INSEE zoning comprising a commune or a group of municipalities which includes in its territory a built-up area of at least 2,000 inhabitants where no dwelling is separated from the nearest to more than 200 metres. In addition, each municipality concerned has more than half of its population in this built-up area. The concept of urban unity is based on the continuity of the building and the number of inhabitants. An urban unit is a municipality or group of municipalities with a continuous building area (no cut-off of more than 200 metres between two buildings) with at least 2,000 inhabitants. If the urban unit is located in a single municipality, it is referred to as an isolated city. If the urban unit extends over several municipalities, and each of these municipalities concentrates more than half of its population in the continuous built-up area, it is referred to as a multi-communal agglomeration.

“The Global Human Settlement Layer Urban Centres Database (GHS-UCDB) is the most complete database on cities to date, publicly released as an open and free dataset. The database represents the global status on Urban Centres in 2015 by offering cities location, their extent (surface, shape), and describing each city with a set of geographical, socio-economic and environmental attributes, many of them going back 25 or even 40 years in time.”Zusätzliche Informationen The Urban Centres are defined by specific cut-off values on resdient population and built-up surfac share in a 1x1km uniform global grid.See ghs_stat_ucdb2015mt_globe_r2019a_v1_0_web_1.pdf for more information.Views of this layer are used in web maps for the ArcGIS Living Atlas of the World.QuelleGlobal Human Settlement - Urban Centre database R2019A - European Commission | Zuletzt Aufgerufen am 25.04.2025Datenbestand2019

15 km perimeter around the communal boundaries of each urban unit with more than 15,000 inhabitants. — The concept of urban unity is based on the continuity of the building and the number of inhabitants. An urban unit is a municipality or group of municipalities with a continuous building area (no cut-off of more than 200 metres between two buildings) with at least 2,000 inhabitants. If the urban unit is located in a single municipality, it is referred to as an isolated city. If the urban unit extends over several municipalities, and each of these municipalities concentrates more than half of its population in the continuous built-up area, it is referred to as a multi-communal agglomeration. Municipalities which do not form part of an urban unit are considered to be rural: municipalities with no continuous built-up area of 2000 inhabitants, and those with less than half of the municipal population in a continuous built-up area.

The concept of urban unity is based on the continuity of the building and the number of inhabitants. An urban unit is a municipality or group of municipalities with a continuous building area (no cut-off of more than 200 metres between two buildings) with at least 2,000 inhabitants. Municipalities which do not form part of an urban unit are considered to be rural: municipalities with no continuous built-up area of 2000 inhabitants, and those with less than half of the municipal population in a continuous built-up area.

The "Forest Proximate People" (FPP) dataset is one of the data layers contributing to the development of indicator #13, “number of forest-dependent people in extreme poverty,” of the Collaborative Partnership on Forests (CPF) Global Core Set of forest-related indicators (GCS). The FPP dataset provides an estimate of the number of people living in or within 5 kilometers of forests (forest-proximate people) for the year 2019 with a spatial resolution of 100 meters at a global level.

For more detail, such as the theory behind this indicator and the definition of parameters, and to cite this data, see: Newton, P., Castle, S.E., Kinzer, A.T., Miller, D.C., Oldekop, J.A., Linhares-Juvenal, T., Pina, L. Madrid, M., & de Lamo, J. 2022. The number of forest- and tree-proximate people: A new methodology and global estimates. Background Paper to The State of the World’s Forests 2022 report. Rome, FAO.

Contact points:

Maintainer: Leticia Pina

Maintainer: Sarah E., Castle

Data lineage:

The FPP data are generated using Google Earth Engine. Forests are defined by the Copernicus Global Land Cover (CGLC) (Buchhorn et al. 2020) classification system’s definition of forests: tree cover ranging from 15-100%, with or without understory of shrubs and grassland, and including both open and closed forests. Any area classified as forest sized ≥ 1 ha in 2019 was included in this definition. Population density was defined by the WorldPop global population data for 2019 (WorldPop 2018). High density urban populations were excluded from the analysis. High density urban areas were defined as any contiguous area with a total population (using 2019 WorldPop data for population) of at least 50,000 people and comprised of pixels all of which met at least one of two criteria: either the pixel a) had at least 1,500 people per square km, or b) was classified as “built-up” land use by the CGLC dataset (where “built-up” was defined as land covered by buildings and other manmade structures) (Dijkstra et al. 2020). Using these datasets, any rural people living in or within 5 kilometers of forests in 2019 were classified as forest proximate people. Euclidean distance was used as the measure to create a 5-kilometer buffer zone around each forest cover pixel. The scripts for generating the forest-proximate people and the rural-urban datasets using different parameters or for different years are published and available to users. For more detail, such as the theory behind this indicator and the definition of parameters, and to cite this data, see: Newton, P., Castle, S.E., Kinzer, A.T., Miller, D.C., Oldekop, J.A., Linhares-Juvenal, T., Pina, L., Madrid, M., & de Lamo, J. 2022. The number of forest- and tree-proximate people: a new methodology and global estimates. Background Paper to The State of the World’s Forests 2022. Rome, FAO.

