Actual value and historical data chart for Czech Republic Population Density People Per Sq Km

Czech Republic CZ: Population Density: People per Square Km data was reported at 136.108 Person/sq km in 2021. This records a decrease from the previous number of 138.576 Person/sq km for 2020. Czech Republic CZ: Population Density: People per Square Km data is updated yearly, averaging 133.733 Person/sq km from Dec 1993 (Median) to 2021, with 29 observations. The data reached an all-time high of 138.576 Person/sq km in 2020 and a record low of 131.927 Person/sq km in 2003. Czech Republic CZ: Population Density: People per Square Km data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Czech Republic – Table CZ.World Bank.WDI: Population and Urbanization Statistics. Population density is midyear population divided by land area in square kilometers. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship--except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. Land area is a country's total area, excluding area under inland water bodies, national claims to continental shelf, and exclusive economic zones. In most cases the definition of inland water bodies includes major rivers and lakes.;Food and Agriculture Organization and World Bank population estimates.;Weighted average;

Historical dataset showing Czech Republic population density by year from 1993 to 2022.

View yearly updates and historical trends for Czech Republic Population Density. Source: World Bank. Track economic data with YCharts analytics.

Population density of Czech Republic went up by 1.80% from 138.3 people per sq. km in 2022 to 140.8 people per sq. km in 2023. Since the 1.78% downward trend in 2021, population density improved by 3.44% in 2023. Population density is midyear population divided by land area in square kilometers.

Czech Republic CZ: Population Density: Inhabitants per sq km data was reported at 139.420 Person in 2022. This records an increase from the previous number of 136.040 Person for 2021. Czech Republic CZ: Population Density: Inhabitants per sq km data is updated yearly, averaging 133.700 Person from Dec 1990 (Median) to 2022, with 33 observations. The data reached an all-time high of 139.420 Person in 2022 and a record low of 82.230 Person in 1991. Czech Republic CZ: Population Density: Inhabitants per sq km data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Czech Republic – Table CZ.OECD.GGI: Social: Demography: OECD Member: Annual.

The Czechia: Population density, people per square km: The latest value from 2021 is 136 people per square km, a decline from 139 people per square km in 2020. In comparison, the world average is 456 people per square km, based on data from 196 countries. Historically, the average for the Czechia from 1993 to 2021 is 135 people per square km. The minimum value, 132 people per square km, was reached in 2001 while the maximum of 139 people per square km was recorded in 2020.

Population density in municipalities of the Czech Republic calculated on the basis of data from the public database of the CZSO. The number of inhabitants in individual municipalities is determined on the basis of statistical reports on birth and death and a set of removals, which is processed by the Czech Statistical Office following the results of the last census and the annual population balance of the Czech Republic for all municipalities. The territorial structure used is from the Register of Computational Circuits (RSO). The data are always processed on 1 January.

The viewing service displays the population density in the municipalities of the Czech Republic based on data from the public database of the ČSÚ. The territorial structure used is from the Register of Census Circuits (RSO). The data is in units ob./km². Processed as of 1 January.

人口密度：每平方公里人口在12-01-2021达136.108Person/sq km，相较于12-01-2020的138.576Person/sq km有所下降。人口密度：每平方公里人口数据按年更新，12-01-1993至12-01-2021期间平均值为133.733Person/sq km，共29份观测结果。该数据的历史最高值出现于12-01-2020，达138.576Person/sq km，而历史最低值则出现于12-01-2003，为131.927Person/sq km。CEIC提供的人口密度：每平方公里人口数据处于定期更新的状态，数据来源于World Bank，数据归类于全球数据库的捷克共和国 – Table CZ.World Bank.WDI: Population and Urbanization Statistics。

