Have you ever wanted to create your own maps, or integrate and visualize spatial datasets to examine changes in trends between locations and over time? Follow along with these training tutorials on QGIS, an open source geographic information system (GIS) and learn key concepts, procedures and skills for performing common GIS tasks – such as creating maps, as well as joining, overlaying and visualizing spatial datasets. These tutorials are geared towards new GIS users. We’ll start with foundational concepts, and build towards more advanced topics throughout – demonstrating how with a few relatively easy steps you can get quite a lot out of GIS. You can then extend these skills to datasets of thematic relevance to you in addressing tasks faced in your day-to-day work.

The dataset and the validation are fully described in a Nature Scientific Data Descriptor https://www.nature.com/articles/s41597-019-0265-5

If you want to use this dataset in an interactive environment, then use this link https://mybinder.org/v2/gh/GeographerAtLarge/TravelTime/HEAD

The following text is a summary of the information in the above Data Descriptor.

The dataset is a suite of global travel-time accessibility indicators for the year 2015, at approximately one-kilometre spatial resolution for the entire globe. The indicators show an estimated (and validated), land-based travel time to the nearest city and nearest port for a range of city and port sizes.

The datasets are in GeoTIFF format and are suitable for use in Geographic Information Systems and statistical packages for mapping access to cities and ports and for spatial and statistical analysis of the inequalities in access by different segments of the population.

These maps represent a unique global representation of physical access to essential services offered by cities and ports.

The datasets travel_time_to_cities_x.tif (where x has values from 1 to 12) The value of each pixel is the estimated travel time in minutes to the nearest urban area in 2015. There are 12 data layers based on different sets of urban areas, defined by their population in year 2015 (see PDF report).

travel_time_to_ports_x (x ranges from 1 to 5)

The value of each pixel is the estimated travel time to the nearest port in 2015. There are 5 data layers based on different port sizes.

Format Raster Dataset, GeoTIFF, LZW compressed Unit Minutes

Data type Byte (16 bit Unsigned Integer)

No data value 65535

Flags None

Spatial resolution 30 arc seconds

Spatial extent

Upper left -180, 85

Lower left -180, -60 Upper right 180, 85 Lower right 180, -60 Spatial Reference System (SRS) EPSG:4326 - WGS84 - Geographic Coordinate System (lat/long)

Temporal resolution 2015

Temporal extent Updates may follow for future years, but these are dependent on the availability of updated inputs on travel times and city locations and populations.

Methodology Travel time to the nearest city or port was estimated using an accumulated cost function (accCost) in the gdistance R package (van Etten, 2018). This function requires two input datasets: (i) a set of locations to estimate travel time to and (ii) a transition matrix that represents the cost or time to travel across a surface.

The set of locations were based on populated urban areas in the 2016 version of the Joint Research Centre’s Global Human Settlement Layers (GHSL) datasets (Pesaresi and Freire, 2016) that represent low density (LDC) urban clusters and high density (HDC) urban areas (https://ghsl.jrc.ec.europa.eu/datasets.php). These urban areas were represented by points, spaced at 1km distance around the perimeter of each urban area.

Marine ports were extracted from the 26th edition of the World Port Index (NGA, 2017) which contains the location and physical characteristics of approximately 3,700 major ports and terminals. Ports are represented as single points

The transition matrix was based on the friction surface (https://map.ox.ac.uk/research-project/accessibility_to_cities) from the 2015 global accessibility map (Weiss et al, 2018).

Code The R code used to generate the 12 travel time maps is included in the zip file that can be downloaded with these data layers. The processing zones are also available.

Validation The underlying friction surface was validated by comparing travel times between 47,893 pairs of locations against journey times from a Google API. Our estimated journey times were generally shorter than those from the Google API. Across the tiles, the median journey time from our estimates was 88 minutes within an interquartile range of 48 to 143 minutes while the median journey time estimated by the Google API was 106 minutes within an interquartile range of 61 to 167 minutes. Across all tiles, the differences were skewed to the left and our travel time estimates were shorter than those reported by the Google API in 72% of the tiles. The median difference was −13.7 minutes within an interquartile range of −35.5 to 2.0 minutes while the absolute difference was 30 minutes or less for 60% of the tiles and 60 minutes or less for 80% of the tiles. The median percentage difference was −16.9% within an interquartile range of −30.6% to 2.7% while the absolute percentage difference was 20% or less in 43% of the tiles and 40% or less in 80% of the tiles.

This process and results are included in the validation zip file.

Usage Notes The accessibility layers can be visualised and analysed in many Geographic Information Systems or remote sensing software such as QGIS, GRASS, ENVI, ERDAS or ArcMap, and also by statistical and modelling packages such as R or MATLAB. They can also be used in cloud-based tools for geospatial analysis such as Google Earth Engine.

The nine layers represent travel times to human settlements of different population ranges. Two or more layers can be combined into one layer by recording the minimum pixel value across the layers. For example, a map of travel time to the nearest settlement of 5,000 to 50,000 people could be generated by taking the minimum of the three layers that represent the travel time to settlements with populations between 5,000 and 10,000, 10,000 and 20,000 and, 20,000 and 50,000 people.