References:

Buchhorn, M., Smets, B., Bertels, L., De Roo, B., Lesiv, M., Tsendbazar, N.E., Herold, M., Fritz, S., 2020. Copernicus Global Land Service: Land Cover 100m: collection 3 epoch 2019. Globe.

Dijkstra, L., Florczyk, A.J., Freire, S., Kemper, T., Melchiorri, M., Pesaresi, M. and Schiavina, M., 2020. Applying the degree of urbanisation to the globe: A new harmonised definition reveals a different picture of global urbanisation. Journal of Urban Economics, p.103312.

WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University, 2018. Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00645

Online resources:

GEE asset for "Forest proximate people - 5km cutoff distance"

N_URBAINE_UNITE_2020_ZSUP_FLA_000 Urban units 2020 for Corrèze and neighbouring departments The objects on the outer periphery of the neighbouring departments are not complete if it overflows over the next department. Based on IGN INSEE and GeoFLA files https://www.insee.fr/fr/information/4802589 The concept of urban unity is based on the continuity of the building and the number of inhabitants. Urban units are built in metropolitan France and in the overseas departments according to the following definition: a municipality or group of municipalities with a continuous building area (no cut-off of more than 200 metres between two buildings) with at least 2,000 inhabitants. If the urban unit is located in a single municipality, it is referred to as an isolated city. If the urban unit extends over several municipalities, and each of these municipalities concentrates more than half of its population in the continuous built-up area, it is referred to as a multi-communal agglomeration. If one of these municipalities concentrates less than half of its population in the continuous built-up area but concentrates 2,000 or more inhabitants there, then it will constitute an isolated urban unit. The agglomeration of Paris is the multi-communal agglomeration containing Paris. Finally, “community outside urban unit” means municipalities not assigned to an urban unit. These thresholds, 200 metres for the continuity of the building and 2,000 inhabitants for the population of built-up areas, are the result of recommendations adopted at international level. For example, in the European population census regulation, population statistics based on zoning into urban units are expected. The calculation of the space between two buildings is done by analysing the building databases of the National Institute for Geographical and Forestry Information (IGN). It takes account of cuts in the urban fabric such as rivers in the absence of bridges, graveries, height differences. Since the 2010 division, certain public spaces (cmeteries, stadiums, aerodromes, parking lots, etc.), industrial or commercial land (factory, activity areas, shopping centres, etc.) have been treated as buildings with the 200-metre rule to connect inhabited construction areas, unlike previous divisions where these spaces were only cancelled in the calculation of distances between buildings. Urban units are redefined periodically. The current zoning, dated 2020, is established with reference to the population known in the 2017 census and the administrative geography of the territory as of 1 January 2020. The previous fiscal year, dated 2010, was based on the 2007 census and the administrative geography of the territory as of 1 January 2010. A first demarcation of cities and agglomerations was carried out on the occasion of the 1954 census. New urban units were then formed in the 1962, 1968, 1975, 1982, 1990 and 1999 censuses. Urban units can span several departments or even cross national borders (see International Urban Unit). The division into urban units concerns all the municipalities of metropolitan France and the overseas departments.