人口密度：每平方公里的居民在12-01-2022达139.420人，相较于12-01-2021的136.040人有所增长。人口密度：每平方公里的居民数据按年更新，12-01-1990至12-01-2022期间平均值为133.700人，共33份观测结果。该数据的历史最高值出现于12-01-2022，达139.420人，而历史最低值则出现于12-01-1991，为82.230人。CEIC提供的人口密度：每平方公里的居民数据处于定期更新的状态，数据来源于Organisation for Economic Co-operation and Development，数据归类于全球数据库的捷克共和国 – Table CZ.OECD.GGI: Social: Demography: OECD Member: Annual。

Species geographical distributions and abundances are a central focus of current ecological research. Although multiple studies have been conducted on their elucidation, some important information are still missing. One of them is the knowledge of ecological traits of species responsible for the population density variations across geographical (i.e. total physical area) and ecological spaces (i.e. suitable habitat area). This is crucial for understanding how ecological specialisation shapes the geographical distribution of species, and provides key knowledge about the sensitivity of species to current environmental challenges. Here, we precisely describe habitat availability for individual species using fine-scale field data collected across the entire Czech Republic. In the next step, we used this information to test the relationships between bird traits and country-scale estimates of population densities assessed in both geographical and ecological space. We did not find any effect of habitat specialisation on avian density in geographical space. But when we recalculated densities for ecological space available, we found a positive correlation with habitat specialization. Specialists occur at higher densities in suitable habitats. Moreover, birds with arboreal and hole-nesting strategies showed higher densities in both geographical and ecological spaces. However, we found no significant effects of morphological (body mass, structural body size) and reproductive (position along the slow-fast life-history continuum) traits on avian densities in either geographical or ecological space. Our findings suggest that ecological space availability is a strong determinant of avian abundance and highlight the importance of precise knowledge of species-specific habitat requirements. Revival of this classical but challenging ecological topic of habitat-specific densities is needed for both proper understanding of pure ecological issues and practical steps in the conservation of nature.

Distribution of biomarkers in the analytical sample as a total and by sex.

Results of ordinary least squares (OLS) regression models with dependent variable SRH, displaying regression coefficients, standardized coefficients (beta), standard errors (in parentheses), and significance level.

Statistical open data on LAU regions of Slovakia, Czech Republic, Poland, Hungary (and other countries in the future). LAU1 regions are called counties, okres, okresy, powiat, járás, járási, NUTS4, LAU, Local Administrative Units, ... and there are 733 of them in this V4 dataset. Overall, we cover 733 regions which are described by 137.828 observations (panel data rows) and more than 1.760.229 data points.

This LAU dataset contains panel data on population, on age structure of inhabitants, on number and on structure of registered unemployed. Dataset prepared by Michal Páleník. Output files are in json, shapefiles, xls, ods, json, topojson or CSV formats. Downloadable at zenodo.org.

This dataset consists of:

data on unemployment (by gender, education and duration of unemployment),

data on vacancies,

open data on population in Visegrad counties (by age and gender),

data on unemployment share.

Combined latest dataset

dataset of the latest available data on unemployment, vacancies and population

dataset includes map contours (shp, topojson or geojson format), relation id in OpenStreetMap, wikidata entry code,

it also includes NUTS4 code, LAU1 code used by national statistical office and abbreviation of the region (usually license plate),

source of map contours is OpenStreetMap, licensed under ODbL

no time series, only most recent data on population and unemployment combined in one output file

columns: period, lau, name, registered_unemployed, registered_unemployed_females, disponible_unemployed, low_educated, long_term, unemployment_inflow, unemployment_outflow, below_25, over_55, vacancies, pop_period, TOTAL, Y15-64, Y15-64-females, local_lau, osm_id, abbr, wikidata, population_density, area_square_km, way

Slovakia – SK: 79 LAU1 regions, data for 2024-10-01, 1.659 data,

Czech Republic – CZ: 77 LAU1 regions, data for 2024-10-01, 1.617 data,

Poland – PL: 380 LAU1 regions, data for 2024-09-01, 6.840 data,

Hungary – HU: 197 LAU1 regions, data for 2024-10-01, 2.955 data,

13.071 data in total.

