This dataset presents the refined version of the degree of urbanisation of European countries. The degree of urbanisation relies on a population grid to classify local units. Originally the classification system was developed for the European Statistical System to classify local units into three classes (level 1): cities, towns & suburbs, and rural areas. In this version the classification was further refined (level 2) to also identify smaller individual settlements; distinguishing towns from suburbs and identifying villages, dispersed areas and mostly uninhabited areas in former rural areas class. The final classes of the refined degree of urbanisation dataset are six, namely 1) cities, 2) towns, 3) suburbs, 4) villages, 5) dispersed rural areas and 6) mostly uninhabited areas. The temporal reference is set between 2011 and 2012 because of the main inputs, the GEOSTAT population grid 2011 and the European Settlement Map 2012 from Copernicus. IMPORTANT NOTE: This metadata has been created using draft documentation provided by the European Commission, DG REGIO. This dataset has been created by the European Commission, DG Regional and Urban Policy (REGIO) in cooperation with the Joint Research Centre (JRC). Re-distribution or re-use of this dataset is allowed provided that the source is acknowledged.

The Global Human Settlement Layer (GHSL) produces new global spatial information, evidence-based analytics, and knowledge describing the human presence in the planet. The Joint Research Centre (JRC) and the Directorate General for Regional Development (DG REGIO) of the European Commission support the GHSL activities. The GHSL contributes to the international partnership “GEO Human Planet Initiative”. The GHSL methods rely on automatic spatial data mining technologies allowing the extraction of analytics and knowledge from large amount of heterogeneous data including global, fine-scale satellite-image data streams, census data, and crowd sources or volunteering geographic information sources. Spatial data reporting objectively and systematically about the presence of population and built-up infrastructures are necessary for any evidence-based modelling or assessing of i) human and physical exposure to threats as environmental contamination and degradation, natural disasters and conflicts, ii) impact of human activities on ecosystems, and iii) access to resources. The GHS Urban Centre Database (GHS- UCDB) describes spatial entities called “urban centres” accordingly to a set of multi-temporal thematic attributes gathered from the GHSL sources integrated with other sources available in the open scientific domain. The Urban Centres are defined by specific cut-off values on resident population and built-up surface share in a 1x1 km global uniform grid. The input data it is generated by the GHSL, and the operating parameters are set in the frame of the “degree of urbanization” (DEGURBA) methodology. The DEGURBA is a methodology for delineation of urban and rural areas made for international statistical comparison purposes that is developed by the European Commission, the Organization for Economic Co-operation and Development (OECD), the Food and Agriculture Organization of the United Nations (FAO), UN-Habitat and the World Bank. The reference GHSL input data used to delineate the Urban Centres are included in the Community pre-Release of GHS Data Package (GHS CR2018) in support to the GEO Human Planet Initiative. The parameter set used to delineate the Urban Centres from the input data are included in the GHSL settlement classification model SMODv9s10E 2018. The reference epoch for the spatial delineation of the Urban Centres is 2015. The attributes of the GHS-UCDB have different time depth for a maximum of 40 years, depending on availability of the input sources.

Odds ratios presented for arthropathies (M00–M25); systemic connective tissue disorders (M30–M36); dorsopathies (M40–M54); soft tissue disorders (M60–M79); osteopathies and chondropathies (M80–M94) and other disorders of the musculoskeletal system and connective tissue (M95–M99).

Ratio of land consumption rate to population growth rate and built up area per capita at the national level for Iceland, Israel and Slovakia and for select cities in Israel, Portugal and Slovakia. Data is produced by the national bureau of statistics in each country. Delineation of cities and urban areas vary by country - data for some countries is produced using the official municipality boundaries and for others the harmonized DEGURBA approach is used.