INSEE zoning comprising a commune or a group of municipalities which includes in its territory a built-up area of at least 2,000 inhabitants where no dwelling is separated from the nearest to more than 200 metres. In addition, each municipality concerned has more than half of its population in this built-up area. The concept of urban unity is based on the continuity of the building and the number of inhabitants. An urban unit is a municipality or group of municipalities with a continuous building area (no cut-off of more than 200 metres between two buildings) with at least 2,000 inhabitants. If the urban unit is located in a single municipality, it is referred to as an isolated city. If the urban unit extends over several municipalities, and each of these municipalities concentrates more than half of its population in the continuous built-up area, it is referred to as a multi-communal agglomeration. Code “size of the urban unit” (based on the municipal population in the 2007 census for UU 2010): 0-Rural 1-Urban units from 2 000 to 4 999 inhabitants 2-Urban units from 5,000 to 9,999 inhabitants 3-Urban units from 10,000 to 19,999 inhabitants 4-Urban units from 20,000 to 49,999 inhabitants 5-Urban units from 50,000 to 99,999 inhabitants 6-Urban units from 100,000 to 199 999 inhabitants 7-Urban units from 200 000 to 1,999 999 inhabitants 8-Agglomeration of Paris Code “Urban unit type” 0-Unit of rural municipalities of the department 1-Single city or single-community urban unit 2-Intra-departmental agglomeration 3-Inter-departmental agglomeration 4-Interregional agglomeration 5-International agglomeration

The European Copernicus Coastal Flood Awareness System (ECFAS) project aimed at contributing to the evolution of the Copernicus Emergency Management Service (https://emergency.copernicus.eu/) by demonstrating the technical and operational feasibility of a European Coastal Flood Awareness System. Specifically, ECFAS provides a much-needed solution to bolster coastal resilience to climate risk and reduce population and infrastructure exposure by monitoring and supporting disaster preparedness, two factors that are fundamental to damage prevention and recovery if a storm hits.

The ECFAS Proof-of-Concept development ran from January 2021 to December 2022. The ECFAS project was a collaboration between Scuola Universitaria Superiore IUSS di Pavia (Italy, ECFAS Coordinator), Mercator Ocean International (France), Planetek Hellas (Greece), Collecte Localisation Satellites (France), Consorzio Futuro in Ricerca (Italy), Universitat Politecnica de Valencia (Spain), University of the Aegean (Greece), and EurOcean (Portugal), and was funded by the European Commission H2020 Framework Programme within the call LC-SPACE-18-EO-2020 - Copernicus evolution: research activities in support of the evolution of the Copernicus services.

Description of the containing files inside the Dataset.

The ECFAS Coastal Dataset represents a single access point to publicly available Pan-European datasets that provide key information for studying coastal areas. The publicly available datasets listed below have been clipped to the coastal area extent, quality-checked and assessed for completeness and usability in terms of coverage, accuracy, specifications and access. The dataset was divided at European country level, except for the Adriatic area which was extracted as a region and not at the country level due to the small size of the countries. The buffer zone of each data was 10km inland in order to be correlated with the new Copernicus product Coastal Zone LU/LC.

Specifically, the dataset includes the new Coastal LU/LC product which was implemented by the EEA and became available at the end of 2020. Additional information collected in relation to the location and characteristics of transport (road and railway) and utility networks (power plants), population density and time variability. Furthermore, some of the publicly available datasets that were used in CEMS related to the above mentioned assets were gathered such as OpenStreetMap (building footprints, road and railway network infrastructures), GeoNames (populated places but also names of administrative units, rivers and lakes, forests, hills and mountains, parks and recreational areas, etc.), the Global Human Settlement Layer (GHS) and Global Human Settlement Population Grid (GHS-POP) generated by JRC. Also, the dataset contains 2 layers with statistics information regarding the population of Europe per sex and age divided in administrative units at NUTS level 3. The first layer includes information for the whole of Europe and the second layer has only the information regarding the population at the Coastal area. Finally, the dataset includes the global database of Floods protection standards. Below there are tables which present the dataset.

* Adriatic folder contains the countries: Slovenia, Croatia, Montenegro, Albania, Bosnia and Herzegovina

* Malta was added to the dataset

Copernicus Land Monitoring Service:

Coastal LU/LC

Scale 1:10.000; A Copernicus hotspot product to monitor landscape dynamics in coastal zones

EU-Hydro - Coastline

Scale 1:30.000; EU-Hydro is a dataset for all European countries providing the coastline

Natura 2000

Scale 1: 100000; A Copernicus hotspot product to monitor important areas for nature conservation

European Settlement Map

Resolution 10m; A spatial raster dataset that is mapping human settlements in Europe

Imperviousness Density

Resolution 10m; The percentage of sealed area

Impervious Built-up

Resolution 10m; The part of the sealed surfaces where buildings can be found

Grassland 2018

Resolution 10m; A binary grassland/non-grassland product

Tree Cover Density 2018

Resolution 10m; Level of tree cover density in a range from 0-100%

Joint Research Center:

Global Human Settlement Population Grid
GHS-POP)

Resolution 250m; Residential population estimates for target year 2015

GHS settlement model layer
(GHS-SMOD)

