兵庫

  • 首都:Kōbe-shi
  • 知事:Toshizo Ido
  • 総人口(千人):5558 (2013)
  • 総面積(Sq.km ):8396.47 (2013)
  • 人口密度(Sq.km当たりの人口):2,007.2 (2012)
  • 県内総生産(百万円):18313629 (2011)
  • 県内総生産の年間増加率(%):-1.2 (2011)
  • 一人当たりの県民所得(千円):2585 (2011)
  • CPIの年間増加率(%):-0.5
  • 当該地域の公式ウェブサイト
  • 失業率(%):6.5 (2010)
  • 賃金、男性(月額平均、千円):364.0 (2011)
  • 賃金、女性(月額平均、千円):249.5 (2011)
  • 世帯あたりの毎月の生活費(千円):272.4 (2011)
  • 1000人あたりの個人使用向けの乗用車 (数):407.7 (2013)
  • 合計特殊出生率(出産)(女性一人当たりの子ども):1.42 (2013)
  • 粗死亡率(1000人当たり):9.78 (2013)
  • 人口の自然増加率(%):-0.07 (2010)
  • 教師一人当たりの小学生(人):16.96 (2011)
  • 主要道路の実際の合計の長さ(1 Sq.km当たり km):4.29 (2012)
  • 犯罪率(人口10万人当たり):58.4 (2010)