The Rural Access Index (RAI) is a measure of access, developed by the World Bank in 2006. It was adopted as Sustainable Development Goal (SDG) indicator 9.1.1 in 2015, to measure the accessibility of rural populations. It is currently the only indicator for the SDGs that directly measures rural access.The RAI measures the proportion of the rural population that lives within 2 km of an all-season road. An all-season road is one that is motorable all year, but may be temporarily unavailable during inclement weather (Roberts, Shyam, & Rastogi, 2006). This dataset implements and expands on the most recent official methodology put forward by the World Bank, ReCAP's 2019 RAI Supplemental Guidelines. This is, to date, the only publicly available application of this method at a global scale.MethodologyReCAP's methodology provided new insight on what makes a road all-season and how this data should be handled: instead of removing unpaved roads from the network, the ones that are classified as unpaved are to be intersected with topographic and climatic conditions and, whenever there’s an overlap with excess precipitation and slope, a multiplying factor ranging from 0% to 100% is applied to the population that would access to that road. This present dataset developed by SDSN's SDG Transformation Centre proposes that authorities ability to maintain and remediate road conditions also be taken into account.Data sourcesThe indicator relies on four major items of geospatial data: land cover (rural or urban), population distribution, road network extent and the “all-season” status of those roads.Land cover data (urban/rural distinction)Since the indicator measures the acess rural populations, it's necessary to define what is and what isn't rural. This dataset uses the DegUrba Methodology, proposed by the United Nations Expert Group on Statistical Methodology for Delineating Cities and Rural Areas (United Nations Expert Group, 2019). This approach has been developed by the European Commission Global Human Settlement Layer (GHSL-SMOD) project, and is designed to instil some consistency into the definitions based on population density on a 1-km grid, but adjusted for local situations.Population distributionThe source for population distribution data is WorldPop. This uses national census data, projections and other ancillary data from countries to produce aggregated, 100 m2 population data. Road extentTwo widely recognized road datasets are used: the real-time updated crowd-sourced OpenStreetMap (OSM) or the GLOBIO’s 2018 GRIP database, which draws data from official national sources. The reasons for picking the latter are mostly related to its ability to provide information on the surface (pavement) of these roads, to the detriment of the timeliness of the data, which is restrained to the year 2018. Additionally, data from Microsoft Bing's recent Road Detection project is used to ensure completeness. This dataset is completely derived from machine learning methods applied over satellite imagery, and detected 1,165 km of roads missing from OSM.Roads’ all-season statusThe World Bank's original 2006 methodology defines the term all-season as “… a road that is motorable all year round by the prevailing means of rural transport, allowing for occasional interruptions of short duration”. ReCAP's 2019 methodology makes a case for passability equating to the all-season status of a road, along with the assumption that typically the wet season is when roads become impassable, especially so in steep roads that are more exposed to landslides.This dataset follows the ReCAP methodology by creating an passability index. The proposed use of passability factors relies on the following three aspects:• Surface type. Many rural roads in LICs (and even in large high-income countries including the USA and Australia) are unpaved. As mentioned before, unpaved roads deteriorate rapidly and in a different way to paved roads. They are very susceptible to water ingress to the surface, which softens the materials and makes them very vulnerable to the action of traffic. So, when a road surface becomes saturated and is subject to traffic, the deterioration is accelerated. • Climate. Precipitation has a significant effect on the condition of a road, especially on unpaved roads, which predominate in LICs and provide much of the extended connectivity to rural and poor areas. As mentioned above, the rainfall on a road is a significant factor in its deterioration, but the extent depends on the type of rainfall in terms of duration and intensity, and how well the roadside drainage copes with this. While ReCAP suggested the use of general climate zones, we argue that better spatial and temporal resolutions can be acquired through the Copernicus Programme precipitation data, which is made available freely at ~30km pixel size for each month of the year.• Terrain. The gradient and altitude of roads also has an effect on their accessibility. Steep roads become impassable more easily due to the potential for scour during heavy rainfall, and also due to slipperiness as a result of the road surface materials used. Here this is drawn from slope calculated from SRTM Digital Terrain data.• Road maintenance. The ability of local authorities to remediate damaged caused by precipitation and landslides is proposed as a correcting factor to the previous ones. Ideally this would be measured by the % of GDP invested in road construction and maintenance, but this isn't available for all countries. For this reason, GDP per capita is adopted as a proxy instead. The data range is normalized in such a way that a road maxed out in terms of precipitation and slope (accessibility score of 0.25) in a country at the top of the GDP per capita range is brought back at to the higher end of the accessibility score (0.95), while the accessibility score of a road meeting the same passability conditions in a country which GDP per capita is towards the lower end is kept unchanged.Data processingThe roads from the three aforementioned datasets (Bing, GRIP and OSM) are merged together to them is applied a 2km buffer. The populations falling exclusively on unpaved road buffers are multiplied by the resulting passability index, which is defined as the normalized sum of the aforementioned components, ranging from 0.25 to. 0.9, with 0.95 meaning 95% probability that the road is all-season. The index applied to the population data, so, when calculated, the RAI includes the probability that the roads which people are using in each area will be all-season or not. For example, an unpaved road in a flat area with low rainfall would have an accessibility factor of 0.95, as this road is designed to be accessible all year round and the environmental effects on its impassability are minimal.The code for generating this dataset is available on Github at: https://github.com/sdsna/rai