Resolution 1km: The GHS Settlement Model grid delineates and classify settlement typologies via a logic of population size, population and built-up area densities

GHS-BUILT

Resolution 10m; Built-up grid derived from Sentinel-2 global image composite for reference year 2018

ENACT 2011 Population Grid

(ENACT-POP R2020A)

Resolution 1km; The ENACT is a population density for the European Union that take into account major daily and monthly population variations

JRC Open Power Plants Database (JRC-PPDB-OPEN)

Europe's open power plant database

GHS functional urban areas
(GHS-FUA R2019A)

Resolution 1km; City and its commuting zone (area of influence of the city in terms of labour market flows)

GHS Urban Centre Database
(GHS-UCDB R2019A)

Resolution 1km; Urban Centres defined by specific cut-off values on resident population and built-up surface

Additional Data:

Open Street Map (OSM)

BF, Transportation Network, Utilities Network, Places of Interest

CEMS

Data from Rapid Mapping activations in Europe

GeoNames

Populated places, Adm. units, Hydrography, Forests, Hills/Mountains, Parks, etc.

Global Administrative Areas

Administrative areas of all countries, at all levels of sub-division

NUTS3 Population Age/Sex Group

Eurostat population by age and sex statistics interescted with the NUTS3 Units

FLOPROS

A global database of FLOod PROtection Standards, which comprises information in the form of the flood return period associated with protection measures, at different spatial scales

Disclaimer:

ECFAS partners provide the data "as is" and "as available" without warranty of any kind. The ECFAS partners shall not be held liable resulting from the use of the information and data provided.

This project has received funding from the Horizon 2020 research and innovation programme under grant agreement No. 101004211

Urban centres are pivotal in shaping societies, yet a systematic global analysis of how countries are organized around multiple urban centres is lacking. We enhance understanding by delineating city-regions worldwide, classifying over 30,000 urban centres into four tiers-town, small, intermediate, and large city-based on population size and mapping their catchment areas based on travel time, differentiating between primary and secondary city-regions. Employing a 3-hour travel time cutoff, we identify 1,403 primary city-regions, increasing to 4,210 with a 1-hour cutoff, more indicative of commuting times. Our findings reveal significant interconnectedness among urban centres and with their surrounding areas, with 3.2 billion people having physical access to multiple tiers within an hour, and 4.7 billion within three hours. Notably, among people living in or closest to towns or small cities, twice as many have easier access to intermediate than to large cities, underscoring intermediate cities' crucial role in connecting surrounding populations. This systematic identification of city-regions globally, uncovers diverse organisational patterns across urban tiers, influenced by geography, level of development and infrastructure, offering a valuable spatial dataset for regional planning, economic development, and resource management.

The "Tree Proximate People" (TPP) dataset provides an estimate of the number of people living in or within 1 kilometer of trees outside forests (forest-proximate people) for the year 2019 with a spatial resolution of 100 meters at a global level. Trees outside forests are defined as areas classified as croplands with at least 10% tree cover.

For more detail, such as the theory behind, the definition of parameters, and to cite this data, see: Newton, P., Castle, S.E., Kinzer, A.T., Miller, D.C., Oldekop, J.A., Linhares-Juvenal, T., Pina, L., Madrid, M., & de Lamo, J. 2022. The number of forest- and tree-proximate people: a new methodology and global estimates. Background Paper to The State of the World’s Forests 2022 report. Rome, FAO.

Contact points:

Maintainer: Leticia Pina

Maintainer: Sarah E., Castle

Data lineage:

The TPP data are generated using Google Earth Engine. Trees outside forests (TOFs) are defined by the Copernicus Global Land Cover (CGLC) (Buchhorn et al. 2020) fractional cover data layer using a minimum of 10% tree cover on croplands lands. Any area classified as land with TOFs sized ≥ 1 ha in 2019 was included in this definition. Lands classified as forests in CGLC were excluded from the analysis. Croplands were defined using the FAO-LCCS2 land use classification layer from MODIS Land Cover (MCD12Q1.006). Croplands were defined as the total of three classifications: 1) “Herbaceous Croplands”: dominated by herbaceous annuals (<2m) with at least 60% cover and a cultivated fraction >60%, 2) “Natural Herbaceous/Croplands Mosaics”: mosaics of small-scale cultivation 40-60% with natural shrub or herbaceous vegetation, and 3) “Forest/Cropland Mosaics”: mosaics of small-scale cultivation 40-60% with >10% natural tree cover. Population density was defined by the WorldPop global population data for 2019 (WorldPop 2018). High density urban populations were excluded from the analysis. High density urban areas were defined as any contiguous area with a total population (using 2019 WorldPop data for population) of at least 50,000 people and comprised of pixels all of which met at least one of two criteria: either the pixel a) had at least 1,500 people per square km, or b) was classified as “built-up” land use by the CGLC dataset (where “built-up” was defined as land covered by buildings and other manmade structures) (Dijkstra et al. 2020). Using these datasets, any rural people living in or within 1 kilometer of TOFs on croplands in 2019 were classified as tree proximate people. Euclidean distance was used as the measure to create a 1-kilometer buffer zone around each TOF pixel. The scripts for generating the tree-proximate people and the rural-urban datasets using different parameters or for different years are published and available to users. For more detail, such as the theory behind this indicator and the definition of parameters, and to cite this data, see: Newton, P., Castle, S.E., Kinzer, A.T., Miller, D.C., Oldekop, J.A., Linhares-Juvenal, T., Pina, L., Madrid, M., & de Lamo, J. 2022. The number of forest- and tree-proximate people: a new methodology and global estimates. Background Paper to The State of the World’s Forests 2022. Rome, FAO.

References:

Buchhorn, M., Smets, B., Bertels, L., De Roo, B., Lesiv, M., Tsendbazar, N.E., Herold, M., Fritz, S., 2020. Copernicus Global Land Service: Land Cover 100m: collection 3 epoch 2019. Globe.

Dijkstra, L., Florczyk, A.J., Freire, S., Kemper, T., Melchiorri, M., Pesaresi, M. and Schiavina, M., 2020. Applying the degree of urbanisation to the globe: A new harmonised definition reveals a different picture of global urbanisation. Journal of Urban Economics, p.103312.

WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University, 2018. Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00645

Online resources:

GEE asset for "Tree proximate people – Croplands, 1km cutoff distance"

This dataset was developed by KTH-dESA and describes settlement patterns relating to electrification in Madagascar. Using the Open Source Spatial Electrification Tool three attributes have been assigned to the settlements retrieved from the Madagascar High Resolution Settlement Layer developed by Facebook Connectivity Lab and CIESIN [1]. The three attributes are as follows:

• Urban or rural status. The urban cutoff level, i.e. the minimum population density per square kilometer, has been calculated so that the urban population matches the official statistics of 35 % in 2015 [2]. The urban cutoff level was calculated to be 683 people/km2, meaning that all settlements above this value are considered urban.

• The number of households in the settlements by 2030. Based on the urban or rural status the future population for the settlements have been estimated by applying a population growth rate to match future population projections according to [3] and [4]. The number of households 2030 have then been calculated using the epected urban and rural household sizes by 2030 of 3.7 and 4.4 people per household respectively [5].

• Modeled household electrification status in 2015 (1 if the household in the cell are considered electrified by the national grid, 2 if electrified by mini-grids and 0 if non-electrified). The algorithm in OnSSET determines which household are likely to be electrified in 2015 to match the current electrification rate of 15% [6], based on meeting certain conditions for night-time light (NTL), population density and distance to the grid and roads. For Madagascar the settlements were calculated to be electrified by the national grid (RI Antananarico, RI Toamasina and RI Fianarantsoa) if they a) where within 5 km from the grid and had a minimum population density of 2287 people/km2 or minimum NTL of 60 or b) within 10 km from the grid and had a minimum population density of 10000 people/km2 or by mini-grids if they c) had a population density above 3882 people/km2 and minimum NTL of 5 or maximum 20 kilometers to major roads.

[1] Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University (2016). High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe https://energydata.info/dataset/madagascar-high-resolution-settlement-layer-2015

[2] United Nations - Economic Commission for Africa. The Demographic Profile of African Countries. (2016).

[3] United Nations, Department of Economic and Social Affairs, Population Division. World Urbanization Prospects: The 2014 Revision. (2014).

[4] Unicef - division of data, research and policy. Generation 2030 | Africa. (2014).

[5] Mentis, D. et al. Lighting the World: the first application of an open source, spatial electrification tool (OnSSET) on Sub-Saharan Africa. Environmental Research Letters. Vol. 12, nr 8. (2017).

[6] USAID. Power Africa in Madagascar | Power Africa | U.S. Agency for International Development. Available at: https://www.usaid.gov/powerafrica/madagascar. (2017).