The accessibility layers also permit user-defined hierarchies that go beyond computing the minimum pixel value across layers. A user-defined complete hierarchy can be generated when the union of all categories adds up to the global population, and the intersection of any two categories is empty. Everything else is up to the user in terms of logical consistency with the problem at hand.

The accessibility layers are relative measures of the ease of access from a given location to the nearest target. While the validation demonstrates that they do correspond to typical journey times, they cannot be taken to represent actual travel times. Errors in the friction surface will be accumulated as part of the accumulative cost function and it is likely that locations that are further away from targets will have greater a divergence from a plausible travel time than those that are closer to the targets. Care should be taken when referring to travel time to the larger cities when the locations of interest are extremely remote, although they will still be plausible representations of relative accessibility. Furthermore, a key assumption of the model is that all journeys will use the fastest mode of transport and take the shortest path.

This dataset provides detailed information on road surfaces from OpenStreetMap (OSM) data, distinguishing between paved and unpaved surfaces across the region. This information is based on road surface prediction derived from hybrid deep learning approach. For more information on Methods, refer to the paper

Roughly 0.6438 million km of roads are mapped in OSM in this region. Based on AI-mapped estimates the share of paved and unpaved roads is approximately 0.0602 and 0.0169 (in million kms), corressponding to 9.344% and 2.6252% respectively of the total road length in the dataset region. 0.5667 million km or 88.0308% of road surface information is missing in OSM. In order to fill this gap, Mapillary derived road surface dataset provides an additional 0.0022 million km of information (corressponding to 0.3924% of total missing information on road surface)

It is intended for use in transportation planning, infrastructure analysis, climate emissions and geographic information system (GIS) applications.

This dataset provides comprehensive information on road and urban area features, including location, surface quality, and classification metadata. This dataset includes attributes from OpenStreetMap (OSM) data, AI predictions for road surface, and urban classifications.

AI features:

• pred_class: Model-predicted class for the road surface, with values "paved" or "unpaved."

• pred_label: Binary label associated with pred_class (0 = paved, 1 = unpaved).

• osm_surface_class: Classification of the surface type from OSM, categorized as "paved" or "unpaved."

• combined_surface_osm_priority: Surface classification combining pred_label and surface(OSM) while prioritizing the OSM surface tag, classified as "paved" or "unpaved."

• combined_surface_DL_priority: Surface classification combining pred_label and surface(OSM) while prioritizing DL prediction pred_label, classified as "paved" or "unpaved."

• n_of_predictions_used: Number of predictions used for the feature length estimation.

• predicted_length: Predicted length based on the DL model’s estimations, in meters.

• DL_mean_timestamp: Mean timestamp of the predictions used, for comparison.

OSM features may have these attributes(Learn what tags mean here):

• name: Name of the feature, if available in OSM.

• name:en: Name of the feature in English, if available in OSM.

• name:* (in local language): Name of the feature in the local official language, where available.

• highway: Road classification based on OSM tags (e.g., residential, motorway, footway).

• surface: Description of the surface material of the road (e.g., asphalt, gravel, dirt).

• smoothness: Assessment of surface smoothness (e.g., excellent, good, intermediate, bad).

• width: Width of the road, where available.

• lanes: Number of lanes on the road.

• oneway: Indicates if the road is one-way (yes or no).

• bridge: Specifies if the feature is a bridge (yes or no).

• layer: Indicates the layer of the feature in cases where multiple features are stacked (e.g., bridges, tunnels).

• source: Source of the data, indicating the origin or authority of specific attributes.

Urban classification features may have these attributes:

• continent: The continent where the data point is located (e.g., Europe, Asia).

• country_iso_a2: The ISO Alpha-2 code representing the country (e.g., "US" for the United States).

• urban: Binary indicator for urban areas based on the GHSU Urban Layer 2019. (0 = rural, 1 = urban)

• urban_area: Name of the urban area or city where the data point is located.

• osm_id: Unique identifier assigned by OpenStreetMap (OSM) to each feature.

• osm_type: Type of OSM element (e.g., node, way, relation).

The data originates from OpenStreetMap (OSM) and is augmented with model predictions using images downloaded from Mapillary in combination with the GHSU Global Human Settlement Urban Layer 2019 and AFRICAPOLIS2020 urban layer.

This dataset is one of many HeiGIT exports on HDX. See the HeiGIT website for more information.

We are looking forward to hearing about your use-case! Feel free to reach out to us and tell us about your research at communications@heigit.org – we would be happy to amplify your work.

Literature review dataset

This table lists the surveyed papers concerning the application of spatial analysis, GIS (Geographic Information Systems) as well as general geographic approaches and geostatistics, to the assessment of CoViD-19 dynamics. The period of survey is from January 1st, 2020 to December 15th, 2020. The first column lists the reference. The second lists the date of publication (preferably, the date of online publication). The third column lists the Country or the Countries and/or the subnational entities investigated. The fourth column lists the epidemiological data utilized in each paper. The fifth column lists other types of data utilized for the analysis. The sixth column lists the more traditionally statistically-based methods, if utilized. The seventh column lists the geo-statistical, GIS or geographic methods, if utilized. The eight column sums up the findings of each paper. The papers are also classified within seven thematic categories. The full references are available at the end of the table in alphabetical order.

