The Bridges of Pittsburgh is a highly interdisciplinary and collaborative public-facing project that pays homage both to an innovative, field-defining mathematical problem and to one of the defining features of our city. We proposed to discover how many of Pittsburgh’s 446 bridges could be traversed without crossing the same bridge twice, in the process addressing issues in processing crowdsourced GIS data, performing graph traversal with complex constraints, and using network analysis to compare communities formed by this road network to the historically-defined neighborhoods of Pittsburgh.This ZIP file contains an RStudio project, with package dependencies bundled via packrat (https://rstudio.github.io/packrat/).- The osmar/ directory contains OSM data, our processing code, and outputs used to generate the map at https://bridgesofpittsburgh.net - 2019_final_community_analysis/ contains code and derived datasets for the community analysis portion of the projectwar- The legacy/ directory contains experimental datasets and code from the earliest phase of this project, which were later superseded by the main pipeline in the osmar/ directory.Each directory contains further README.md files documenting their structure.

The Freight Analysis Framework (FAF5) - Network Links dataset was created from 2017 base year data and was published on April 11, 2022 from the Bureau of Transportation Statistics (BTS) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The FAF (Version 5) Network contains 487,384 link features. All link features are topologically connected to permit network pathbuilding and vehicle assignment using a variety of assignment algorithms. The FAF Link and the FAF Node datasets can be used together to create a network. The link features include all roads represented in prior FAF networks, and all roads in the National Highway System (NHS) and the National Highway Freight Network (NHFN) that are currently open to traffic. Other included links provide connections between intersecting routes, and to select intermodal facilities and all U.S. counties. The network consists of over 588,000 miles of equivalent road mileage. The dataset covers the 48 contiguous States plus the District of Columbia, Alaska, and Hawaii. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529027

This data contains general information about Pedestrian Network in Hong Kong. Pedestrian Network is a set of 3D line features derived from road features and road furniture from Lands Department and Transport Department. A number of attributes are associated with the pedestrian network such as spatially related street names. Besides, the pedestrian network includes information like wheelchair accessibility and obstacles to facilitate the digital inclusion for the needy. Please refer to this video to learn how to use 3D Pedestrian Network Dataset in ArcGIS Pro to facilitate your transportation analysis.The data was provided in the formats of JSON, GML and GDB by Lands Department and downloaded via GEODATA.GOV.HK website.

The original data files were processed and converted into an Esri file geodatabase. Wheelchair accessibility, escalator/lift, staircase walking speed and street gradient were used to create and build a network dataset in order to demonstrate basic functions for pedestrian network and routing analysis in ArcMap and ArcGIS Pro. There are other tables and feature classes in the file geodatabase but they are not included in the network dataset, users have to consider the use of information based on their requirements and make necessary configurations. The coordinate system of this dataset is Hong Kong 1980 Grid.

The objectives of uploading the network dataset to ArcGIS Online platform are to facilitate our Hong Kong ArcGIS users to utilize the data in a spatial ready format and save their data conversion effort.

For details about the schema and information about the content and relationship of the data, please refer to the data dictionary provided by Lands Department at https://geodata.gov.hk/gs/download-datadict/201eaaee-47d6-42d0-ac81-19a430f63952.

For details about the data, source format and terms of conditions of usage, please refer to the website of GEODATA STORE at https://geodata.gov.hk.Dataset last updated on: 2022 Oct

