user application designed to be run largely by SDNY's nontechnical staff. It consisted of two components, a database to capture case tracking information and a mapping component to link case and geographic data. The case tracking data were contained in a Microsoft Access database and the client application contained all of the forms, queries, reports, macros, table links, and code necessary to enter, navigate through, and query the data. The mapping application was developed using Environmental Systems Research Institute's (ESRI) ArcView 3.0a GIS. This collection shows how the user-interface of the database and the mapping component were customized to allow the staff to perform spatial queries without having to be geographic information systems (GIS) experts. Part 1 of this collection contains the Visual Basic script used to customize the user-interface of the Microsoft Access database. Part 2 contains the Avenue script used to customize ArcView to link the data maintained in the server databases, to automate the office's most common queries, and to run simple analyses.

The full linkage map of Argopecten irradians irradians and A. purpuratus. The full sequences of all markers on the linkage maps. The R codes for QTL mapping.

Salmonids are an important cultural and ecological resource exhibiting near worldwide distribution between their native and introduced range. Previous research has generated linkage maps and genomic resources for several species as well as genome assemblies for two species. We first leveraged improvements in mapping and genotyping methods to create a dense linkage map for Chinook salmon Oncorhynchus tshawytscha by assembling family data from different sources. We successfully mapped 14,620 SNP loci including 2,336 paralogs in subtelomeric regions. This improved map was then used as a foundation to integrate genomic resources for gene annotation and population genomic analyses. We anchored a total of 286 scaffolds from the Atlantic salmon genome to the linkage map to provide a framework for the placement 11,728 Chinook salmon ESTs. Previously identified thermotolerance QTL were found to co-localize with several candidate genes including HSP70, a gene known to be involved in thermal respo...

Seattle Parks and Recreation ARCGIS park feature map layer web services are hosted on Seattle Public Utilities' ARCGIS server. This web services URL provides a live read only data connection to the Seattle Parks and Recreations Public Restroom dataset.

Online mapping with Web Map Layers using Cadcorp application for display of various projects including Grounds Maint., Local Plans, Dog Fouling etc.

AbstractIn 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 evidence for high levels of synteny and gene order conservation in this family. We then developed an integrated map for P. sitchensis and P. glauca based on 27,052 makers and 11,609 gene sequences. Altogether, these two linkage maps, the accompanying catalog of 286,159 SNPs and the genotyping chip developed herein opens new perspectives for a variety of fundamental and more applied research objectives, such as for the improvement of spruce genome assemblies, or for marker-assisted sustainable management of genetic resources in Sitka spruce and related species. MethodsThe 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). Usage notesAll 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.

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.

Genetic linkage maps are valuable tools in evolutionary biology; however, their availability for wild populations is extremely limited. Fundulus heteroclitus (Atlantic killifish) is a non-migratory estuarine fish that exhibits high allelic and phenotypic diversity partitioned among subpopulations that reside in disparate environmental conditions. An ideal candidate model organism for studying gene-environment interactions, the molecular toolbox for F. heteroclitus is limited. We identified hundreds of novel microsatellites which, when combined with existing microsatellites and single nucleotide polymorphisms (SNPs), were used to construct the first genetic linkage map for this species. By integrating independent linkage maps from three genetic crosses, we developed a consensus map containing 24 linkage groups, consistent with the number of chromosomes reported for this species. These linkage groups span 2300 centimorgans (cM) of recombinant genomic space, intermediate in size relative to the current linkage maps for the teleosts, medaka and zebrafish. Comparisons between fish genomes support a high degree of synteny between the consensus F. heteroclitus linkage map and the medaka and (to a lesser extent) zebrafish physical genome assemblies. This dataset is associated with the following publication: Waits , E., J. Martinson , B. Rinner, S. Morris, D. Proestou, D. Champlin , and D. Nacci. Genetic linkage map and comparative genome analysis for the estuarine Atlantic killifish (Fundulus heteroclitus). Open Journal of Genetics. Scientific Research Publishing, Inc., Irvine, CA, USA, 6: 28-38, (2016).