This table was the basis for the realization of a comprehensive geographic literature review. It aims to be a useful tool to ease the "due-diligence" activity of all the researchers interested in the spatial analysis of the pandemic.

The reference to cite the related paper is the following:

Pranzo, A.M.R., Dai Prà, E. & Besana, A. Epidemiological geography at work: An exploratory review about the overall findings of spatial analysis applied to the study of CoViD-19 propagation along the first pandemic year. GeoJournal (2022). https://doi.org/10.1007/s10708-022-10601-y

To read the manuscript please follow this link: https://doi.org/10.1007/s10708-022-10601-y

A dataset that explores Green Card sponsorship trends, salary data, and employer insights for geographic information systems (geoinformatics) in the U.S.

This data set was generated through the 2020 LU/LC update mapping effort. The 2020 update is the seventh in a series of land use mapping efforts that was begun in 1986. Revisions and additions to the initial baseline layer were done in subsequent years from imagery captured in 1995/97, 2002, 2007, 2012, 2015 and now, 2020. This present 2020 update was created by comparing the 2015 LU/LC layer from NJDEP's Geographic Information Systems (GIS) database to 2020 color infrared (CIR) imagery and delineating and coding areas of change. Work for this data set was done by Aerial Information Systems, Inc., Redlands, CA, under direction of the New Jersey Department of Environmental Protection (NJDEP), Bureau of Geographic Information System (BGIS). LU/LC changes were captured by adding new line work and attribute data for the 2020 land use directly to the base data layer. All 2015 LU/LC polygons and 2015 LU/LC coding remains in this data set, so change analysis for the period 2015-2020 can be undertaken from this one layer. The mapping was done by USGS HUC8 basins, 13 of which cover portions of New Jersey. This statewide layer is composed of the final data sets generated for each HUC8 basin. Initial QA/QC was done on each HUC8 data set as it was produced with final QA/QC and basin-to-basin edgematching done on this statewide layer. The classification system used was a modified Anderson et al., classification system. Minimum mapping unit (MMU) is 1 acre for changes to non-water and non-wetland polygons. Changes to these two categories were mapped using .25 acres as the MMU. (See entry under the Advisory section concerning additional review being done on NHD waterbody attribute coding and impervious surface estimation.) ADVISORY This data set, edition 20231120, is a statewide layer that includes updated land use/land cover data for all HUC8 basins in New Jersey. The polygon delineations and associated land use code assignments are considered the final versions for this mapping effort. Note, however, that there is continuing review being done on this layer to update several additional attributes not presently evaluated in this edition. These attributes include several from the National Hydrography Database (NHD) that are specific to the waterbodies mapped in this layer, and several attributes containing impervious surface estimates for each polygon. Evaluating the NHD codes facilitates extracting the water features mapped in this layer and using them to update the New Jersey portion of the NHD. Those NHD specific attributes are still being evaluated and may be added to a future edition of this base data set. Similarly, additional review is being done to assess the feasibility of incorporating data on impervious surface (IS) amounts generated from two independent projects, one of which was just completed by NOAA, into this base land use layer. While the NHD and IS attributes will enhance the use of this base layer in several types of analyses, this present layer can be used for doing all primary land use analyses without having those attributes evaluated. Further, evaluating these extra attributes will result in few, if any, changes to the polygon delineations and standard land use coding that are the primary features of this layer. As such, the layer is being provided in its present edition for general use. As the additional attributes are evaluated, they may be added to a future edition of this data set. The basic land use features and codes, however, as mapped in this version of the data set will serve as the base 2020 LU/LC update. As stated in this metadata record's Use Constraints section, NJDEP makes no representations of any kind, including, but not limited to, the warranties of merchantability or fitness for a particular use, nor are any such warranties to be implied with respect to the digital data layers furnished hereunder. NJDEP assumes no responsibility to maintain them in any manner or form. By downloading this data, user agrees to the data use constraints listed within this metadata record.The data for Somerset County data was extracted & processed from the latest dataset by the Somerset County Office of GIS Services (SCOGIS).

ArcGIS Map Packages and GIS Data for Gillreath-Brown, Nagaoka, and Wolverton (2019)

**When using the GIS data included in these map packages, please cite all of the following:

Gillreath-Brown, Andrew, Lisa Nagaoka, and Steve Wolverton. A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, 2019. PLoSONE 14(8):e0220457. http://doi.org/10.1371/journal.pone.0220457

Gillreath-Brown, Andrew, Lisa Nagaoka, and Steve Wolverton. ArcGIS Map Packages for: A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, Gillreath-Brown et al., 2019. Version 1. Zenodo. https://doi.org/10.5281/zenodo.2572018

OVERVIEW OF CONTENTS

This repository contains map packages for Gillreath-Brown, Nagaoka, and Wolverton (2019), as well as the raw digital elevation model (DEM) and soils data, of which the analyses was based on. The map packages contain all GIS data associated with the analyses described and presented in the publication. The map packages were created in ArcGIS 10.2.2; however, the packages will work in recent versions of ArcGIS. (Note: I was able to open the packages in ArcGIS 10.6.1, when tested on February 17, 2019). The primary files contained in this repository are:

• Raw DEM and Soils data
  • Digital Elevation Model Data (Map services and data available from U.S. Geological Survey, National Geospatial Program, and can be downloaded from the National Elevation Dataset)
    • DEM_Individual_Tiles: Individual DEM tiles prior to being merged (1/3 arc second) from USGS National Elevation Dataset.
    • DEMs_Merged: DEMs were combined into one layer. Individual watersheds (i.e., Goodman, Coffey, and Crow Canyon) were clipped from this combined DEM.
  • Soils Data (Map services and data available from Natural Resources Conservation Service Web Soil Survey, U.S. Department of Agriculture)
    • Animas-Dolores_Area_Soils: Small portion of the soil mapunits cover the northeastern corner of the Coffey Watershed (CW).
    • Cortez_Area_Soils: Soils for Montezuma County, encompasses all of Goodman (GW) and Crow Canyon (CCW) watersheds, and a large portion of the Coffey watershed (CW).
• ArcGIS Map Packages
  • Goodman_Watershed_Full_SMPM_Analysis: Map Package contains the necessary files to rerun the SMPM analysis on the full Goodman Watershed (GW).
  • Goodman_Watershed_Mesa-Only_SMPM_Analysis: Map Package contains the necessary files to rerun the SMPM analysis on the mesa-only Goodman Watershed.
  • Crow_Canyon_Watershed_SMPM_Analysis: Map Package contains the necessary files to rerun the SMPM analysis on the Crow Canyon Watershed (CCW).
  • Coffey_Watershed_SMPM_Analysis: Map Package contains the necessary files to rerun the SMPM analysis on the Coffey Watershed (CW).

For additional information on contents of the map packages, please see see "Map Packages Descriptions" or open a map package in ArcGIS and go to "properties" or "map document properties."

LICENSES

Code: MIT year: 2019
Copyright holders: Andrew Gillreath-Brown, Lisa Nagaoka, and Steve Wolverton

CONTACT

Andrew Gillreath-Brown, PhD Candidate, RPA
Department of Anthropology, Washington State University
andrew.brown1234@gmail.com – Email
andrewgillreathbrown.wordpress.com – Web

The Geographic Names Information System (GNIS) was developed by the United States Geological Survey (USGS) to meet major national needs regarding geographic names and their standardization and dissemination. This dataset consists of standard report files written from the National Geographic Names Data Base, one of five data bases maintained in the GNIS. A standard format data file containing Michigan place names and geographic features such as towns, schools, reservoirs, parks, streams, valleys, springs and ridges is accompanied by a file that provides a Cross-Reference to USGS 7.5 x 7.5 minute quadrangle maps for each feature. The records in the data files are organized alphabetically by place or feature name. The other variables available in the dataset include: Federal Information Processing Standard (FIPS) state/county codes, Geographic Coordinates -- latitude and longitude to degrees, minutes, and seconds followed by a single digit alpha directional character, and a GNIS Map Code that can be used with the Cross-Reference file to provide the name of the 7.5 x 7.5 minute quadrangle map that contains that geographic feature.

Please note that this file is large, ~450 MB, and may take a substantial amount of time to download especially on slower internet connections.Shapefile (NJ State Plane NAD 1983) download: Click "Open" or Click hereFile Geodatabase (NJ State Plane NAD 1983) download: Click hereThis data represents a "generalized" version of the 2007 LULC. To improve the performance of the web applications displaying the 2002 land use data, it was necessary to create a new simplified layer that included only the minimum number of polygons and attributes needed to represent the 2002 land use conditions. The 2007 LU/LC data set is the fourth in a series of land use mapping efforts that was begun in 1986. Revisions and additions to the initial baseline layer were done in subsequent years from imagery captured in 1995/97, 2002 and 2007. This present 2007 update was created by comparing the 2002 LU/LC layer from NJ DEP's Geographical Information Systems (GIS) database to 2007 color infrared (CIR) imagery and delineating and coding areas of change. Work for this data set was done by Aerial Information Systems, Inc., Redlands, CA, under direction of the New Jersey Department of Environmental Protection (NJDEP), Bureau of Geographic Information System (BGIS). LU/LC changes were captured by adding new line work and attribute data for the 2007 land use directly to the base data layer. All 2002 LU/LC polygons and attribute fields remain in this data set, so change analysis for the period 2002-2007 can be undertaken from this one layer. The classification system used was a modified Anderson et al., classification system. An impervious surface (IS) code was also assigned to each LU/LC polygon based on the percentage of impervious surface within each polygon as of 2007. Minimum mapping unit (MMU) is 1 acre. ADVISORY: This metadata file contains information for the 2007Land Use/Land Cover (LU/LC) data sets, which were mapped by Watershed Management Area (WMA). There are additional reference documents listed in this file under Supplemental Information which should also be examined by users of these data sets. As stated in this metadata record's Use Constraints section, NJDEP makes no representations of any kind, including, but not limited to, the warranties of merchantability or fitness for a particular use, nor are any such warranties to be implied with respect to the digital data layers furnished hereunder. NJDEP assumes no responsibility to maintain them in any manner or form. By downloading this data, user agrees to the data use constraints listed within this metadata record.