This dataset provides high-resolution gridded temperature and precipitation observations from a selection of sources. Additionally the dataset contains daily global average near-surface temperature anomalies. All fields are defined on either daily or monthly frequency. The datasets are regularly updated to incorporate recent observations. The included data sources are commonly known as GISTEMP, Berkeley Earth, CPC and CPC-CONUS, CHIRPS, IMERG, CMORPH, GPCC and CRU, where the abbreviations are explained below. These data have been constructed from high-quality analyses of meteorological station series and rain gauges around the world, and as such provide a reliable source for the analysis of weather extremes and climate trends. The regular update cycle makes these data suitable for a rapid study of recently occurred phenomena or events. The NASA Goddard Institute for Space Studies temperature analysis dataset (GISTEMP-v4) combines station data of the Global Historical Climatology Network (GHCN) with the Extended Reconstructed Sea Surface Temperature (ERSST) to construct a global temperature change estimate. The Berkeley Earth Foundation dataset (BERKEARTH) merges temperature records from 16 archives into a single coherent dataset. The NOAA Climate Prediction Center datasets (CPC and CPC-CONUS) define a suite of unified precipitation products with consistent quantity and improved quality by combining all information sources available at CPC and by taking advantage of the optimal interpolation (OI) objective analysis technique. The Climate Hazards Group InfraRed Precipitation with Station dataset (CHIRPS-v2) incorporates 0.05° resolution satellite imagery and in-situ station data to create gridded rainfall time series over the African continent, suitable for trend analysis and seasonal drought monitoring. The Integrated Multi-satellitE Retrievals dataset (IMERG) by NASA uses an algorithm to intercalibrate, merge, and interpolate “all'' satellite microwave precipitation estimates, together with microwave-calibrated infrared (IR) satellite estimates, precipitation gauge analyses, and potentially other precipitation estimators over the entire globe at fine time and space scales for the Tropical Rainfall Measuring Mission (TRMM) and its successor, Global Precipitation Measurement (GPM) satellite-based precipitation products. The Climate Prediction Center morphing technique dataset (CMORPH) by NOAA has been created using precipitation estimates that have been derived from low orbiter satellite microwave observations exclusively. Then, geostationary IR data are used as a means to transport the microwave-derived precipitation features during periods when microwave data are not available at a location. The Global Precipitation Climatology Centre dataset (GPCC) is a centennial product of monthly global land-surface precipitation based on the ~80,000 stations world-wide that feature record durations of 10 years or longer. The data coverage per month varies from ~6,000 (before 1900) to more than 50,000 stations. The Climatic Research Unit dataset (CRU v4) features an improved interpolation process, which delivers full traceability back to station measurements. The station measurements of temperature and precipitation are public, as well as the gridded dataset and national averages for each country. Cross-validation was performed at a station level, and the results have been published as a guide to the accuracy of the interpolation. This catalogue entry complements the E-OBS record in many aspects, as it intends to provide high-resolution gridded meteorological observations at a global rather than continental scale. These data may be suitable as a baseline for model comparisons or extreme event analysis in the CMIP5 and CMIP6 dataset.