column/number of observations description SK CZ PL HU

period period (month and year) the data is for 79 77 380 197

lau LAU code of the region 79 77 380 197

name name of the region in local language 79 77 380 197

registered_unemployed number of unemployed registered at labour offices 79 77 380 197

registered_unemployed_females number of unemployed women 79 77 380 197

disponible_unemployed unemployed able to accept job offer 79 77 0 0

low_educated unmployed without secondary school (ISCED 0 and 1) 79 77 380 197

long_term unemployed for longer than 1 year 79 77 380 0

unemployment_inflow inflow into unemployment 79 77 0 0

unemployment_outflow outflow from unemployment 79 77 0 0

below_25 number of unemployed below 25 years of age 79 77 380 197

over_55 unemployed older than 55 years 79 77 380 197

vacancies number of vacancies reported by labour offices 79 77 380 0

pop_period date of population data 79 77 380 197

TOTAL total population 79 77 380 197

Y15-64 number of people between 15 and 64 years of age, population in economically active age 79 77 380 197

Y15-64-females number of women between 15 and 64 years of age 79 77 380 197

local_lau region's code used by local labour offices 79 77 380 197

osm_id relation id in OpenStreetMap database 79 77 380 197

abbr abbreviation used for this region 79 77 380 0

wikidata wikidata identification code 79 77 380 197

population_density population density 79 77 380 197

area_square_km area of the region in square kilometres 79 77 380 197

way geometry, polygon of given region 79 77 380 197

Unemployment dataset

time series of unemployment data in Visegrad regions

by gender, duration of unemployment, education level, age groups, vacancies,

columns: period, lau, name, registered_unemployed, registered_unemployed_females, disponible_unemployed, low_educated, long_term, unemployment_inflow, unemployment_outflow, below_25, over_55, vacancies

Slovakia – SK: 79 LAU1 regions, data for 334 periods (1997-01-01 ... 2024-10-01), 202.082 data,

Czech Republic – CZ: 77 LAU1 regions, data for 244 periods (2004-07-01 ... 2024-10-01), 147.528 data,

Poland – PL: 380 LAU1 regions, data for 189 periods (2005-03-01 ... 2024-09-01), 314.100 data,

Hungary – HU: 197 LAU1 regions, data for 106 periods (2016-01-01 ... 2024-10-01), 104.408 data,

768.118 data in total.

column/number of observations description SK CZ PL HU

period period (month and year) the data is for 26 386 18 788 71 772 20 882

lau LAU code of the region 26 386 18 788 71 772 20 882

name name of the region in local language 26 386 18 788 71 772 20 882

registered_unemployed number of unemployed registered at labour offices 26 386 18 788 71 772 20 882

registered_unemployed_females number of unemployed women 26 386 18 788 62 676 20 882

disponible_unemployed unemployed able to accept job offer 25 438 18 788 0 0

low_educated unmployed without secondary school (ISCED 0 and 1) 11 771 9855 41 388 20 881

long_term unemployed for longer than 1 year 24 253 9855 41 388 0

unemployment_inflow inflow into unemployment 26 149 16 478 0 0

unemployment_outflow outflow from unemployment 26 149 16 478 0 0

below_25 number of unemployed below 25 years of age 11 929 9855 17 100 20 881

over_55 unemployed older than 55 years 11 929 9855 17 100 20 882

vacancies number of vacancies reported by labour offices 11 692 18 788 62 676 0

Population dataset

time series on population by gender and 5 year age groups in V4 counties

columns: period, lau, name, gender, TOTAL, Y00-04, Y05-09, Y10-14, Y15-19, Y20-24, Y25-29, Y30-34, Y35-39, Y40-44, Y45-49, Y50-54, Y55-59, Y60-64, Y65-69, Y70-74, Y75-79, Y80-84, Y85-89, Y90-94, Y_GE95, Y15-64