This dataset is about book series. It has 1 row and is filtered where the books is Concepts and techniques of geographic information systems. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

The primary intent of this workshop is to provide practical training in using Statistics Canada geography files with the leading industry standard software: Environmental Systems Research Institute, Inc.(ESRI) ArcGIS 9x. Participants will be introduced to the key features of ArcGIS 9x, as well as to geographic concepts and principles essential to understanding and working with geographic information systems (GIS) software. The workshop will review a range of geography and attribute files available from Statistics Canada, as well as some best practices for accessing this information. A brief overview of complementary data sets available from federal and provincial agencies will be provided. There will also be an opportunity to complete a practical exercise using ArcGIS9x. (Note: Data associated with this presentation is available on the DLI FTP site under folder 1873-221.)

This data set was generated through the 2020 LU/LC update mapping effort. The 2020 update is the seventh in a series of land use mapping efforts that was begun in 1986. Revisions and additions to the initial baseline layer were done in subsequent years from imagery captured in 1995/97, 2002, 2007, 2012, 2015 and now, 2020. This present 2020 update was created by comparing the 2015 LU/LC layer from NJDEP's Geographic Information Systems (GIS) database to 2020 color infrared (CIR) imagery and delineating and coding areas of change. Work for this data set was done by Aerial Information Systems, Inc., Redlands, CA, under direction of the New Jersey Department of Environmental Protection (NJDEP), Bureau of Geographic Information System (BGIS). LU/LC changes were captured by adding new line work and attribute data for the 2020 land use directly to the base data layer. All 2015 LU/LC polygons and 2015 LU/LC coding remains in this data set, so change analysis for the period 2015-2020 can be undertaken from this one layer. The mapping was done by USGS HUC8 basins, 13 of which cover portions of New Jersey. This statewide layer is composed of the final data sets generated for each HUC8 basin. Initial QA/QC was done on each HUC8 data set as it was produced with final QA/QC and basin-to-basin edgematching done on this statewide layer. The classification system used was a modified Anderson et al., classification system. Minimum mapping unit (MMU) is 1 acre for changes to non-water and non-wetland polygons. Changes to these two categories were mapped using .25 acres as the MMU. (See entry under the Advisory section concerning additional review being done on NHD waterbody attribute coding and impervious surface estimation.) ADVISORY This data set, edition 20231120, is a statewide layer that includes updated land use/land cover data for all HUC8 basins in New Jersey. The polygon delineations and associated land use code assignments are considered the final versions for this mapping effort. Note, however, that there is continuing review being done on this layer to update several additional attributes not presently evaluated in this edition. These attributes include several from the National Hydrography Database (NHD) that are specific to the waterbodies mapped in this layer, and several attributes containing impervious surface estimates for each polygon. Evaluating the NHD codes facilitates extracting the water features mapped in this layer and using them to update the New Jersey portion of the NHD. Those NHD specific attributes are still being evaluated and may be added to a future edition of this base data set. Similarly, additional review is being done to assess the feasibility of incorporating data on impervious surface (IS) amounts generated from two independent projects, one of which was just completed by NOAA, into this base land use layer. While the NHD and IS attributes will enhance the use of this base layer in several types of analyses, this present layer can be used for doing all primary land use analyses without having those attributes evaluated. Further, evaluating these extra attributes will result in few, if any, changes to the polygon delineations and standard land use coding that are the primary features of this layer. As such, the layer is being provided in its present edition for general use. As the additional attributes are evaluated, they may be added to a future edition of this data set. The basic land use features and codes, however, as mapped in this version of the data set will serve as the base 2020 LU/LC update. As stated in this metadata record's Use Constraints section, NJDEP makes no representations of any kind, including, but not limited to, the warranties of merchantability or fitness for a particular use, nor are any such warranties to be implied with respect to the digital data layers furnished hereunder. NJDEP assumes no responsibility to maintain them in any manner or form. By downloading this data, user agrees to the data use constraints listed within this metadata record.The data for Somerset County data was extracted & processed from the latest dataset by the Somerset County Office of GIS Services (SCOGIS).

Based on open access data, 79 Mediterranean passenger ports are analyzed to compare their infrastructure, hinterland accessibility and offered multi-modality categories. Comparative Geo-spatial analysis is also carried out by using the data normalization method in order to visualize the ports' performance on maps. These data driven comprehensive analytical results can bring added value to sustainable development policy and planning initiatives in the Mediterranean Region. The analyzed elements can be also contributed to the development of passenger port performance indicators. The empirical research methods used for the Mediterranean passenger ports can be replicated for transport nodes of any region around the world to determine their relative performance on selected criteria for improvement and planning.

The Mediterranean passenger ports were initially categorized into cruise and ferry ports. The cruise ports were identified from the member list of the Association for the Mediterranean Cruise Ports (MedCruise), representing more than 80% of the cruise tourism activities per country. The identified cruise ports were mapped by selecting the corresponding geo-referenced ports from the map layer developed by the European Marine Observation and Data Network (EMODnet). The United Nations (UN) Code for Trade and Transport Locations (LOCODE) was identified for each of the cruise ports as the common criteria to carry out the selection. The identified cruise ports not listed by the EMODnet were added to the geo-database by using under license the editing function of the ArcMap (version 10.1) geographic information system software. The ferry ports were identified from the open access industry initiative data provided by the Ferrylines, and were mapped in a similar way as the cruise ports (Figure 1).