Ez az adatkészlet az európai országok urbanizációs fokának kifinomult változatát mutatja be. Az urbanizáció mértéke a népességi rácsra támaszkodik a helyi egységek osztályozásához. Az osztályozási rendszert eredetileg az Európai Statisztikai Rendszer számára fejlesztették ki, hogy a helyi egységeket három osztályba sorolják (1. szint): városok, városok és külvárosok, és vidéki területek. Ebben a változatban a besorolást tovább finomították (2. szint) a kisebb települések azonosítása érdekében; megkülönbözteti a városokat a külvárosoktól, és azonosítja a falvakat, a szétszórt területeket és a többnyire lakatlan területeket a korábbi vidéki területek osztályában. A finomított urbanizációs adatkészlet utolsó osztályai hat, azaz 1) városok, 2) városok, 3) külvárosok, 4) falvak, 5) szétszórt vidéki területek és 6) többnyire lakatlan területek. Az időbeli referencia 2011 és 2012 között van meghatározva a fő inputok, a GEOSTAT 2011-es népességi rácsa és a Kopernikusz 2012-es európai települési térképe miatt.

FONTOS MEGJEGYZÉS: Ezek a metaadatok az Európai Bizottság Regionális és Várospolitikai Főigazgatósága által benyújtott dokumentáció-tervezetek felhasználásával készültek. Ezt az adatkészletet az Európai Bizottság Regionális és Várospolitikai Főigazgatósága (REGIO) a Közös Kutatóközponttal (JRC) együttműködve hozta létre. Ezen adatkészlet újraelosztása vagy újrafelhasználása megengedett, feltéve, hogy a forrást feltüntetik.

To meet the demand for statistics at a local level, Eurostat maintains a system of Local Administrative Units (LAUs) compatible with NUTS. These LAUs are the building blocks of the NUTS, and comprise the municipalities and communes of the European Union. Until 2016, two levels of Local Administrative Units (LAU) existed:The upper LAU level (LAU level 1, formerly NUTS level 4) were defined for most, but not all of the countries, and, the lower LAU level (LAU level 2, formerly NUTS level 5) consisted of municipalities or equivalent units in the 28 EU Member States. Since 2017, only one level of LAU has been kept. The LAUs are administrative for reasons such as the availability of data and policy implementation capacity, a subdivision of the NUTS 3 regions covering the whole economic territory of the Member States, and appropriate for the implementation of local level typologies included in TERCET, namely the coastal area and Degree of Urbanisation (DEGURBA) classification.Since there are frequent changes to the LAUs, Eurostat publishes an updated list towards the end of each year. Until 2019, The LAUs in Ireland correspond to the Electoral Division (Statistical CSO) boundary. However since 2019, the LAUs for Ireland reflect the Local Electoral Areas as defined in Local Electoral Areas and Municipal District Orders 2018 (SI nos 27/2019 and 28/2019).

The dataset CORRUPTION_SATELLITE.xlsx is available on Figshare. The first sheet has our estimates for construction-based corruption for 54,091 European municipalities for the years 1971-2011 (with 10-year intervals). The second sheet contains the same estimate for Italy. The dataset also contains the data we use to produce our estimates. The third sheet has seventeen analytical indicators for Italy: Italian name of municipalities (Municipality), Year (Year), code of NUTS_3 according to Eurostat (NUTS_3), the Italian name of the Province corresponding to NUTS_3 (Province), LAU code (Code), area of the municipality in square kilometers (Area_km), total population (Pop), degree of Urbanization according to Copernicus (DEGURBA), amount of built-up land (Built_up), area of the province (Prov_km), Gross Value Added (GVA), percentage of construction sector on value added (perc_const), population density (density), population elderly (elderly), and gross domestic production at present purchase power (gdp_ppp). The fourth sheet contains the Index of the expected budget as computed by OpenCivitas for Italy. The fifth sheet contains the panel data for Europe. It has the same structure as the second sheet but with three new columns: two identification codes (GISCO_ID and RAS_ID) and the name of the country (COUNTRY).

This data repository makes available accessibility indicators from populated 1km EU grid to towns and cities at aggregated scales (NUTS, EU reference grid cells). It covers all Europe and is based on the outputs of data previously processed and documented in another Zenodo repository. All the data sources, data processing and general objectives are described in this resource.