INSEE zoning comprising a commune or a group of municipalities which includes in its territory a built-up area of at least 2,000 inhabitants where no dwelling is separated from the nearest to more than 200 metres. In addition, each municipality concerned has more than half of its population in this built-up area. The concept of urban unity is based on the continuity of the building and the number of inhabitants. An urban unit is a municipality or group of municipalities with a continuous building area (no cut-off of more than 200 metres between two buildings) with at least 2,000 inhabitants. If the urban unit is located in a single municipality, it is referred to as an isolated city. If the urban unit extends over several municipalities, and each of these municipalities concentrates more than half of its population in the continuous built-up area, it is referred to as a multi-communal agglomeration. New zoning of the Urban Units 2010 carried out with the census of the municipal population in 2008.

The concept of urban unity is based on the continuity of the building and the number of inhabitants. An urban unit is a municipality or a set of municipalities with a continuous built-up area (no cut of more than 200 meters between two buildings) that has at least 2,000 inhabitants.If the urban unit is located in a single municipality, it is called an isolated city. If the urban unit extends over several municipalities, and each of these municipalities concentrates more than half of its population in the continuous built-up area, it is called a multi-communal agglomeration. The concept of urban unity is based on the continuity of the building and the number of inhabitants. An urban unit is a municipality or a set of municipalities with a continuous built-up area (no cut of more than 200 meters between two buildings) that has at least 2,000 inhabitants.If the urban unit is located in a single municipality, it is called an isolated city. If the urban unit extends over several municipalities, and each of these municipalities concentrates more than half of its population in the continuous built-up area, it is called a multi-communal agglomeration.

The concept of urban unity is based on the continuity of the building and the number of inhabitants. An urban unit is a municipality or group of municipalities with a continuous built-up area (no cut-off of more than 200 metres between two buildings) with at least 2,000 inhabitants.If the urban unit is located in a single municipality, it is called an isolated city. If the urban unit extends over several municipalities, and each of these municipalities concentrates more than half of its population in the continuous built-up area, it is referred to as a multi-communal agglomeration.

The concept of urban unity is based on the continuity of the building and the number of inhabitants. An urban unit is a municipality or group of municipalities with a continuous building area (no cut-off of more than 200 metres between two buildings) with at least 2,000 inhabitants. If the urban unit is located in a single municipality, it is referred to as an isolated city. If the urban unit extends over several municipalities, and each of these municipalities concentrates more than half of its population in the continuous built-up area, it is referred to as a multi-communal agglomeration.

The concept of urban unity is based on the continuity of the building and the number of inhabitants. An urban unit is a municipality or group of municipalities with a continuous built-up area (no cut-off of more than 200 metres between two buildings) with at least 2,000 inhabitants.If the urban unit is located in a single municipality, it is called an isolated city. If the urban unit extends over several municipalities, and each of these municipalities concentrates more than half of its population in the continuous built-up area, it is referred to as a multi-communal agglomeration.