Based on the available data from the identified cruise ports, a database (see Table A1–A3) was created for a Mediterranean scale analysis. The ferry ports were excluded due to the unavailability of relevant information on selected criteria (Table 2). However, the cruise ports serving as ferry passenger ports were identified in order to maximize the scope of the analysis. Port infrastructure and hinterland accessibility data were collected from the statistical reports published by the MedCruise, which are a compilation of data provided by its individual member port authorities and the cruise terminal operators. Other supplementary sources were the European Sea Ports Organization (ESPO) and the Global Ports Holding, a cruise terminal operator with an established presence in the Mediterranean. Additionally, open access data sources (e.g. the Google Maps and Trip Advisor) were consulted in order to identify the multi-modal transports and bridge the data gaps on hinterland accessibility by measuring the approximate distances.

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.

How to cite

When you use the datasets or maps, please also cite to the following paper introducing the whole of process from data collection, harmonization and visualization until releasing the data:

Rantanen, T., Tolvanen, H., Roose, M., Ylikoski, J. & Vesakoski, O. (2022) “Best practices for spatial language data harmonization, sharing and map creation - A case study of Uralic” PLoS ONE 17(6): e0269648. https://doi.org/10.1371/journal.pone.0269648.

Overview

The Geographical database of the Uralic languages consists of past and current distributions of the Uralic languages both as the original digital spatial datasets and as finalized maps. The database has been collected by the interdisciplinary BEDLAN (Biological Evolution and Diversification of LANguages) research team in collaboration with experts of Uralic languages. The work has been financed by the University of Turku (UTU–BGG), Kone Foundation (UraLex, AikaSyyni), the Academy of Finland (URKO), UiT – The Arctic University of Norway and the University of Oulu, as well as the Finno-Ugrian Society. The data have been compiled for the purposes of doing spatial linguistic and multidisciplinary research, and to visually present the state-of-the-art knowledge of the Uralic languages and their dialects. Geographic distributions are visualized as vector data primarily by using polygon objects (speaker areas or language areas), and in some rare cases, by using points. Based on the language distributions, coordinates for the languages and their dialects (point locations) have also been defined.

WARNING: This is a pre-release dataset and its fields names and data structures are subject to change. It should be considered pre-release until the end of 2024. Expected changes:

• Metadata is missing or incomplete for some layers at this time and will be continuously improved.
  
• We expect to update this layer roughly in line with CDTFA at some point, but will increase the update cadence over time as we are able to automate the final pieces of the process.

Purpose

County and incorporated place (city) boundaries along with third party identifiers used to join in external data. Boundaries are from the authoritative source the California Department of Tax and Fee Administration (CDTFA), altered to show the counties as one polygon. This layer displays the city polygons on top of the County polygons so the area isn"t interrupted. The GEOID attribute information is added from the US Census. GEOID is based on merged State and County FIPS codes for the Counties. Abbreviations for Counties and Cities were added from Caltrans Division of Local Assistance (DLA) data. Place Type was populated with information extracted from the Census. Names and IDs from the US Board on Geographic Names (BGN), the authoritative source of place names as published in the Geographic Name Information System (GNIS), are attached as well. Finally, the coastline is used to separate coastal buffers from the land-based portions of jurisdictions. This feature layer is for public use.

Related Layers

This dataset is part of a grouping of many datasets:

1. Cities: Only the city boundaries and attributes, without any unincorporated areas
   • With Coastal Buffers
   • Without Coastal Buffers
2. Counties: Full county boundaries and attributes, including all cities within as a single polygon
   • With Coastal Buffers
   • Without Coastal Buffers
3. Cities and Full Counties: A merge of the other two layers, so polygons overlap within city boundaries. Some customers require this behavior, so we provide it as a separate service.
   • With Coastal Buffers (this dataset)
   • Without Coastal Buffers
4. Place Abbreviations
5. Unincorporated Areas (Coming Soon)
6. Census Designated Places (Coming Soon)
   
7. Cartographic Coastline
   • Polygon
   • Line source (Coming Soon)

Point of Contact

California Department of Technology, Office of Digital Services, odsdataservices@state.ca.gov

Field and Abbreviation Definitions

• COPRI: county number followed by the 3-digit city primary number used in the Board of Equalization"s 6-digit tax rate area numbering system
• Place Name: CDTFA incorporated (city) or county name
• County: CDTFA county name. For counties, this will be the name of the polygon itself. For cities, it is the name of the county the city polygon is within.
• Legal Place Name: Board on Geographic Names authorized nomenclature for area names published in the Geographic Name Information System
• GNIS_ID: The numeric identifier from the Board on Geographic Names that can be used to join these boundaries to other datasets utilizing this identifier.
• GEOID: numeric geographic identifiers from the US Census Bureau Place Type: Board on Geographic Names authorized nomenclature for boundary type published in the Geographic Name Information System
• Place Abbr: CalTrans Division of Local Assistance abbreviations of incorporated area names
• CNTY Abbr: CalTrans Division of Local Assistance abbreviations of county names
• Area_SqMi: The area of the administrative unit (city or county) in square miles, calculated in EPSG 3310 California Teale Albers.
• COASTAL: Indicates if the polygon is a coastal buffer. Null for land polygons. Additional values include "ocean" and "bay".
• GlobalID: While all of the layers we provide in this dataset include a GlobalID field with unique values, we do not recommend you make any use of it. The GlobalID field exists to support offline sync, but is not persistent, so data keyed to it will be orphaned at our next update. Use one of the other persistent identifiers, such as GNIS_ID or GEOID instead.