A dedicated notebook and a reamde document in the root of the archive shows how this information has been aggregated at upper spatial/territorial levels, using geometric and statistical attributes associated to 1km grid cells (spatial position, population evolution, DEGURBA categories).

These outputs are more suitable for analytical purposes at NUTS level (combination to other classical socio-economic indicators) or web mapping.

This archive makes available accessibility indicators at EU scale from populated 1km EU grid to towns and cities at EU scale (512 million travel time by car calculated between origins and destinations). It follows a reproducible, transparent and updatable framework. It uses only open source and free routing engines (OSRM), based on OpenStreetMap (OSM) network. This routing engine makes possible the creation of travel time indicators for a large set of origins and destinations.

The EU towns and cities layer has been recently made available and named by the European Commission. This layer is based on a common methodology for all Europe. Within GRANULAR activities, we consider the towns and cities layer as a proxy to discuss on little and medium commercial centralities in Europe.

This methodological framework, implemented with open source solutions (data and code) only and documented in a reproducible way in R notebooks, could be easily extended to other origins and destinations, if a relevant layer will be identified in the future.

Based on travel time matrix, it is possible to compute a large set of indicators. This archive (see readme at the root folder) describes the input data used, summarises the data processing and provide information and metadata on output indicators created at 1km grid cells. Finally it shows how this information has been aggregated at upper territorial levels (NUTS), using statistical attributes associated to 1km grid cells (population evolution, DEGURBA categories).

All the output data is also available.

