This map shows density surfaces derived from the 2010 US Census block points.This data shows % of people who identified themselves as single race and whiteThe block points were interpolated using the density function to a 2km x 2km grid of the continental US (with water and coastal data masks). There are many stories in these Maps:- What is that clean North/South Line through the center? Why do so many people live East of that line?- Notice the paths of the towns in the west – why are they so linear? And it seems there is a pattern to the spaces between the towns, why?- Looking at the ethnic maps, what explains the patterns? Look at the % Native American map – what are the areas of higher values? (note I did not make a % Asian map as at this scale there was not enough % to show any significant clusters.)

Estimated density of people per grid-cell, approximately 1km (0.008333 degrees) resolution. The units are number of people per Km² per pixel, expressed as unit: "ppl/Km²". The mapping approach is Random Forest-based dasymetric redistribution. The WorldPop project was initiated in October 2013 to combine the AfriPop, AsiaPop and AmeriPop population mapping projects. It aims to provide an open access archive of spatial demographic datasets for Central and South America, Africa and Asia to support development, disaster response and health applications. The methods used are designed with full open access and operational application in mind, using transparent, fully documented and peer-reviewed methods to produce easily updatable maps with accompanying metadata and measures of uncertainty. Acknowledgements information at https://www.worldpop.org/acknowledgements

The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in American Samoa: (1) Overall population density (2) Women (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of reproductive age (ages 15-49).

This map uses dot density patterns to indicate which population is larger in each area: urban (green) or rural (blue). Data is from the U.S. Census Bureau’s 2020 Census Demographic and Housing Characteristics. The map's layers contain total population counts by sex, age, and race groups for Nation, State, County, Census Tract, and Block Group in the United States and Puerto Rico.The U.S. Census designates each census block as part of an urban area or as rural. Larger geographies in this map such as block group, tract, county and state can therefore have a mix of urban and rural population. This map illustrates the 100% urban areas with all green dots, and 100% rural areas in dark blue dots. Areas with mixed urban/rural population have a proportional mix of green and blue dots to give a visual indication of where change may be happening. From the Census:"The Census Bureau’s urban-rural classification is a delineation of geographic areas, identifying both individual urban areas and the rural area of the nation. The Census Bureau’s urban areas represent densely developed territory, and encompass residential, commercial, and other non-residential urban land uses. The Census Bureau delineates urban areas after each decennial census by applying specified criteria to decennial census and other data. Rural encompasses all population, housing, and territory not included within an urban area.For the 2020 Census, an urban area will comprise a densely settled core of census blocks that meet minimum housing unit density and/or population density requirements. This includes adjacent territory containing non-residential urban land uses. To qualify as an urban area, the territory identified according to criteria must encompass at least 2,000 housing units or have a population of at least 5,000." SourceAbout the dataYou can use this map as is and you can also modify it to use other attributes included in its layers. This map's layers contain total population counts by sex, age, and race groups data from the 2020 Census Demographic and Housing Characteristics. This is shown by Nation, State, County, Census Tract, Block Group boundaries. Each geography layer contains a common set of Census counts based on available attributes from the U.S. Census Bureau. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis.Vintage of boundaries and attributes: 2020 Demographic and Housing Characteristics Table(s): P1, H1, H3, P2, P3, P5, P12, P13, P17, PCT12 (Not all lines of these DHC tables are available in this feature layer.)Data downloaded from: U.S. Census Bureau’s data.census.gov siteDate the Data was Downloaded: May 25, 2023Geography Levels included: Nation, State, County, Census Tract, Block GroupNational Figures: included in Nation layer The United States Census Bureau Demographic and Housing Characteristics: 2020 Census Results 2020 Census Data Quality Geography & 2020 Census Technical Documentation Data Table Guide: includes the final list of tables, lowest level of geography by table and table shells for the Demographic Profile and Demographic and Housing Characteristics.News & Updates This map is ready to be used in ArcGIS Pro, ArcGIS Online and its configurable apps, Story Maps, dashboards, Notebooks, Python, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the U.S. Census Bureau when using this data. Data Processing Notes: These 2020 Census boundaries come from the US Census TIGER geodatabases. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For Census tracts and block groups, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract and block group boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2020 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are unchanged and available as attributes within the data table (units are square meters).  The layer contains all US states, Washington D.C., and Puerto Rico. Census tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99). Block groups that fall within the same criteria (Block Group denoted as 0 with no area land) have also been removed.Percentages and derived counts, are calculated values (that can be identified by the "_calc_" stub in the field name). Field alias names were created based on the Table Shells file available from the Data Table Guide for the Demographic Profile and Demographic and Housing Characteristics. Not all lines of all tables listed above are included in this layer. Duplicative counts were dropped. For example, P0030001 was dropped, as it is duplicative of P0010001.To protect the privacy and confidentiality of respondents, their data has been protected using differential privacy techniques by the U.S. Census Bureau.