Accuracy

CDTFA"s source data notes the following about accuracy:

City boundary changes and county boundary line adjustments filed with the Board of Equalization per Government Code 54900. This GIS layer contains the boundaries of the unincorporated county and incorporated cities within the state of California. The initial dataset was created in March of 2015 and was based on the State Board of Equalization tax rate area boundaries. As of April 1, 2024, the maintenance of this dataset is provided by the California Department of Tax and Fee Administration for the purpose of determining sales and use tax rates. The boundaries are continuously being revised to align with aerial imagery when areas of conflict are discovered between the original boundary provided by the California State Board of Equalization and the boundary made publicly available by local, state, and federal government. Some differences may occur between actual recorded boundaries and the boundaries used for sales and use tax purposes. The boundaries in this map are representations of taxing jurisdictions for the purpose of determining sales and use tax rates and should not be used to determine precise city or county boundary line locations. COUNTY = county name; CITY = city name or unincorporated

The 2015 LU/LC data set is the sixth in a series of land use mapping efforts that was begun in 1986. Revisions and additions to the initial baseline layer were done in subsequent years from imagery captured in 1995/97, 2002, 2007, 2012 and 2015. This present 2015 update was created by comparing the 2012 LU/LC layer from NJDEP's Geographic Information Systems (GIS) database to 2015 color infrared (CIR) imagery and delineating and coding areas of change. Work for this data set was done by Aerial Information Systems, Inc., Redlands, CA, under direction of the New Jersey Department of Environmental Protection (NJDEP), Bureau of Geographic Information System (BGIS). LU/LC changes were captured by adding new line work and attribute data for the 2015 land use directly to the base data layer. All 2012 LU/LC polygons and attribute fields remain in this data set, so change analysis for the period 2012-2015 can be undertaken from this one layer. The classification system used was a modified Anderson et al., classification system. An impervious surface (IS) code was also assigned to each LU/LC polygon based on the percentage of impervious surface within each polygon as of 2015. Minimum mapping unit (MMU) is 1 acre. ADVISORY: This metadata file contains information for the 2015 Land Use/Land Cover (LU/LC) data sets, which were mapped by USGS Subbasin (HU8). There are additional reference documents listed in this file under Supplemental Information which should also be examined by users of these data sets. As stated in this metadata record's Use Constraints section, NJDEP makes no representations of any kind, including, but not limited to, the warranties of merchantability or fitness for a particular use, nor are any such warranties to be implied with respect to the digital data layers furnished hereunder. NJDEP assumes no responsibility to maintain them in any manner or form. By downloading this data, user agrees to the data use constraints listed within this metadata record.

This data set was generated through the 2020 LU/LC update mapping effort. The 2020 update is the seventh in a series of land use mapping efforts that was begun in 1986. Revisions and additions to the initial baseline layer were done in subsequent years from imagery captured in 1995/97, 2002, 2007, 2012, 2015 and now, 2020. This present 2020 update was created by comparing the 2015 LU/LC layer from NJDEP's Geographic Information Systems (GIS) database to 2020 color infrared (CIR) imagery and delineating and coding areas of change. Work for this data set was done by Aerial Information Systems, Inc., Redlands, CA, under direction of the New Jersey Department of Environmental Protection (NJDEP), Bureau of Geographic Information System (BGIS). LU/LC changes were captured by adding new line work and attribute data for the 2020 land use directly to the base data layer. All 2015 LU/LC polygons and 2015 LU/LC coding remains in this data set, so change analysis for the period 2015-2020 can be undertaken from this one layer. The mapping was done by USGS HUC8 basins, 13 of which cover portions of New Jersey. This statewide layer is composed of the final data sets generated for each HUC8 basin. Initial QA/QC was done on each HUC8 data set as it was produced with final QA/QC and basin-to-basin edgematching done on this statewide layer. The classification system used was a modified Anderson et al., classification system. Minimum mapping unit (MMU) is 1 acre for changes to non-water and non-wetland polygons. Changes to these two categories were mapped using .25 acres as the MMU. This data set, edition 20241101, is a statewide layer that includes updated land use/land cover data for all HUC8 basins in New Jersey. The polygon delineations and associated land use code assignments are considered the final versions for this mapping effort. Note, this edition includes additional attributes from the National Hydrography Database (NHD) that are specific to the waterbodies mapped in this layer, and several attributes containing impervious surface estimates for each polygon. Evaluating the NHD codes facilitates extracting the water features mapped in this layer and using them to update the New Jersey portion of the NHD. Similarly, impervious surface (IS) amounts generated from two independent projects, one of which was just completed by NOAA, have been incorporated into this base land use layer. While the NHD and IS attributes will enhance the use of this base layer in several types of analyses, this present layer can be used for doing all primary land use analyses without having those attributes evaluated. Further, evaluating these extra attributes will result in few, if any, changes to the polygon delineations and standard land use coding that are the primary features of this layer. As such, the layer is being provided in its present edition for general use. The basic land use features and codes, however, as mapped in this version of the data set will serve as the base 2020 LU/LC update. As stated in this metadata record's Use Constraints section, NJDEP makes no representations of any kind, including, but not limited to, the warranties of merchantability or fitness for a particular use, nor are any such warranties to be implied with respect to the digital data layers furnished hereunder. NJDEP assumes no responsibility to maintain them in any manner or form. By downloading this data, user agrees to the data use constraints listed within this metadata record.