Slovakia – SK: 79 LAU1 regions, data for 28 periods (1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023), 152.628 data,

Czech Republic – CZ: 78 LAU1 regions, data for 24 periods (2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023), 125.862 data,

Poland – PL: 382 LAU1 regions, data for 29 periods (1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023), 626.941 data,

Hungary – HU: 197 LAU1 regions, data for 11 periods (2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023), 86.680 data,

992.111 data in total.

column/number of observations description SK CZ PL HU

period period (month and year) the data is for 6636 5574 32 883 4334

lau LAU code of the region 6636 5574 32 883 4334

name name of the region in local language 6636 5574 32 883 4334

gender gender (male or female) 6636 5574 32 883 4334

TOTAL total population 6636 5574 32 503 4334

Y00-04 inhabitants between 00 to 04 years inclusive 6636 5574 32 503 4334

Y05-09 number of inhabitants between 05 to 09 years of age 6636 5574 32 503 4334

Y10-14 number of people between 10 to 14 years inclusive 6636 5574 32 503 4334

Y15-19 number of inhabitants between 15 to 19 years of age 6636 5574 32 503 4334

Y20-24 number of people between 20 to 24 years inclusive 6636 5574 32 503 4334

Y25-29 number of inhabitants between 25 to 29 years of age 6636 5574 32 503 4334

Y30-34 inhabitants between 30 to 34 years inclusive 6636 5574 32 503 4334

Y35-39 number of inhabitants between 35 to 39 years of age 6636 5574 32 503 4334

Y40-44 inhabitants between 40 to 44 years inclusive 6636 5574 32 503 4334

Y45-49 number of inhabitants younger than 49 and older than 45 years 6636 5574 32 503 4334

Y50-54 inhabitants between 50 to 54 years inclusive 6636 5574 32 503 4334

Y55-59 number of inhabitants between 55 to 59 years of age 6636 5574 32 503 4334

Y60-64 inhabitants between 60 to 64 years inclusive 6636 5574 32 503 4334

Y65-69 number of inhabitants younger than 69 and older than 65 years 6636 5574 32 503 4334

Y70-74 inhabitants between 70 to 74 years inclusive 6636 5574 24 670 4334

Y75-79 number of inhabitants between 75 to 79 years of age 6636 5574 24 670 4334

Y80-84 number of people between 80 to 84 years inclusive 6636 5574 24 670 4334

Y85-89 number of inhabitants younger than 89 and older than 85 years 6636 5574 0 0

Y90-94 inhabitants between 90 to 94 years inclusive 6636 5574 0 0

Y_GE95 number of people 95 years or older 6636 3234 0 0

Y15-64 number of people between 15 and 64 years of age, population in economically active age 6636 5574 32 503 4334

Notes

more examples at www.iz.sk

NUTS4 / LAU1 / LAU codes for HU and PL are created by me, so they can (and will) change in the future; CZ and SK NUTS4 codes are used by local statistical offices, so they should be more stable

NUTS4 codes are consistent with NUTS3 codes used by Eurostat

local_lau variable is an identifier used by local statistical office

abbr is abbreviation of region's name, used for map purposes (usually cars' license plate code; except for Hungary)

wikidata is code used by wikidata

osm_id is region's relation number in the OpenStreetMap database

Example outputs

you can download data in CSV, xml, ods, xlsx, shp, SQL, postgis, topojson, geojson or json format at 📥 doi:10.5281/zenodo.6165135

Counties of Slovakia – unemployment rate in Slovak LAU1 regions

Regions of the Slovak Republic

Unemployment of Czechia and Slovakia – unemployment share in LAU1 regions of Slovakia and Czechia

interactive map on unemployment in Slovakia

Slovakia – SK, Czech Republic – CZ, Hungary – HU, Poland – PL, NUTS3 regions of Slovakia

download at 📥 doi:10.5281/zenodo.6165135

suggested citation: Páleník, M. (2024). LAU1 dataset [Data set]. IZ Bratislava. https://doi.org/10.5281/zenodo.6165135