As of 2025, Asia was the most densely populated region of the world, with nearly 156 inhabitants per square kilometer, whereas Oceania's population density was just over five inhabitants per square kilometer.

In the middle of 2023, about 60 percent of the global population was living in Asia.The total world population amounted to 8.1 billion people on the planet. In other words 4.7 billion people were living in Asia as of 2023. Global populationDue to medical advances, better living conditions and the increase of agricultural productivity, the world population increased rapidly over the past century, and is expected to continue to grow. After reaching eight billion in 2023, the global population is estimated to pass 10 billion by 2060. Africa expected to drive population increase Most of the future population increase is expected to happen in Africa. The countries with the highest population growth rate in 2024 were mostly African countries. While around 1.47 billion people live on the continent as of 2024, this is forecast to grow to 3.9 billion by 2100. This is underlined by the fact that most of the countries wit the highest population growth rate are found in Africa. The growing population, in combination with climate change, puts increasing pressure on the world's resources.

Data are derived from generalized linear models and model selection techniques using 129 estimates of population density of wild pigs (Sus scrofa) from 5 continents. Models were used to determine the strength of association among a diverse set of biotic and abiotic factors associated with wild pig population dynamics. The models and associated factors were used to predict the potential population density of wild pigs at the 1 km resolution. Predictions were then compared with available population estimates for wild pigs on their native range in North America indicating the predicted densities are within observed values. See Lewis et al (2017) and Lewis et al (2019) for more information.Lewis, Jesse S., Matthew L. Farnsworth, Chris L. Burdett, David M. Theobald, Miranda Gray, and Ryan S. Miller. "Biotic and abiotic factors predicting the global distribution and population density of an invasive large mammal." Scientific reports7 (2017): 44152.Lewis, Jesse S., Joseph L. Corn, John J. Mayer, Thomas R. Jordan, Matthew L. Farnsworth, Christopher L. Burdett, Kurt C. VerCauteren, Steven J. Sweeney, and Ryan S. Miller. "Historical, current, and potential population size estimates of invasive wild pigs (Sus scrofa) in the United States." Biological Invasions21, no. 7 (2019): 2373-2384.

These maps represent a modeled distribution of population based on the nominal censuses of the town of Curitiba and adjacent towns. Classification of data is normalized for each category to allow comparison between different periods of time.

Abundance measures are almost non-existent for several bird species threatened with extinction, particularly range-restricted Neotropical taxa, for which estimating population sizes can be challenging. Here we use data collected over nine years to explore the abundance of 11 endemic birds from the Sierra Nevada de Santa Marta (SNSM), one of Earth’s most irreplaceable ecosystems. We established 99 transects in the “Cuchilla de San Lorenzo†Important Bird Area within native forest, early successional vegetation, and areas of transformed vegetation by human activities. A total of 763 bird counts were carried out covering the entire elevation range in the study area (~175–2650 m). We applied hierarchical distance-sampling models to assess elevation- and habitat-related variation in local abundance and obtain values of population density and total and effective population size. Most species were more abundant in the montane elevational range (1800–2650 m). Habitat-related differences in abun..., , , # Data from: Abundance models of endemic birds of the Sierra Nevada de Santa Marta, northern South America, suggest small population sizes and dependence on montane elevations

MS Reference Number: ORNITH-APP-23-061R2 Dataset name: Abundance_models_priority_endemics_SNSM.xlsx

The whole dataset contains data for fitting hierarchical distance-sampling models for priority, endemic bird species from the Sierra Nevada de Santa Marta, northern Colombia. Models were used to assess elevation- and habitat-related variation in local abundance and obtain values of population density and total and effective population size for the study species. Details on other methods used for estimating extent of presence (EOP) and area of occupancy (AOO), and for generating abundance maps are provided in the manuscript and the supplementary material file that accompanies it. Abundance maps will be uploaded as distribution hypothesis for each species to the BioModelos online platform (Velásquez-Tibatá et al. ...