The use of social media and location-based networks through GPS-enabled devices provides geospatial data for a plethora of applications in urban studies. However, the extent to which information found in geo-tagged social media activity corresponds to the spatial context is still a topic of debate. In this article, we developed a framework aimed at retrieving the thematic and spatial relationships between content originated from space-based (Twitter) and place-based (Google Places and OSM) sources of geographic user-generated content based on topics identified by the embedding-based BERTopic model. The contribution of the framework lies on the combination of methods that were selected to improve previous works focused on content-location relationships. Using the city of Lisbon (Portugal) to test our methodology, we first applied the embedding-based topic model to aggregated textual data coming from each source. Results of the analysis evidenced the complexity of content-location relationships, which are mostly based on thematic profiles. Nonetheless, the framework can be employed in other cities and extended with other metrics to enrich the research aimed at exploring the correlation between online discourse and geography.

Dissertation and dataset present an archaeological study of the Huarmey Valley region, located on the northern coast of Peru. My work uses modern and innovative digital methods. My research focuses on better understanding the location of one of the most important sites in the valley, Castillo de Huarmey, by learning about the context in which it functioned. The Imperial Mausoleum located at the site, along with the burial chamber beneath it, is considered one of the most important discoveries regarding the Wari culture in recent years.In the dissertation, I address issues concerning both the location of the site on a macro scale - in the entire Huarmey Valley, on a micro scale - the context of the Huarmey Valley delta – and the spatial relationships within the burial chamber located beneath the Mausoleum. I ask the questions (i) How did Castillo de Huarmey communicate with other sites dated to the same period located in the valley and also in adjacent valleys? Did this influence its role in the region? (ii) Is the Mausoleum at Castillo de Huarmey located intentionally and what was the meaning of this location at the micro and macro scale? (iii) What spatial relations existed between Castillo de Huarmey and other sites from the same period? (iv) Does the position of the artifacts, found in situ in the burial chamber, show important relationships between buried individuals? (v) Does spatial analysis show interesting spatial patterns within the burial inside the chamber?The questions can be answered by describing and testing the digital methods proposed in the doctoral dissertation related to both field data collection and their analysis and interpretation. These methods were selected and adapted to a specific area (the Northern Coast of Peru) and to the objective of answering the questions posed in the thesis. The wide range of digital methods used in archaeology is made possible by the use of Geographic Information Systems (abbreviated GIS) in research. To date, GIS in archaeology is used in three aspects (Wheatley and Gillings 2002): (i) statistical and spatial analysis to obtain new information, (ii) landscape archaeology, and (iii) Cultural Resource Management.My dissertation is divided into three main components that discuss the types of digital methods used in archaeology. The division of these methods will be adapted to the level of detail of the research (from the location of the site in the region, to the delta of the Huarmey Valley, to the burial chamber of the Mausoleum) and to the way they are used in archaeology (from Cultural Resource Management, to archaeological landscape analysis, to statistical-spatial analysis). One of the aims of the dissertation is to show the methodological path of the use of digital methods, i.e. from the acquisition of data in the field, through analysis, to their interpretation in a cultural context. However, the main objective of my research is to interpret the spatial relationships from the macro to the micro level, in the case described, against the background of other sites located in the valley, the location of Castillo de Huarmey in the context of the valley delta, and finally to the burial chamber of the Mausoleum. The uniqueness of the described burial makes the research and its results pioneering in nature.As a final result of my work I would like to determine whether relationships can be demonstrated between the women buried in the burial chamber and whether the location of particular categories of artifacts can illustrate specific spatial patterns of burial. Furthermore, my goal is to attempt to understand the relationship between the Imperial Mausoleum and other sites (archival as well as newly discovered) located in the Huarmey Valley and to understand the role of the site's location.Published dataset represents, described in the dissertation, mobile GIS survey on the site PV35-5 created in Survey123, ESRI application; xml and xls used for creating the survey that was used during the research of the site, as well as the results of the survey published in ArcGIS Pro package. The package includes collected data as points, saved as .shp, as well as ortophotomaps (as geotiff) and Digital Elevation Model and hillshade of PV35-5. The published dataset represents part of the dissertation describing archaeological landscape analysis of Huarmey Valley’s delta.