Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Daily population and trips per person data from Spain 2020-2021
This repository contains daily population records based on a study conducted by the MITMA, that analysed the mobility and distribution of the population in Spain from February 14th 2020 to May 9th 2021. The study is based on a sample of more than 13 million anonymised mobile phone lines provided by a single mobile operator whose subscribers are evenly distributed.
For more information on the data visit: https://www.mitma.gob.es/ministerio/covid-19/evolucion-movilidad-big-data
Data provided by MITMA is related to the layer mitma_mov. For the rest of the layers, the population was estimated using the population grid from GEOSTAT: https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/population-distribution-demography/geostat
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Synthetic populations for regions of the World (SPW) | Spain
Dataset information
A synthetic population of a region as provided here, captures the people of the region with selected demographic attributes, their organization into households, their assigned activities for a day, the locations where the activities take place and thus where interactions among population members happen (e.g., spread of epidemics).
License
Acknowledgment
This project was supported by the National Science Foundation under the NSF RAPID: COVID-19 Response Support: Building Synthetic Multi-scale Networks (PI: Madhav Marathe, Co-PIs: Henning Mortveit, Srinivasan Venkatramanan; Fund Number: OAC-2027541).
Contact information
Henning.Mortveit@virginia.edu
Identifiers
Region name | Spain |
Region ID | esp |
Model | coarse |
Version | 0_9_0 |
Statistics
Name | Value |
---|---|
Population | 45639013.0 |
Average age | 41.1 |
Households | 17918332.0 |
Average household size | 2.6 |
Residence locations | 17918332.0 |
Activity locations | 5782846.0 |
Average number of activities | 5.6 |
Average travel distance | 130.4 |
Sources
Description | Name | Version | Url |
---|---|---|---|
Activity template data | World Bank | 2021 | https://data.worldbank.org |
Administrative boundaries | ADCW | 7.6 | https://www.adci.com/adc-worldmap |
Curated POIs based on OSM | SLIPO/OSM POIs | http://slipo.eu/?p=1551 https://www.openstreetmap.org/ | |
Household data | IPUMS | https://international.ipums.org/international | |
Population count with demographic attributes | GPW | v4.11 | https://sedac.ciesin.columbia.edu/data/set/gpw-v4-admin-unit-center-points-population-estimates-rev11 |
Files description
Base data files (esp_data_v_0_9.zip)
Filename | Description |
---|---|
esp_person_v_0_9.csv | Data for each person including attributes such as age, gender, and household ID. |
esp_household_v_0_9.csv | Data at household level. |
esp_residence_locations_v_0_9.csv | Data about residence locations |
esp_activity_locations_v_0_9.csv | Data about activity locations, including what activity types are supported at these locations |
esp_activity_location_assignment_v_0_9.csv | For each person and for each of their activities, this file specifies the location where the activity takes place |
Derived data files
Filename | Description |
---|---|
esp_contact_matrix_v_0_9.csv | A POLYMOD-type contact matrix constructed from a network representation of the location assignment data and a within-location contact model. |
Validation and measures files
Filename | Description |
---|---|
esp_household_grouping_validation_v_0_9.pdf | Validation plots for household construction |
esp_activity_durations_{adult,child}_v_0_9.pdf | Comparison of time spent on generated activities with survey data |
esp_activity_patterns_{adult,child}_v_0_9.pdf | Comparison of generated activity patterns by the time of day with survey data |
esp_location_construction_0_9.pdf | Validation plots for location construction |
esp_location_assignement_0_9.pdf | Validation plots for location assignment, including travel distribution plots |
esp_esp_ver_0_9_0_avg_travel_distance.pdf | Choropleth map visualizing average travel distance |
esp_esp_ver_0_9_0_travel_distr_combined.pdf | Travel distance distribution |
esp_esp_ver_0_9_0_num_activity_loc.pdf | Choropleth map visualizing number of activity locations |
esp_esp_ver_0_9_0_avg_age.pdf | Choropleth map visualizing average age |
esp_esp_ver_0_9_0_pop_density_per_sqkm.pdf | Choropleth map visualizing population density |
esp_esp_ver_0_9_0_pop_size.pdf | Choropleth map visualizing population size |
In 2023, approximately 127.1 million people lived in Guangdong province in China. That same year, only about 3.65 million people lived in the sparsely populated highlands of Tibet. Regional differences in China China is the world’s most populous country, with an exceptional economic growth momentum. The country can be roughly divided into three regions: Western, Eastern, and Central China. Western China covers the most remote regions from the sea. It also has the highest proportion of minority population and the lowest levels of economic output. Eastern China, on the other hand, enjoys a high level of economic development and international corporations. Central China lags behind in comparison to the booming coastal regions. In order to accelerate the economic development of Western and Central Chinese regions, the PRC government has ramped up several incentive plans such as ‘Rise of Central China’ and ‘China Western Development’. Economic power of different provinces When observed individually, some provinces could stand an international comparison. Jiangxi province, for example, a medium-sized Chinese province, had a population size comparable to Argentina or Spain in 2023. That year, the GDP of Zhejiang, an eastern coastal province, even exceeded the economic output of the Netherlands. In terms of per capita annual income, the municipality of Shanghai reached a level close to that of the Czech Republik. Nevertheless, as shown by the Gini Index, China’s economic spur leaves millions of people in dust. Among the various kinds of economic inequality in China, regional or the so-called coast-inland disparity is one of the most significant. Posing as evidence for the rather large income gap in China, the poorest province Heilongjiang had a per capita income similar to that of Sri Lanka that year.
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Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Daily population and trips per person data from Spain 2020-2021
This repository contains daily population records based on a study conducted by the MITMA, that analysed the mobility and distribution of the population in Spain from February 14th 2020 to May 9th 2021. The study is based on a sample of more than 13 million anonymised mobile phone lines provided by a single mobile operator whose subscribers are evenly distributed.
For more information on the data visit: https://www.mitma.gob.es/ministerio/covid-19/evolucion-movilidad-big-data
Data provided by MITMA is related to the layer mitma_mov. For the rest of the layers, the population was estimated using the population grid from GEOSTAT: https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/population-distribution-demography/geostat