Major libraries used: Osmar - To import data from open street maps Leaflet, mapview, sp - Mapping the network dplyr, plyr - To manipulate data frames Data used: The data used for creating the freeway network is obtained from open street maps, whereas, the data for sensors is obtained from PeMS. Methodology Creating the map required We start by downloading the bulk osm data for California (approx. 18GB). Next, using osmar library, we extract the required data using a bounding box, demarcating the latitude and longitude boundaries. bbox = corner_bbox(-118.0042, 33.6363, -117.7226, 33.9194) Then extracting only the freeway information (links and nodes) from the resulting data. The problem here is that most of the links contain more than two nodes, which would make the incidence matrix unnecessarily large. Thus, we extract the nodes that connect two links, and map them over the links. Creating the incidence matrix To create the incidence matrix, each link was looked up for the first and last node incident on it, keeping in mind the direction. The rows represent nodes, whereas the columns represent links. +1 was assigned at the head and -1 to the tail of the link, all other entries to 0. The incidence matrix has a dimension of 2973 by 2640. Assigning links to appropriate freeways The osm data does not explicitly mention about which freeway is a particular link part of. Thus, for the freeway links a combination of "name" and "ref" tag was used to obtain the information about the freeway. Adding ramps to the Fwy data frame So far, we only have freeway links assigned to the appropriate freeways. Now, we would like to add the immediate links that go off or on the freeway, aka ramps, to the data frame Fwy. This is done by checking for each node on the freeway segment, out of the links it is incident upon, which one is tagged as "motorway-link" in the osm data. If there is such a link, it was added to the Fwy data set with appropriate freeway values. Overlapping sensors over the network The sensor data is now used to add a layer over the existing graph to help visualize the complete network. Different types of sensors are grouped separately and can be viewed as per user's choice by clicking the check boxes. The legend shows the color used for each sensor type. Hovering upon the sensor, link or node highlights their IDs. By default, main line (ML) sensors are checked. Mapping sensors to the appropriate links Currently, by visualization we can figure out the link that contains a particular sensor. As PeMS sensor metadata does not interact with the osm data, the link-sensor relation is unknown. We need to create an algorithm such that each sensor automatically gets mapped to the link using the geographical properties. The algorithm for mapping sensors to the links is as follows: For each sensor location, extract all the links on the freeway segment in the direction sensor is installed For the nodes on each link, calculate 3 distances Distance between the nodes (d1) Distance between first node and sensor (d2) Distance between last node and sensor Calculate d1 - (d2+d3), call it d4 Calculate d4 for each link, and arrange d4 in ascending order The link for which d4 is smallest and lesser than a threshold (1e-4 in this case), assign it the sensor Repeat the above steps for each sensor location Finally, the results are stored as a form of a list (linkId). For illustration purpose, 5 ML sensors on I-5, highlighted on the map, are shown below with the appropriate link chosen by the algorithm. One can verify the IDs by hovering above the links in the map and cross checking with the table that appears below. Re-mapping ramp and freeway-freeway sensors Looking closely, one would figure out that the ramp sensors (OR/FR) are located on the freeways rather than ramps. Same for freeway-Freeway (FF) sensors. This was one of the tedious challenges I encountered in this project. But with a combination of a simple algorithm and manual work, the sensors were remapped. The details are omitted in this document. In the map below, all the remapped sensors are shown on their appropriate new links. Creating the adjacency matrix To create adjacency matrix, for each link, nodes having 1 or -1 were searched in the incidence matrix. The cell corresponding to these nodes in the adjacency matrix was assigned 1, else 0. Conclusion The results of this project, namely, incidence matrix, adjacency matrix and link-sensor relation data frame were used for the network sensor error estimation algorithm. This project led to the application of the error estimation algorithm on large networks, which is expected to result in an important contribution to the field of sensor bias estimation. The aim of this script is to automate the process of directed network graph formation, i.e., creation of incidence matrix, node adjacency matrix, and map the sensors to appropriate links. The data used fo...