Climate limits the distribution of species and varies along a latitudinal gradient. However, studies relating climate variation to species population growth rates in different climatic zones while taking into account species’ ecological traits are scarce. We assessed species population responses to the main climatic constraints at a continental scale by studying how precipitation and temperature at different latitudes influence interannual growth rates while considering species’ life-history traits. We gathered data on the abundance of 141 European breeding bird species from national breeding bird monitoring schemes in the Mediterranean (Catalonia, NE Spain), temperate (Czech Republic), and boreal (Sweden) climatic zones for the period 2002–2022. We used generalized linear models to relate the interannual population growth rates of bird species to spring and winter temperature, water availability and heavy rainfall in the breeding season. We considered migration strategy as a covariate ..., , , # Code and data for: Latitude-specific responses of European birds' population growth rates to temperature and water availability

Data and R script allowing to reproduce the modelling in the accompanied journal article. The models are stored for convenience. All files are available in Climate_growth_rate.zip.

Description of the data and file structure

File tree

• data
• model

Data

dt_growth_r_clim.csv Data set containing the following variables:

• EURING = bird species code
• Spec_name = bird species name
• Reg = breeding region, i.e., MED = Mediterranean, CONT = Continental, S_BOR = Southern Boreal, N_BOR = Northern Boreal
• Year = year corresponding to breeding season (2002 to 2021)
• Growth_r = interannual population growth rate (ratio of annual population indices in year t+1 and t)
• Growth_r_SE = standard error of population growth rate
• Dens = population density (log-transformed annual population index)
• Dens_SE = standard error of popul...,

Concentrations of air pollutants in the 1x1 km grid, year 2023. Limit values for the protection of human health SO2 - 4th highest 24-hour SO2 concentration [µg.m-3], PM10 - annual average concentration [µg.m-3], PM10 - 36th highest 24-hour average concentration [µg.m-3], PM2.5 - annual average concentration [µg. m-3], NO2 - annual average concentration [µg.m-3], O3 - 26th highest maximum daily 8-hour moving average concentration averaged over 3 years, 2019-2021 [µg.m-3], As - annual average concentration [ng.m-3], Cd - annual average concentration [ng.m-3], benzo[a]pyrene - annual average concentration [ng.m-3], benzene - annual average concentration [µg.m-3]. Areas with exceedances of limit values without O3. Areas with exceedances of the limit values with the inclusion of O3. Limit values for the protection of ecosystems and vegetation O3 - AOT40 exposure index values, 5-year average, 2019-2023 [µg.m-3], NOx - annual average concentration [µg.m-3], SO2 - annual (annual average) and winter period (winter average) [µg.m-3].

Legislation (Act 201/2012 Coll., as amended) requires that the primary source of assessment be the results of stationary measurements. Measured concentrations may be supplemented by modelling and indicative measurements in the production of pollutant maps to ensure that the resulting estimate provides sufficient information on the spatial distribution of air pollutant concentrations. In the Czech Republic, the Eulerian chemical dispersion model CAMx is mainly used, additionally also the Gaussian model SYMOS and the European Eulerian model EMEP. In addition, in the case of individual pollutants, e.g. altitude or population density.

チェコの人口密度を国土面積と総人口から算出し最新の推移グラフや日本との比較表、世界人口密度ランキング(狭い)等を用い、人口密度が低いのか高いのかを説明しています。各種データはcsv出力・ダウンロードも可能です。（EXCELでも使用可能）元データのソースはworldbank.orgで、当サイト(GraphToChart)が独自に計算・算出し全て無料で利用可能ですので、研究や分析レポートにお役立て頂ければ幸いです。