Το Global Human Settlement Layer (GHSL) παράγει νέες παγκόσμιες χωρικές πληροφορίες, τεκμηριωμένες αναλύσεις και γνώσεις που περιγράφουν την ανθρώπινη παρουσία στον πλανήτη. Το Κοινό Κέντρο Ερευνών (JRC) και η Γενική Διεύθυνση Περιφερειακής Ανάπτυξης (ΓΔ REGIO) της Ευρωπαϊκής Επιτροπής υποστηρίζουν τις δραστηριότητες του GHSL. Το GHSL συμβάλλει στη διεθνή συνεργασία «GEO Human Planet Initiative». Οι μέθοδοι GHSL βασίζονται σε αυτόματες τεχνολογίες εξόρυξης χωρικών δεδομένων που επιτρέπουν την εξαγωγή αναλύσεων και γνώσεων από μεγάλο όγκο ετερογενών δεδομένων, συμπεριλαμβανομένων παγκόσμιων ροών δεδομένων υψηλής κλίμακας δορυφορικών εικόνων, δεδομένων απογραφής και πηγών πλήθους ή εθελοντικών πηγών γεωγραφικών πληροφοριών. Η υποβολή εκθέσεων χωρικών δεδομένων αντικειμενικά και συστηματικά σχετικά με την παρουσία πληθυσμού και δομημένων υποδομών είναι αναγκαία για κάθε τεκμηριωμένη μοντελοποίηση ή αξιολόγηση i) της ανθρώπινης και φυσικής έκθεσης σε απειλές όπως η περιβαλλοντική ρύπανση και υποβάθμιση, οι φυσικές καταστροφές και συγκρούσεις, ii) ο αντίκτυπος των ανθρώπινων δραστηριοτήτων στα οικοσυστήματα και iii) η πρόσβαση σε πόρους. Η βάση δεδομένων του GHS Urban Centre (GHS- UCDB) περιγράφει χωρικές οντότητες που ονομάζονται «αστικά κέντρα» ανάλογα με ένα σύνολο πολυχρονικών θεματικών χαρακτηριστικών που συλλέγονται από τις πηγές GHSL ενσωματωμένες με άλλες πηγές διαθέσιμες στον ανοικτό επιστημονικό τομέα. Τα αστικά κέντρα ορίζονται από ειδικές τιμές αποκοπής για τον μόνιμο πληθυσμό και το δομημένο μερίδιο επιφάνειας σε ένα παγκόσμιο ομοιόμορφο πλέγμα 1x1 km. Τα δεδομένα εισόδου που παράγονται από το GHSL και οι παράμετροι λειτουργίας καθορίζονται στο πλαίσιο της μεθοδολογίας «βαθμού αστικοποίησης» (DEGURBA). Το DEGURBA είναι μια μεθοδολογία για την οριοθέτηση των αστικών και αγροτικών περιοχών για σκοπούς διεθνούς στατιστικής σύγκρισης που αναπτύσσεται από την Ευρωπαϊκή Επιτροπή, τον Οργανισμό Οικονομικής Συνεργασίας και Ανάπτυξης (ΟΟΣΑ), τον Οργανισμό Τροφίμων και Γεωργίας των Ηνωμένων Εθνών (FAO), το UN-Habitat και την Παγκόσμια Τράπεζα. Τα δεδομένα αναφοράς GHSL που χρησιμοποιούνται για την οριοθέτηση των αστικών κέντρων περιλαμβάνονται στην κοινοτική δέσμη μέτρων δεδομένων GHS (GHS CR2018) για την υποστήριξη της πρωτοβουλίας GEO Human Planet.Η παράμετρος που χρησιμοποιείται για την οριοθέτηση των αστικών κέντρων από τα εισερχόμενα δεδομένα περιλαμβάνεται στο μοντέλο ταξινόμησης διακανονισμού GHSL SMODv9s10E 2018. Η εποχή αναφοράς για τη χωρική οριοθέτηση των αστικών κέντρων είναι το 2015.Τα χαρακτηριστικά του GHS-UCDB έχουν διαφορετικό βάθος χρόνου για μέγιστο χρονικό διάστημα 40 ετών, ανάλογα με τη διαθεσιμότητα των πηγών εισόδου. Το Κοινό Κέντρο Ερευνών (JRC) και η Γενική Διεύθυνση Περιφερειακής Ανάπτυξης (ΓΔ REGIO) της Ευρωπαϊκής Επιτροπής υποστηρίζουν τις δραστηριότητες του GHSL. Το GHSL συμβάλλει στη διεθνή συνεργασία «GEO Human Planet Initiative». Οι μέθοδοι GHSL βασίζονται σε αυτόματες τεχνολογίες εξόρυξης χωρικών δεδομένων που επιτρέπουν την εξαγωγή αναλύσεων και γνώσεων από μεγάλο όγκο ετερογενών δεδομένων, συμπεριλαμβανομένων παγκόσμιων ροών δεδομένων υψηλής κλίμακας δορυφορικών εικόνων, δεδομένων απογραφής και πηγών πλήθους ή εθελοντικών πηγών γεωγραφικών πληροφοριών. Η υποβολή εκθέσεων χωρικών δεδομένων αντικειμενικά και συστηματικά σχετικά με την παρουσία πληθυσμού και δομημένων υποδομών είναι αναγκαία για κάθε τεκμηριωμένη μοντελοποίηση ή αξιολόγηση i) της ανθρώπινης και φυσικής έκθεσης σε απειλές όπως η περιβαλλοντική ρύπανση και υποβάθμιση, οι φυσικές καταστροφές και συγκρούσεις, ii) ο αντίκτυπος των ανθρώπινων δραστηριοτήτων στα οικοσυστήματα και iii) η πρόσβαση σε πόρους. Η βάση δεδομένων του GHS Urban Centre (GHS- UCDB) περιγράφει χωρικές οντότητες που ονομάζονται «αστικά κέντρα» ανάλογα με ένα σύνολο πολυχρονικών θεματικών χαρακτηριστικών που συλλέγονται από τις πηγές GHSL ενσωματωμένες με άλλες πηγές διαθέσιμες στον ανοικτό επιστημονικό τομέα.Τα αστικά κέντρα ορίζονται από ειδικές τιμές αποκοπής για τον μόνιμο πληθυσμό και το δομημένο μερίδιο επιφάνειας σε ένα παγκόσμιο ομοιόμορφο πλέγμα 1x1 km. Τα δεδομένα εισόδου που παράγονται από το GHSL και οι παράμετροι λειτουργίας καθορίζονται στο πλαίσιο της μεθοδολογίας «βαθμού αστικοποίησης» (DEGURBA). Το DEGURBA είναι μια μεθοδολογία για την οριοθέτηση των αστικών και αγροτικών περιοχών για σκοπούς διεθνούς στατιστικής σύγκρισης που αναπτύσσεται από την Ευρωπαϊκή Επιτροπή, τον Οργανισμό Οικονομικής Συνεργασίας και Ανάπτυξης (ΟΟΣΑ), τον Οργανισμό Τροφίμων και Γεωργίας των Ηνωμένων Εθνών (FAO), το UN-Habitat και την Παγκόσμια Τράπεζα. Τα δεδομένα αναφοράς GHSL που χρησιμοποιούνται για την οριοθέτηση των αστικών κέντρων περιλαμβάνονται στην κοινοτική δέσμη μέτρων δεδομένων GHS (GHS CR2018) για την υποστήριξη της πρωτοβουλίας GEO Human Planet. Η παράμετρος που χρησιμοποιείται για την οριοθέτηση των αστικών κέντρων από τα εισερχόμενα δεδομένα περιλαμβάνεται στο μοντέλο ταξινόμησης διακανονισμού GHSL SMODv9s10E 2018. Η εποχή αναφοράς γι