Reason for SelectionRiver networks with a variety of connected stream size classes are more likely to have a wide range of available habitat to support a greater number of species. This will help retain aquatic biodiversity in a changing climate by allowing species to access climate refugia and move between habitats. Input DataBase Blueprint 2022 extent Southeast Blueprint 2024 extentSoutheast Aquatic Resources Partnership’s Network Complexity metric The Southeast Aquatic Resources Partnership (SARP) developed metrics for their Southeast Aquatic Barrier Prioritization Tool. On June 7, 2023, Brendan Ward with Astute Spruce (software developer working on behalf of SARP) shared high resolution NHDPlus flowlines with attributes depicting the network complexity attribute for each functional network (see definition of “functional network” below). The network complexity attribute calculates the total number of different stream size classes within each functional network. SARP assigned stream and river reaches to size classes based on total drainage area:1a: Headwaters (<3.861 sq mi)1b: Creeks (≥3.861 and <8.61 sq mi)2: Small Rivers (≥38.61 and <200 sq mi)3a: Medium Tributary Rivers (≥200 and <1,000 sq mi)3b: Medium Mainstem Rivers (≥1,000 and <3,861 sq mi)4: Large Rivers (≥3,861 and <9,653 sq mi)5: Great Rivers (≥9,653 sq mi)Functional NetworkSARP compiles the Southeast Aquatic Barrier Inventory from national, regional, state, and local partner databases across the Southeast region. These include the National Inventory of Dams (2018), National Anthropogenic Barrier Dataset (2012), databases from state dam safety programs and other state agencies, information from local partners, and dam locations estimated by SARP. Waterfalls are compiled from national datasets and local partners. Dams and waterfalls are snapped to hydrologic networks extracted from the National Hydrography Dataset (NHD) - High Resolution Beta version. All dams and waterfalls are treated as “hard” barriers for network connectivity analysis. Aquatic networks are cut at the location of each barrier. All network “loops” (non-primary flowlines) are omitted from the analysis. An upstream functional network is constructed by traversing upstream from each barrier through all tributaries to the upstream-most origination point or upstream barrier, whichever comes first. Additional functional networks are defined from downstream-most non-barrier termination points, such as marine areas or other downstream termination points. The total length of all network segments within a functional network is summed to calculate the total network length of each functional network. Each flowline segment within the NHD is assigned to a size class based on total drainage area. This was used to calculate the number of unique size classes per functional network. Estimated Floodplain Map of the Conterminous U.S. from the Environmental Protection Agency’s (EPA) EnviroAtlas; see this factsheet for more information; download the data The EPA Estimated Floodplain Map of the Conterminous U.S. displays “...areas estimated to be inundated by a 100-year flood (also known as the 1% annual chance flood). These data are based on the Federal Emergency Management Agency (FEMA) 100-year flood inundation maps with the goal of creating a seamless floodplain map at 30-m resolution for the conterminous United States. This map identifies a given pixel’s membership in the 100-year floodplain and completes areas that FEMA has not yet mapped” (EPA 2018). National Hydrography Dataset Plus High Resolution (NHDPlus HR) National Release catchments, accessed 11-30-2022; download the data; view the user guideNHDPlus Version 2.1 medium resolution catchments (note: V2.1 is just the current sub-version of the dataset generally called NHDPlusV2); view the user guideCatchments A catchment is the local drainage area of a specific stream segment based on the surrounding elevation. Catchments are defined based on surface water features, watershed boundaries, and elevation data. It can be difficult to conceptualize the size of a catchment because they vary significantly in size based on the length of a particular stream segment and its surrounding topography—as well as the level of detail used to map those characteristics. To learn more about catchments and how they’re defined, check out these resources:An article from USGS explaining the differences between various NHD productsThe glossary at the bottom of this tutorial for an EPA water resources viewer, which defines some key termsMapping StepsMerge the functional network lines from the 11 subregions delivered by SARP into one feature class. Convert the combined SARP network complexity values from the high resolution NHDPlus flowlines to a 30 m raster. Clip to the Base Blueprint 2022 extent.Apply the network complexity values to the NHDPlus HR catchments using the ArcPy Zonal Statistics “MAJORITY” function. This results in a raster where each catchment is assigned the majority network complexity value that intersects the catchment. Most catchments have only one intersecting line, but for catchments with interior dams, the analysis uses the majority network complexity value.To define the analysis extent of the indicator, make a copy of the NHDPlus HR catchments and convert it to raster, assigning it a value of 1.Clip the network complexity raster to the EPA floodplain layer. During this step, assign a value of 0 to areas outside the EPA floodplain. Zero values are intended to help users better understand the extent of this indicator and make it perform better in online tools.Some areas of the floodplain are not scored in the resulting layer because they are missing SARP network complexity values. This is due to the fact that some small reaches, such as braids and loops in the stream network, are not assigned a network complexity value. SARP has to remove loops and braided streams in order to calculate network complexity because the analysis can only accommodate a one-way flow of water. Identify these holes in the floodplain and fill them in by looking at the network complexity value of the surrounding pixels and assigning the maximum value to the missing catchments in the floodplain. Note: This simplifies a complex series of analysis steps. For more specifics, please consult the code.Clip the network complexity raster to the NHDPlus V2.1 medium resolution catchments. This removes estuarine areas that are outside the intended scope of this indicator, particularly on the NC coast.As a final step, clip to the spatial extent of Southeast Blueprint 2024.Note: For more details on the mapping steps, code used to create this layer is available in the Southeast Blueprint Data Download under > 6_Code. Final indicator valuesIndicator values are assigned as follows:7 = 7 connected stream classes6 = 6 connected stream classes5 = 5 connected stream classes4 = 4 connected stream classes3 = 3 connected stream classes2 = 2 connected stream classes1 = 1 connected stream class0 = Not identified as a floodplainKnown IssuesThis indicator does not include other smaller scale attributes of complexity (e.g., sinuosity, mixtures of riffles/pools/runs) that influence the habitat quality of the connections. The EPA Estimated Floodplain layer sometimes misses the small, linear connections made by artificial canals, especially when they go through areas that wouldn’t naturally be part of the floodplain. As a result, some areas (like lakes) that are connected via canals may appear to be disconnected, but still receive high scores.Small headwaters and creeks are not included in this indicator because the EPA estimated floodplain dataset does not include them.While this indicator generally includes the open water area of reservoirs, some open water portions of reservoirs (e.g., Kerr Lake in NC/VA) are missing from the estimated floodplain dataset.This indicator likely overestimates the number of connected stream classes in some areas due to missing barriers in the inventory, such as smaller dams or road-stream crossings. It could also underestimate the number of connected stream classes, given the extensive ongoing restoration work to improve aquatic connectivity across the SECAS geography. If you identify a missing barrier or a removed barrier, please let SARP know by emailing Kat Hoenke at kat@southeastaquatics.net. You can learn more about the current inventory of dams and road-stream crossings by visiting https://connectivity.sarpdata.com/.SARP did a lot of work to snap the dam locations to the line network, but there are likely still dams (including some large ones) that didn’t get snapped correctly due to the large distance between the centerpoint of the dam and the nearest flowline. If you see any of these cases when reviewing the data, please let SARP know (the giveaway is networks that look longer than they should on a map).In the area just south of Guadalupe Mountains National Park in West Texas, this indicator depicts the floodplain as a series of straight lines that poorly match the actual floodplain. This is due to an error in the EPA floodplain map used in this indicator.Due to issues with the national NHDPlus HR catchments layer, there are a handful of missing catchments (e.g., northwest TX, coastal LA, and eastern NC). These places receive a value of NoData in the indicator and are therefore underprioritized. We are investigating ways to resolve this in future updates.This indicator may slightly overvalue network complexity in WV compared to other Southeast states because the coverage of dams and barriers data is not as comprehensive. While the dams and barriers data coverage improved sufficiently for us to use the network complexity indicator across the entire state of WV in 2023 for the first time (whereas it was only used in the southern part of the state in 2022), there is still room for improvement and we anticipate significant