This dataset shows all 336 municipalities in Venezuela, which correspond to second-level administrative divisions currently used in said country.

The Excel file includes filters for each column.

Column Description

• Municipality: Name of the municipality
• Capital: Capital city of the municipality
• State: State the municipality belongs to
• Map: Map of the municipality within the state it belongs to
• Population (2023): Population of the municipality as of 2023
• Area (squared km): Total land area of the municipality
• Population density (people per sq. km): Population per square kilometer
• Region: Administrative region the municipality belongs to

NOTE: Population numbers are estimates and may not reflect reality with full precision.

This dataset shows all 515 departments in Argentina, which correspond to second-level administrative divisions currently used in said country.

The Excel file includes filters for each column.

Column Description

• Department: Name of the department
• Capital: Capital city of the department
• Province: Province the department belongs to
• Map: Map of the department within the province it belongs to
• Population (2022): Population of the department as of 2022
• Area (squared km): Total land area of the department
• Population density (people per sq. km): Population per square kilometer

NOTES - Within the province of Buenos Aires, departments are not referred to as such, but as "partidos". - There are 135 partidos in the province of Buenos Aires, the other 380 second-level administrative divisions correspond to "departamentos" (departments) spread throughout the rest of the country. - The city of Buenos Aires is classified as "ciudad autónoma" (autonomous city), meaning that it is a separate department in itself.

Data from the Oak Ridge National Laboratory, LandScan Global Population 1998 Database. Estimates for rural population were obtained by excluding the Urban Population Areas. This was achieved by removing settled and partly settled grid cells from the Landcover Dataset and removing(limiting) propulation density figures to produce a net result which approximates the known rural population. Data-set has been exported as Binary format.

This layer shows Population. This is shown by state and county boundaries. This service contains the 2018-2022 release of data from the American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the point by Population Density and size of the point by Total Population. The size of the symbol represents the total count of housing units. Population Density was calculated based on the total population and area of land fields, which both came from the U.S. Census Bureau. Formula used for Calculating the Pop Density (B01001_001E/GEO_LAND_AREA_SQ_KM). To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2018-2022ACS Table(s): B01001, B09020Data downloaded from: Census Bureau's API for American Community Survey Date of API call: January 18, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:Boundaries come from the Cartographic Boundaries via US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates, and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto Rico. The Counties (and equivalent) layer contains 3221 records - all counties and equivalent, Washington D.C., and Puerto Rico municipios. See Areas Published. Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells.Margin of error (MOE) values of -555555555 in the API (or "*****" (five asterisks) on data.census.gov) are displayed as 0 in this dataset. The estimates associated with these MOEs have been controlled to independent counts in the ACS weighting and have zero sampling error. So, the MOEs are effectively zeroes, and are treated as zeroes in MOE calculations. Other negative values on the API, such as -222222222, -666666666, -888888888, and -999999999, all represent estimates or MOEs that can't be calculated or can't be published, usually due to small sample sizes. All of these are rendered in this dataset as null (blank) values.