This layer shows which parts of the United States and Puerto Rico fall within ten minutes" walk of one or more grocery stores. It is estimated that 20% of U.S. population live within a 10 minute walk of a grocery store, and 92% of the population live within a 10 minute drive of a grocery store. The layer is suitable for looking at access at a neighborhood scale. When you add this layer to your web map, along with the drivable access layer and the SafeGraph grocery store layer, it becomes easier to spot grocery stores that sit within a highly populated area, and grocery stores that sit in a shopping center far away from populated areas. Add the Census block points layer to show a popup with the count of stores within 10 minutes" walk and drive. This view of a city begins to hint at the question: how many people have each type of access to grocery stores? And, what if they are unable to walk a mile regularly, or don"t own a car? How to Use This Layer in a Web MapUse this layer in a web map to introduce the concepts of access to grocery stores in your city or town. This is the kind of map where people will want to look up their home or work address to validate what the map is saying. See this example web map which you can use in your projects, storymaps, apps and dashboards. The layer was built with that use in mind. Many maps of access use straight-line, as-the-crow-flies distance, which ignores real-world barriers to walkability like rivers, lakes, interstates and other characteristics of the built environment. Block analysis using a network data set and Origin-Destination analysis factors these barriers in, resulting in a more realistic depiction of access. Lastly, this layer can serve as backdrop to other community resources, like food banks, farmers markets (example), and transit (example). Add a transit layer to immediately gauge its impact on the population"s grocery access. You can also use this map to see how it relates to communities of concern. Add a layer of any block group or tract demographics, such as Percent Senior Population (examples), or Percent of Households with Access to 0 Vehicles (examples). The layer is a useful visual resource for helping community leaders, business and government leaders see their town from the perspective of its residents, and begin asking questions about how their community could be improved. Data sourcesPopulation data is from the 2010 U.S. Census blocks. Each census block has a count of stores within a 10 minute walk, and a count of stores within a ten minute drive. Census blocks known to be unpopulated are given a score of 0. The layer is available as a hosted feature layer. Grocery store locations are from SafeGraph, reflecting what was in the data as of October 2020. Access to the layer was obtained from the SafeGraph offering in ArcGIS Marketplace. For this project, ArcGIS StreetMap Premium was used for the street network in the origin-destination analysis work, because it already has the necessary attributes on each street segment to identify which streets are considered walkable, and supports a wide variety of driving parameters. The walkable access layer and drivable access layers are rasters, whose colors were chosen to allow the drivable access layer to serve as backdrop to the walkable access layer. Data PreparationArcGIS Network Analyst was used to set up a network street layer for analysis. ArcGIS StreetMap Premium was installed to a local hard drive and selected in the Origin-Destination workflow as the network data source. This allows the origins (Census block centroids) and destinations (SafeGraph grocery stores) to be connected to that network, to allow origin-destination analysis. The Census blocks layer contains the centroid of each Census block. The data allows a simple popup to be created. This layer"s block figures can be summarized further, to tract, county and state levels. The SafeGraph grocery store locations were created by querying the SafeGraph source layer based on primary NAICS code. After connecting to the layer in ArcGIS Pro, a definition query was set to only show records with NAICS code 445110 as an initial screening. The layer was exported to a local disk drive for further definition query refinement, to eliminate any records that were obviously not grocery stores. The final layer used in the analysis had approximately 53,600 records. In this map, this layer is included as a vector tile layer. Methodology Every census block in the U.S. was assigned two access scores, whose numbers are simply how many grocery stores are within a 10 minute walk and a 10 minute drive of that census block. Every census block has a score of 0 (no stores), 1, 2 or more stores. The count of accessible stores was determined using Origin-Destination Analysis in ArcGIS Network Analyst, in ArcGIS Pro. A set of Tools in this ArcGIS Pro package allow a similar analysis to be conducted for any city or other area. The Tools step through the data prep and analysis steps. Download the Pro package, open it and substitute your own layers for Origins and Destinations. Parcel centroids are a suggested option for Origins, for example. Origin-Destination analysis was configured, using ArcGIS StreetMap Premium as the network data source. Census block centroids with population greater than zero were used as the Origins, and grocery store locations were used as the Destinations. A cutoff of 10 minutes was used with the Walk Time option. Only one restriction was applied to the street network: Walkable, which means Interstates and other non-walkable street segments were treated appropriately. You see the results in the map: wherever freeway overpasses and underpasses are present near a grocery store, the walkable area extends across/through that pass, but not along the freeway. A cutoff of 10 minutes was used with the Drive Time option. The default restrictions were applied to the street network, which means a typical vehicle"s access to all types of roads was factored in. The results for each analysis were captured in the Lines layer, which shows which origins are within the cutoff of each destination over the street network, given the assumptions about that network (walking, or driving a vehicle). The Lines layer was then summarized by census block ID to capture the Maximum value of the Destination_Rank field. A census block within 10 minutes of 3 stores would have 3 records in the Lines layer, but only one value in the summarized table, with a MAX_Destination_Rank field value of 3. This is the number of stores accessible to that census block in the 10 minutes measured, for walking and driving. These data were joined to the block centroids layer and given unique names. At this point, all blocks with zero population or null values in the MAX_Destination_Rank fields were given a store count of 0, to help the next step. Walkable and Drivable areas are calculated into a raster layer, using Nearest Neighbor geoprocessing tool on the count of stores within a 10 minute walk, and a count of stores within a ten minute drive, respectively. This tool uses a 200 meter grid and interpolates the values between each census block. A census tracts layer containing all water polygons "erased" from the census tract boundaries was used as an environment setting, to help constrain interpolation into/across bodies of water. The same layer use used to "shoreline" the Nearest Neighbor results, to eliminate any interpolation into the ocean or Great Lakes. This helped but was not perfect. Notes and LimitationsThe map provides a baseline for discussing access to grocery stores in a city. It does not presume local population has the desire or means to walk or drive to obtain groceries. It does not take elevation gain or loss into account. It does not factor time of day nor weather, seasons, or other variables that affect a person"s commute choices. Walking and driving are just two ways people get to a grocery store. Some people ride a bike, others take public transit, have groceries delivered, or rely on a friend with a vehicle. Thank you to Melinda Morang on the Network Analyst team for guidance and suggestions at key moments along the way; to Emily Meriam for reviewing the previous version of this map and creating new color palettes and marker symbols specific to this project. Additional ReadingThe methods by which access to food is measured and reported have improved in the past decade or so, as has the uses of such measurements. Some relevant papers and articles are provided below as a starting point. Measuring Food Insecurity Using the Food Abundance Index: Implications for Economic, Health and Social Well-BeingHow to Identify Food Deserts: Measuring Physical and Economic Access to Supermarkets in King County, WashingtonAccess to Affordable and Nutritious Food: Measuring and Understanding Food Deserts and Their ConsequencesDifferent Measures of Food Access Inform Different SolutionsThe time cost of access to food – Distance to the grocery store as measured in minutes