Access National Hydrography ProductsThe National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee.The NHD is a national framework for assigning reach addresses to water-related entities, such as industrial discharges, drinking water supplies, fish habitat areas, wild and scenic rivers. Reach addresses establish the locations of these entities relative to one another within the NHD surface water drainage network, much like addresses on streets. Once linked to the NHD by their reach addresses, the upstream/downstream relationships of these water-related entities--and any associated information about them--can be analyzed using software tools ranging from spreadsheets to geographic information systems (GIS). GIS can also be used to combine NHD-based network analysis with other data layers, such as soils, land use and population, to help understand and display their respective effects upon one another. Furthermore, because the NHD provides a nationally consistent framework for addressing and analysis, water-related information linked to reach addresses by one organization (national, state, local) can be shared with other organizations and easily integrated into many different types of applications to the benefit of all.Statements of attribute accuracy are based on accuracy statements made for U.S. Geological Survey Digital Line Graph (DLG) data, which is estimated to be 98.5 percent. One or more of the following methods were used to test attribute accuracy: manual comparison of the source with hardcopy plots; symbolized display of the DLG on an interactive computer graphic system; selected attributes that could not be visually verified on plots or on screen were interactively queried and verified on screen. In addition, software validated feature types and characteristics against a master set of types and characteristics, checked that combinations of types and characteristics were valid, and that types and characteristics were valid for the delineation of the feature. Feature types, characteristics, and other attributes conform to the Standards for National Hydrography Dataset (USGS, 1999) as of the date they were loaded into the database. All names were validated against a current extract from the Geographic Names Information System (GNIS). The entry and identifier for the names match those in the GNIS. The association of each name to reaches has been interactively checked, however, operator error could in some cases apply a name to a wrong reach.Points, nodes, lines, and areas conform to topological rules. Lines intersect only at nodes, and all nodes anchor the ends of lines. Lines do not overshoot or undershoot other lines where they are supposed to meet. There are no duplicate lines. Lines bound areas and lines identify the areas to the left and right of the lines. Gaps and overlaps among areas do not exist. All areas close.The completeness of the data reflects the content of the sources, which most often are the published USGS topographic quadrangle and/or the USDA Forest Service Primary Base Series (PBS) map. The USGS topographic quadrangle is usually supplemented by Digital Orthophoto Quadrangles (DOQs). Features found on the ground may have been eliminated or generalized on the source map because of scale and legibility constraints. In general, streams longer than one mile (approximately 1.6 kilometers) were collected. Most streams that flow from a lake were collected regardless of their length. Only definite channels were collected so not all swamp/marsh features have stream/rivers delineated through them. Lake/ponds having an area greater than 6 acres were collected. Note, however, that these general rules were applied unevenly among maps during compilation. Reach codes are defined on all features of type stream/river, canal/ditch, artificial path, coastline, and connector. Waterbody reach codes are defined on all lake/pond and most reservoir features. Names were applied from the GNIS database. Detailed capture conditions are provided for every feature type in the Standards for National Hydrography Dataset available online through https://prd-wret.s3-us-west-2.amazonaws.com/assets/palladium/production/atoms/files/NHD%201999%20Draft%20Standards%20-%20Capture%20conditions.PDF.Statements of horizontal positional accuracy are based on accuracy statements made for U.S. Geological Survey topographic quadrangle maps. These maps were compiled to meet National Map Accuracy Standards. For horizontal accuracy, this standard is met if at least 90 percent of points tested are within 0.02 inch (at map scale) of the true position. Additional offsets to positions may have been introduced where feature density is high to improve the legibility of map symbols. In addition, the digitizing of maps is estimated to contain a horizontal positional error of less than or equal to 0.003 inch standard error (at map scale) in the two component directions relative to the source maps. Visual comparison between the map graphic (including digital scans of the graphic) and plots or digital displays of points, lines, and areas, is used as control to assess the positional accuracy of digital data. Digital map elements along the adjoining edges of data sets are aligned if they are within a 0.02 inch tolerance (at map scale). Features with like dimensionality (for example, features that all are delineated with lines), with or without like characteristics, that are within the tolerance are aligned by moving the features equally to a common point. Features outside the tolerance are not moved; instead, a feature of type connector is added to join the features.Statements of vertical positional accuracy for elevation of water surfaces are based on accuracy statements made for U.S. Geological Survey topographic quadrangle maps. These maps were compiled to meet National Map Accuracy Standards. For vertical accuracy, this standard is met if at least 90 percent of well-defined points tested are within one-half contour interval of the correct value. Elevations of water surface printed on the published map meet this standard; the contour intervals of the maps vary. These elevations were transcribed into the digital data; the accuracy of this transcription was checked by visual comparison between the data and the map.