Race is a social and historical construct, and the racial categories counted by the census change over time so the process of constructing stable racial categories for these 50 years out of census data required complex and imperfect decisions. Here we have used historical research on early 20th century southern California to construct historic racial categories from the IPUMS full count data, which allows us to track groups that were not formally classified as racial groups in some census decades like Mexican, but which were important racial categories in southern California. Detailed explanation of how we constructed these categories and the rationale we used for the decisions we made can be found here. Layers are symbolized to show the percentage of each of the following groups from 1900-1940:AmericanIndian Not-Hispanic, AmericanIndian Hispanic, Black non-Hispanic, Black-Hispanic, Chinese, Korean, Filipino and Japanese, Mexican, Hispanic Not-Mexican, white non-Hispanic. The IPUMS Census data is messy and includes some errors and undercounts, making it hard to map some smaller populations, like Asian Indians (in census called Hindu in 1920) and creating a possible undercount of Native American populations. The race data mapped here also includes categories that may not have been socially meaningful at the time like Black-Hispanic, which generally would represent people from Mexico who the census enumerator classified as Black because of their dark skin, but who were likely simply part of Mexican communities at the time. We have included maps of the Hispanic not-Mexican category which shows very small numbers of non-Mexican Hispanic population, and American Indian Hispanic, which often captures people who would have been listed as Indian in the census, probably because of skin color, but had ancestry from Mexico (or another Hispanic country). This category may include some indigenous Californians who married into or assimilated into Mexican American communities in the early 20th century. If you are interested in mapping some of the other racial or ethnic groups in the early 20th century, you can explore and map the full range of variables we have created in the People's History of the IE IE_ED1900-1940 Race Hispanic Marriage and Age Feature layer.Suggested Citation: Tilton, Jennifer. People's History Race Ethnicity Dot Density Map 1900-1940. A People's History of the Inland Empire Census Project 1900-1940 using IPUMS Ancestry Full Count Data. Program in Race and Ethnic Studies University of Redlands, Center for Spatial Studies University of Redlands, UCR Public History. 2023. 2025Feature Layer CitationTilton, Jennifer, Tessa VanRy & Lisa Benvenuti. Race and Demographic Data 1900-1940. A People's History of the Inland Empire Census Project 1900-1940 using IPUMS Ancestry Full Count Data. Program in Race and Ethnic Studies University of Redlands, Center for Spatial Studies University of Redlands, UCR Public History. 2023. Additional contributing authors: Mackenzie Nelson, Will Blach & Andy Garcia Funding provided by: People’s History of the IE: Storyscapes of Race, Place, and Queer Space in Southern California with funding from NEH-SSRC Grant 2022-2023 & California State Parks grant to Relevancy & History. Source for Census Data 1900- 1940 Ruggles, Steven, Catherine A. Fitch, Ronald Goeken, J. David Hacker, Matt A. Nelson, Evan Roberts, Megan Schouweiler, and Matthew Sobek. IPUMS Ancestry Full Count Data: Version 3.0 [dataset]. Minneapolis, MN: IPUMS, 2021. Primary Sources for Enumeration District Linework 1900-1940 Steve Morse provided the full list of transcribed EDs for all 5 decades "United States Enumeration District Maps for the Twelfth through the Sixteenth US Censuses, 1900-1940." Images. FamilySearch. https://FamilySearch.org: 9 February 2023. Citing NARA microfilm publication A3378. Washington, D.C.: National Archives and Records Administration, 2003. BLM PLSS Map Additional Historical Sources consulted include: San Bernardino City Annexation GIS Map Redlands City Charter Proposed with Ward boundaries (Not passed) 1902. Courtesy of Redlands City Clerk. Redlands Election Code Precincts 1908, City Ordinances of the City of Redlands, p. 19-22. Courtesy of Redlands City Clerk Riverside City Charter 1907 (for 1910 linework) courtesy of Riverside City Clerk. 1900-1940 Raw Census files for specific EDs, to confirm boundaries when needed, accessed through Family Search. If you have additional questions or comments, please contact jennifer_tilton@redlands.edu.

These maps represent a modeled distribution of population based on the nominal censuses of the town of Curitiba and adjacent towns, providing a snapshot of the demographic situation in a specific year. Classification of data is normalized for each year to allow comparison between different categories of data for the same year.

"""

County-Level Demographic: Population, Race, Gender

Overview

This dataset provides a detailed breakdown of demographic information for counties across the United States, derived from the U.S. Census Bureau's 2023 American Community Survey (ACS). The data includes population counts by gender, race, and ethnicity, alongside unique identifiers for each county using State and County FIPS codes.

Dataset Features

The dataset includes the following columns: - County: Name of the county. - State: Name of the state the county belongs to. - State FIPS Code: Federal Information Processing Standard (FIPS) code for the state. - County FIPS Code: FIPS code for the county. - FIPS: Combined State and County FIPS codes, a unique identifier for each county. - Total Population: Total population in the county. - Male Population: Number of males in the county. - Female Population: Number of females in the county. - Total Race Responses: Total race-related responses recorded in the survey. - White Alone: Number of individuals identifying as White alone. - Black or African American Alone: Number of individuals identifying as Black or African American alone. - Hispanic or Latino: Number of individuals identifying as Hispanic or Latino.