The concept of urban unity is based on the continuity of the building and the number of inhabitants. An urban unit is a municipality or group of municipalities with a continuous built-up area (no cut-off of more than 200 metres between two buildings) with at least 2,000 inhabitants.If the urban unit is located in a single municipality, it is called an isolated city. If the urban unit extends over several municipalities, and each of these municipalities concentrates more than half of its population in the continuous built-up area, it is referred to as a multi-communal agglomeration.

As part of the co-development and information-sharing phase of the Collaborative Process on the Second-Generation Cut-off and Section 10 Voting Thresholds, Canada aims to provide early and ongoing information to support Indigenous and First Nations rights-holders’ participation in consultation events. Indigenous Services Canada has produced a Rights-Holders Information Kit containing detailed descriptions of the second-generation cut-off and section 10 voting thresholds. The Kit has been broadly distributed via mail, email, and is available online. This data set contains community-specific data sheets, which have been developed as part of this initiative to provide First Nations with the impact of the second-generation cut-off on their community’s registered population. The data sheets have been organized by region and can be found in the documents below. To find out more about the Collaborative Process on the Second-Generation Cut-off and Section 10 Voting Thresholds, please contact Registration Reform at Reforme-de-linscription-Registration-Reform@sac-isc.gc.ca.

ObjectiveThe objective of this study was to describe the age and sex-specific prevalence of renal insufficiency, and observe its trends over a decade at an urban Bangladesh setup.MethodThis was a cross-sectional study, in which we observed the Estimated Glomerular Filtration Rate (eGFR) of 218,888 adults, aged ≥19 years, who had submitted their blood specimen to the Clinical Biochemistry Laboratory of the International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b) during the years 2006–2015. We applied CKD-EPI definition in estimating eGFR using their age-and sex-specific serum creatinine concentrations. Based on the eGFR, we classified the population into five stages of renal insufficiency (stage-1 to stage-5), at age intervals of five-years. Data were analysed using the Linear Regression and Multinomial Logistic Regression models.ResultsFemales constituted 43% (n = 94,931) of the study population; and 34% (n = 42,576) of the males and 31% (n = 29,830) of the females had their serum creatinine concentrations above the upper limit of the laboratory reference cut-off. The overall prevalence of stage-2 to stage-5 renal insufficiency were 24% (n = 52,126), 17% (n = 38,539), 8% (n = 16,504) and 6% (n = 12,665) respectively; the prevalence were 23% (n = 1,890), 19% (n = 1,579), 9% (n = 769) and 9% (n = 770) respectively in 2006, and 24% (n = 10,062), 17% (n = 6,903), 6% (n = 2,537) and 5% (n = 1,924) respectively in 2015. The prevalence was higher among the females. At least 2% of the adults, younger than <44 years, had stage-4 and stage-5 in 2015. The age-adjusted eGFR was significantly lower among the post-menopausal females (aged ≥46 y) compared to the same age group males (64.08±10.83 vs. 66.83±10.41 mL/min/1.73 m2; p<0.001). Compared to 2006, the number of individuals with renal insufficiency (stage 2 and above) had increased at least two times, irrespective of age, in 2015. A single year of increase in the age was significantly associated with 1.32 unit reductions in the eGFR; and the reductions were higher for females who also had higher odds of renal insufficiency stages-2 and beyond.ConclusionThis study observed high prevalence of stage-2 to stage-5 renal insufficiency in Bangladeshi populations, irrespective of age, and especially among the females.