This Existing Vegetation (Eveg) polygon feature class is a CALVEG (Classification and Assessment with LANDSAT of Visible Ecological Groupings) map product at a scale of 1:24,000 for CALVEG Zone 7, the South Coast . Source imagery for this layer ranges from the year 2002 to 2010.The CALVEG classification system was used for vegetation typing and crosswalked to other classification systems in this database. USGS Land Use / Land Cover Anderson 1 classification system is included in the database to meet national standard requirements. Mapping standards meet requirements of the USDA Forest Service as defined by the FS GIS data dictionary, FGDC Vegetation standards and the FS Existing Vegetation Classification and Mapping Technical Guide. Regional add-ons are retained for crosswalking to the California Wildlife Habitat Relationship System (CWHR). For a description of CALVEG and a data dictionary for codes in this database, go to the Existing Vegetation Layer Description at http://www.fs.usda.gov/detail/r5/landmanagement/resourcemanagement/?cid=stelprdb5365219. For an index of CALVEG zones, go to http://www.fs.usda.gov/detail/r5/landmanagement/resourcemanagement/?cid=stelprdb5347192 and select the link called Existing Vegetation Tiles Index. For a CALVEG mapping status by scale and year, go to http://www.fs.usda.gov/detail/r5/landmanagement/resourcemanagement/?cid=stelprdb5347192 and select the "Existing Vegetation Mapping Status by Year, Scale and Project" link. *******Note: This layer is comprised of "multi-part" features, spatially separate polygons sharing the same attributes and stored as a single feature. A group of islands could be represented as a multi-part polygon feature. This allows for reduction in the size of the database and portability across a network. For analysis purposes however, it is wise to select a smaller area of interest and break apart features using the "Multipart To Singlepart" tool in ArcGIS. In its entirety, a "single-part" format of this feature class can potentially be more than one million polygons.