Processing Methodology

1. Source:
   • Data was retrieved using the U.S. Census Bureau ACS API.
2. County-Level Aggregation:
   • Each county is uniquely identified using State FIPS Code and County FIPS Code.
   • These codes were concatenated to form the unified FIPS column.
3. Data Cleaning:
   • All numeric columns were converted to appropriate data types.
   • County and state names were extracted from the raw NAME field for clarity.

Why Use This Dataset?

This dataset is highly versatile and suitable for: - Demographic Analysis: - Analyze population distribution by gender, race, and ethnicity. - Geographic Studies: - Use FIPS codes to map counties geographically. - Data Visualizations: - Create visual insights into demographic trends across counties.

File Format

• The dataset is available as a CSV file with 3,000+ rows (one for each county).

Licensing

• Source: Data is sourced from the U.S. Census Bureau's 2023 American Community Survey (ACS).
• License: This dataset is in the public domain and provided under the U.S. Census Bureau’s terms of use. Attribution to the Census Bureau is appreciated.

Acknowledgments

Special thanks to the U.S. Census Bureau for making this data publicly available and to the Kaggle community for fostering a collaborative space for data analysis and exploration. """

Map containing historical census data from 1900 - 2000 throughout the western United States at the county level. Data includes total population, population density, and percent population change by decade for each county. Population data was obtained from the US Census Bureau and joined to 1:2,000,000 scale National Atlas counties shapefile.

The world's population first reached one billion people in 1805, and reached eight billion in 2022, and will peak at almost 10.2 billion by the end of the century. Although it took thousands of years to reach one billion people, it did so at the beginning of a phenomenon known as the demographic transition; from this point onwards, population growth has skyrocketed, and since the 1960s the population has increased by one billion people every 12 to 15 years. The demographic transition sees a sharp drop in mortality due to factors such as vaccination, sanitation, and improved food supply; the population boom that follows is due to increased survival rates among children and higher life expectancy among the general population; and fertility then drops in response to this population growth. Regional differences The demographic transition is a global phenomenon, but it has taken place at different times across the world. The industrialized countries of Europe and North America were the first to go through this process, followed by some states in the Western Pacific. Latin America's population then began growing at the turn of the 20th century, but the most significant period of global population growth occurred as Asia progressed in the late-1900s. As of the early 21st century, almost two-thirds of the world's population lives in Asia, although this is set to change significantly in the coming decades. Future growth The growth of Africa's population, particularly in Sub-Saharan Africa, will have the largest impact on global demographics in this century. From 2000 to 2100, it is expected that Africa's population will have increased by a factor of almost five. It overtook Europe in size in the late 1990s, and overtook the Americas a few years later. In contrast to Africa, Europe's population is now in decline, as birth rates are consistently below death rates in many countries, especially in the south and east, resulting in natural population decline. Similarly, the population of the Americas and Asia are expected to go into decline in the second half of this century, and only Oceania's population will still be growing alongside Africa. By 2100, the world's population will have over three billion more than today, with the vast majority of this concentrated in Africa. Demographers predict that climate change is exacerbating many of the challenges that currently hinder progress in Africa, such as political and food instability; if Africa's transition is prolonged, then it may result in further population growth that would place a strain on the region's resources, however, curbing this growth earlier would alleviate some of the pressure created by climate change.

Description

This dataset includes the inputs and outputs generated in the spatial modeling of CES using social media data for eight mountain parks in Spain and Portugal (Aigüestortes, Sierra de Guadarrama, Ordesa, Peneda-Gerês, Picos de Europa, Sierra de las Nieves, Sierra Nevada and Teide). This spatial modeling is addressed in the article in preparation entitled: "What drives cultural ecosystem services in mountain protected areas? An AI-assisted answer using social media."

The variables used as inputs to generate the models come from different sources:

-CES presence points come from social media photos (Flickr and Twitter) labeled using AI models and validated by experts. The models used for automatic labeling were Dino v2 and OPENAI's GPT 4.1 model. Consensus was sought on the labels from these two label sources, which showed F1 values above 0.75, and these labels were used as presence data.