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A Camada Global de Assentamento Humano (GHSL) produz novas informações espaciais globais, análises baseadas em evidências e conhecimentos que descrevem a presença humana no planeta. O Centro Comum de Investigação (JRC) e a Direção-Geral do Desenvolvimento Regional (DG REGIO) da Comissão Europeia apoiam as atividades da GHSL. A GHSL contribui para a parceria internacional «GEO Human Planet Initiative». Os métodos GHSL baseiam-se em tecnologias automáticas de prospeção de dados espaciais que permitem a extração de análises e conhecimentos a partir de uma grande quantidade de dados heterogéneos, incluindo fluxos de dados de imagens de satélite à escala global e fina, dados de recenseamento e fontes de multidões ou fontes de informação geográfica voluntárias. Para qualquer modelização ou avaliação baseada em dados concretos i) da exposição humana e física a ameaças como contaminação e degradação ambiental, catástrofes naturais e conflitos, ii) do impacto das atividades humanas nos ecossistemas e iii) do acesso aos recursos, são necessários dados espaciais que comuniquem de forma objetiva e sistemática a presença da população e das infraestruturas construídas. A Base de Dados de Centros Urbanos do GHS (GHS- UCDB) descreve entidades espaciais denominadas «centros urbanos» em conformidade com um conjunto de atributos temáticos multitemporais recolhidos a partir de fontes GHSL integradas com outras fontes disponíveis no domínio científico aberto. Os centros urbanos são definidos por valores-limite específicos relativos à população residente e à parte da superfície construída numa grelha global uniforme de 1x1 km. Os dados de entrada são gerados pela GHSL e os parâmetros de funcionamento são definidos no âmbito da metodologia do «grau de urbanização» (DEGURBA). O DEGURBA é uma metodologia para a delimitação de áreas urbanas e rurais feita para fins de comparação estatística internacional que é desenvolvida pela Comissão Europeia, a Organização para a Cooperação e Desenvolvimento Económico (OCDE), a Organização das Nações Unidas para a Alimentação e a Agricultura (FAO), a ONU-Habitat e o Banco Mundial. Os dados de entrada GHSL de referência utilizados para delinear os Centros Urbanos estão incluídos no Pacote de Dados GHS da Comunidade (GHS CR2018) em apoio à Iniciativa GEO Planeta Humano. O conjunto de parâmetros utilizado para delinear os Centros Urbanos a partir dos dados de entrada está incluído no modelo de classificação de liquidação GHSL SMODv9s10E 2018. A época de referência para a delimitação espacial dos Centros Urbanos é 2015. Os atributos do GHS-UCDB têm diferentes profundidades de tempo por um máximo de 40 anos, dependendo da disponibilidade das fontes de entrada.

La clasificación del grado de urbanización de la población andaluza en malla estadística elaborada por el Instituto de Estadística y Cartografía de Andalucía, es el primer paso para obtener información del nivel 1 y 2 del grado de urbanización (DEGURBA) del territorio de Andalucía. Se trata de una malla regular de celdas de 1km x 1km a las que, a partir de la georreferenciación de la población andaluza realizada en la distribución espacial de la población para el último año disponible (2022), se le ha aplicado la metodología descrita por EUROSTAT siguiendo el manual metodológico “Applying the Degree of Urbanisation -2021 edition”, para asignarle la tipología referida al nivel 1 y 2 del grado de urbanización, basándose en criterios de contigüidad geográfica, densidad y umbrales de población. También se muestra la malla regular de celdas uniformes de 250m de lado con población (siguiendo la directiva INSPIRE) y tiene por objeto depurar la malla anterior de 1kmx1km eliminando de cada celda de 1 kmx1km aquellas celdas de 250m de lado integradas en la celda y que no contienen población según la malla de población del año de referencia. Además con objeto de analizar la evolución de la intensidad de poblamiento y su grado de urbanización desde una mayor perspectiva temporal se ha calculado el nivel 1 del grado de urbanización para el año 2002.

La página web del Instituto de Estadística y Cartografía de Andalucía ofrece un servicio de visualización: "Clasificación del Grado de Urbanización” que permite su consulta interactiva https://www.juntadeandalucia.es/institutodeestadisticaycartografia/gradourbanizacion/index.htm