The environmental variables used are mainly derived from:

• OpenStreetMap (OSM) https://www.openstreetmap.org/

• Variables derived from remote sensing

• Topographic variables

• Current and future climate variables derived from CHELSA (https://chelsa-climate.org/)

• Landscape metrics (calculated with Fragstats software https://www.fragstats.org/)

• Viewshed

• Land use and land cover maps (https://land.copernicus.eu/en/products/corine-land-cover)

The models were generated with the maximum entropy (MaxEnt) algorithm using the biomod2 R package, leveraging its suitability for presence-only data, low sample sizes, and mixed predictor types. To address sampling bias, we generated 10 pseudo-absence replicates based on the “target-group background” method. Models were evaluated using AUC-ROC and True Skill Statistic (TSS), with performance validation via 10-fold cross-validation, resulting in 100 runs per model. Ensemble models were created from runs with AUC-ROC > 0.6, using the median for spatial projections of CES and the coefficient of variation to estimate uncertainty. We implemented two modelling approaches: one assuming consistent CES preferences across parks, and another assuming park-specific preferences shaped by local environmental contexts.

Table 1. Categories used in social media photo tagging: Stoten, based on the scientific framework proposed by Moreno-Llorca et al. (2020) (https://doi.org/10.1016/j.scitotenv.2020.140067).

Stoten

Cultural

Fauna/Flora

Gastronomy

Nature & Landscape

Not relevant

Recreational

Religious

Rural tourism

Sports

Sun and beach

Urban

Table 2. Table of contents of the dataset

Folder

format

Description

Inputs

Base layers

by National Park

100-meter grid

grid_wgs84_atrib

.shp

100 x 100 meter grid for each of the studied national parks that cover the study area

Biosphere Reserve

MAB_wgs84

.shp

Biosphere reserve layers present in each of the national parks studied

Municipality

Municipality

.shp

Layers of municipalities that overlap with the park area, biosphere reserve, Natura 2000 and the socioeconomic influence area with a 100-meter buffer

National park limit

National_park_limit

.shp

Boundaries of each of the national parks studied

Natura 2000

RN2000

.shp

Layers of the Natura 2000 for each of the national parks studied

Socioeconomic influence area

AIS

.shp

Area of socioeconomic influence of each of the parks studied

Readme

.txt

File containing layer metadata, including download locations and descriptions of shape attributes.

by National Park

Accessibility

.tif

Accessibility variables that include routes, streets, parking, and train tracks

Climate

.tif

Chelsea-derived climate variable layers and solar radiation layers

Ecosystem functioning

.tif

Layers derived from remote sensing that are related with the functional attributes of ecosystems

Ecosystem structure

.tif

Landscape and spectral diversity metrics

Geodiversity

.tif

Topographic and derived variables

Land use Land cover

.tif

Layers related to land use and cover

Tourism and Culture

.tif

Layers related to infrastructure associated with tourism such as bars, restaurants, lodgings and places of cultural interest such as monuments

Scripts

Modeling to get output data

Biomod_modelling_by_park

.R

Script used for modeling CES using data from social media by fitting one ENM for each park and CES.

Biomod_modelling_all_parks

.R

Script used for modeling CES using data from social media by fitting one ENM for each CES.

Modeling to get output data

Downloading and processing variables

EFAS

EFAs code

.js

GEE scripts used to download the Ecosystem Functional Attributes (EFAs) (Paruelo et al.2001; Alcaraz-Segura et al. 2006) derived from Sentinel 2 dataset for each of the national parks studied

OSM

1) Download layers

.py

Python scripts used to download the OpenStreetMap layers of interest for each of the national parks studied.

2) Join layers

.py

Scripts used to merge OSM layers belonging to the same category. e.g., primary, secondary, and tertiary highways.

3) Count point

.py

Scripts used to count the number of points in each of the 100 grid cells for each park, used in case of point type data

4) Presence and absence

.py

Scripts used to assess presence in each of the cells of the 100-square grid for each park, used in the case of data types such as points, lines, and polygons.

Remote sensing

Canopy

.js

GEE scripts used to download the canopy (https://gee-community-catalog.org/projects/canopy/) downloaded and cropped for each of the national parks studied

ESPI

.js

GEE scripts used to download the ESPI index (Ecosystem Service Provision Index) downloaded and cropped for each of the national parks studied

European disturbance map

.js

GEE scripts used to download European disturbance maps (//https://www.eea.europa.eu/data-and-maps/figures/biogeographical-regions-in-europe-2)

downloaded and cropped for each of the national parks studied

LST

.js

GEE scripts used to download LST maps (from Landsat Collection)

downloaded and cropped for each of the national parks studied

Night lights

.js

GEE scripts used to download nighttime light maps (https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_DNB_ANNUAL_V22)

downloaded and cropped for each of the national parks studied

Population density

.js

GEE scripts used to download population density maps (https://developers.google.com/earth-engine/datasets/catalog/CIESIN_GPWv411_GPW_Population_Density)

downloaded and cropped for each of the national parks studied

Soil groups

.js

GEE scripts used to download Hydrologic Soil Group maps (https://gee-community-catalog.org/projects/hihydro_soil/)

downloaded and cropped for each of the national parks studied

Solar radiation

.js

GEE scripts used to download solar radiation maps (https://globalsolaratlas.info/support/faq)

downloaded and cropped for each of the national parks studied

RGB diversity

Seasonal KMeans clustering

.js

GEE scripts were used to calculate seasonal clusters using Sentinel 2 RGB bands with GEE's .wekaKMeans algorithm. These layers were downloaded and cropped for each of the national parks studied.

Colour diversity analysis

.R

R script used to calculate spectral diversity (Shannon, Simpson and inverse Simpson) using the cluster layers and RGB bands derived from Sentinel 2.

Post processing

Align_and_Clip_rasters

.py

Python scripts used to align and clip the downloaded layers to a 100-meter grid reference layer for each of the national parks studied.

Outputs

CES projections

proj_Aiguestortes_Sports_ensemble

.tif

Spatial projections for the best models obtained for each CES and park

References:

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Work Zone Map Service Integration Market Outlook

According to our latest research, the Global Work Zone Map Service Integration market size was valued at $1.2 billion in 2024 and is projected to reach $4.8 billion by 2033, expanding at a robust CAGR of 16.4% during the forecast period of 2025–2033. The primary growth driver for this market is the increasing emphasis on road safety and efficient traffic management, which is compelling governments and transportation authorities worldwide to adopt advanced digital solutions for real-time work zone mapping and integration. The convergence of intelligent transportation systems (ITS), rapid urbanization, and heightened infrastructure investments are collectively catalyzing the demand for work zone map service integration across multiple sectors, including construction, traffic management, and public safety.

Regional Outlook

North America currently commands the largest share of the Work Zone Map Service Integration market, accounting for approximately 38% of the global market value in 2024. This dominance is attributed to the region's mature technological ecosystem, early adoption of intelligent transportation solutions, and robust regulatory frameworks supporting digital transformation in transportation infrastructure. The United States, in particular, has witnessed significant investments in smart city projects and infrastructure modernization, which has fueled widespread deployment of work zone mapping technologies. Furthermore, the presence of leading technology providers, proactive government policies, and a high level of awareness regarding road safety have collectively reinforced North America’s leadership position in this market segment.

In contrast, the Asia Pacific region is poised to emerge as the fastest-growing market, projected to register a CAGR of 19.8% from 2025 to 2033. The rapid expansion is driven by accelerated urbanization, burgeoning infrastructure development projects, and increased government spending on smart transportation initiatives across countries such as China, India, Japan, and South Korea. The region's large population base and rising vehicle density have intensified the need for effective traffic management and work zone safety solutions. Additionally, local governments are increasingly prioritizing digital transformation to enhance public safety and streamline construction planning, which is fostering a fertile environment for the adoption of work zone map service integration technologies.

Emerging economies in Latin America and the Middle East & Africa are gradually adopting work zone map service integration, albeit at a slower pace due to challenges such as limited digital infrastructure, budget constraints, and regulatory hurdles. However, localized demand is growing, particularly in urban centers where traffic congestion and construction activities are on the rise. Policy reforms, international collaborations, and pilot projects are beginning to pave the way for broader market penetration. Nonetheless, these regions face persistent challenges related to technology accessibility, workforce training, and standardization, which may temper the pace of adoption in the near term.

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