比較

すべてのデータセット: A C E I J M
  • A
    • 6月 2024
      ソース: Eurostat
      アップロード者: Knoema
      以下でアクセス: 22 6月, 2024
      データセットを選択
      Eurostat Dataset Id:ilc_li01 The domain "Income and living conditions" covers four topics: people at risk of poverty or social exclusion, income distribution and monetary poverty, living conditions and material deprivation, which are again structured into collections of indicators on specific topics. The collection "People at risk of poverty or social exclusion" houses main indicator on risk of poverty or social inclusion included in the Europe 2020 strategy as well as the intersections between sub-populations of all Europe 2020 indicators on poverty and social exclusion. The collection "Income distribution and monetary poverty" houses collections of indicators relating to poverty risk, poverty risk of working individuals as well as the distribution of income. The collection "Living conditions" hosts indicators relating to characteristics and living conditions of households, characteristics of the population according to different breakdowns, health and labour conditions, housing conditions as well as childcare related indicators. The collection "Material deprivation" covers indicators relating to economic strain, durables, housing deprivation and environment of the dwelling.
  • C
    • 12月 2018
      ソース: Institute for Health Metrics and Evaluation
      アップロード者: Knoema
      以下でアクセス: 02 1月, 2019
      データセットを選択
      Data cited: Global Burden of Disease Collaborative Network. Global Burden of Disease Study 2016 (GBD 2016) Cancer Incidence, Mortality, Years of Life Lost, Years Lived with Disability, and Disability-Adjusted Life Years 1990-2016. Seattle, United States: Institute for Health Metrics and Evaluation (IHME), 2018.   The Global Burden of Disease Study 2016 (GBD 2016), coordinated by the Institute for Health Metrics and Evaluation (IHME), estimated the burden of diseases, injuries, and risk factors for 195 countries and territories and at the subnational level for a subset of countries. Estimates for deaths, disability-adjusted life years (DALYs), years lived with disability (YLDs), years of life lost (YLLs), prevalence, and incidence for 29 cancer groups by age and sex for 1990-2016 are available from the GBD Results Tool. Files available in this record are the web tables published in JAMA Oncology in June 2018 in "Global, Regional, and National Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability-Adjusted Life-years for 29 Cancer Groups, 1990 to 2016."
    • 3月 2020
      ソース: Eurostat
      アップロード者: Knoema
      以下でアクセス: 18 3月, 2020
      データセットを選択
      Eurostat Dataset Id:cpc_ecgov  The focus of this domain is on the following country groups:Acceeding country: Croatia (HR)Candidate countries: the former Yugoslav Republic of Macedonia (MK), Montenegro (ME), Iceland (IS), Serbia (RS) and Turkey (TR)Potential candidate countries: Albania (AL), Bosnia and Herzegovina (BA), as well as Kosovo under UNSCR 1244/99 (XK)
    • 10月 2022
      ソース: Google
      アップロード者: Knoema
      以下でアクセス: 04 5月, 2023
      データセットを選択
      These Community Mobility Reports aim to provide insights into what has changed in response to policies aimed at combating COVID-19. The reports chart movement trends over time by geography, across different categories of places such as retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential.
  • E
    • 1月 2024
      ソース: Eurostat
      アップロード者: Knoema
      以下でアクセス: 11 1月, 2024
      データセットを選択
      Eurostat Dataset Id:bd_9fh_sz_cl_r2 The data category covers a group of variables which explain the characteristics and demography of the business population. The methodology allows for the production of data on enterprise births (and deaths), that is, enterprise creations (cessations) that amount to the creation (dissolution) of a combination of production factors and where no other enterprises are involved. In other words, enterprises created or closed solely as a result of e.g. restructuring, merger or break-up are not included in this data. Until 2010 reference year the harmonised data collection is carried out to satisfy the requirements for the Structural Indicators, used for monitoring progress of the Lisbon process, regarding business births, deaths and survival. It also provides key data for the joint OECD-Eurostat "Entrepreneurship Indicators Programme". In summary, the collected indicators are as follows:Population of active enterprisesNumber of enterprise birthsNumber of enterprise survivals up to five yearsNumber of enterprise deathsRelated variables on employmentDerived indicators such as birth rates, death rates, survival rates and employment sharesAn additional set of indicators on high-growth enterprises and 'gazelles' (high-growth enterprises that are up to five years old) The data are drawn from business registers, although some individual countries improve the availability or freshness of data on employment and turnover by integrating other sources. Geographically EU Member States and EFTA countries are covered. In practice not all Member States have participated in the first harmonised data collection exercises.
    • 1月 2020
      ソース: Eurostat
      アップロード者: Knoema
      以下でアクセス: 15 1月, 2020
      データセットを選択
      Eurostat Dataset Id:enpr_inisoc The domain focuses on the Eastern European Neighbourhood Policy countries (ENP): Armenia (AM), Azerbaijan (AZ), Belarus (BY), Georgia (GE), Moldova (MD) and the Ukraine (UA). Data are provided for 200 to 300 indicators.
    • 1月 2020
      ソース: Eurostat
      アップロード者: Knoema
      以下でアクセス: 15 1月, 2020
      データセットを選択
      Eurostat Dataset Id:enpr_scienc The domain focuses on the Eastern European Neighbourhood Policy countries (ENP): Armenia (AM), Azerbaijan (AZ), Belarus (BY), Georgia (GE), Moldova (MD) and the Ukraine (UA). Data are provided for 200 to 300 indicators.
  • I
    • 3月 2024
      ソース: Eurostat
      アップロード者: Knoema
      以下でアクセス: 09 3月, 2024
      データセットを選択
      Eurostat Dataset Id:sbs_sc_ind_r2 SBS covers the Nace Rev.2 Section B to N and division S95 which are organized in four annexes, covering Industry (sections B-E), Construction (F), Trade (G) and Services (H, I, J, L, M, N and S95). Financial services are covered in three specific annexes and separate metadata files have been compiled. Up to reference year 2007 data was presented using the NACE Rev.1.1 classification. The SBS coverage was limited to NACE Rev.1.1 Sections C to K. Starting from the reference year 2008 data is available in NACE Rev.2. Double reported data in NACE Rev.1.1 for the reference year 2008 will be available in the first and second quarter of 2011. Main characteristics (variables) of the SBS data category:Business Demographic variables (e.g. number of enterprises)"Output related" variables (e.g. Turnover, Value added)"Input related" variables               - labour input (e.g. Employment, Hours worked)               - goods and services input (e.g. Total of purchases)               - capital input (e.g. Material investments) Several important derived indicators are generated in the form of ratios of certain monetary characteristics or per head values. Annual enterprise statistics: Characteristics collected are published by country and detailed on NACE Rev 2 and NACE Rev 1.1 class level (4 digits). Some classes or groups in 'services' in NACE Rev 1.1 sections H, I, K have been aggregated. Annual enterprise statistics broken down by size classes: Characteristics are published by country and detailed down to NACE Rev 2 and NACE Rev 1.1 group level (3-digits) and employment size class. For trade (NACE Rev2 and NACE Rev 1.1 Section G) a supplementary breakdown by turnover size class is available. Annual regional statistics: Four characteristics are published by NUTS-2 country region and detailed on NACE Rev 2 and NACE Rev 1.1 division level (2-digits) (but to group level for the trade section). More information on the contents of different tables: the detail level and breakdowns required starting with the reference year is defined in Commission Regulation N° 251/2009.  For previous reference years it is included in Commission Regulations (EC) N° 2701/98 and amended by N°1614/2002 and N°1669/2003. SBS data are collected primarily by National Statistical Institutes (NSI). Regulatory or controlling national offices for financial institutions or central banks often provides the information required for the financial sector (NACE Rev 2 Section K / NACE  Rev 1.1 Section J). 
  • J
    • 3月 2023
      ソース: The Center for Systems Science and Engineering at JHU
      アップロード者: Knoema
      以下でアクセス: 13 3月, 2023
      データセットを選択
      Data cited at: Prof.Prof. Lauren Gardner; Center for Systems Science and Engineering at John Hopkins University, blog Post -  https://systems.jhu.edu/research/public-health/ncov/   On December 31, 2019, the World Health Organization (WHO) was informed of an outbreak of “pneumonia of unknown cause” detected in Wuhan City, Hubei Province, China – the seventh-largest city in China with 11 million residents. As of February 04, 2020, there are over 24,502 cases confirmed globally, including cases in at least 30 regions in China and 30 countries.  Interests: In-Market Segments Knoema All Users   Knoema modified the original dataset to include calculations per million.   https://knoema.com/WBPEP2018Oct https://knoema.com/USICUBDS2020 https://knoema.com/NBSCN_P_A_A0301 https://knoema.com/IMFIFSS2017Nov https://knoema.com/AUDSS2019 https://knoema.com/UNAIDSS2017 https://knoema.com/UNCTADPOPOCT2019Nov https://knoema.com/WHOWSS2018 https://knoema.com/KPMGDHC2019
  • M
    • 4月 2022
      ソース: Apple, Inc.
      アップロード者: Knoema
      以下でアクセス: 14 4月, 2022
      データセットを選択
      We define our day as midnight-to-midnight, Pacific time. Cities represent usage in greater metropolitan areas and are stably defined during this period. In many countries/regions and cities, relative volume has increased since January 13th, consistent with normal, seasonal usage of Apple Maps. Day of week effects are important to normalize as you use this data. Data that is sent from users’ devices to the Maps service is associated with random, rotating identifiers so Apple doesn’t have a profile of your movements and searches. Apple Maps has no demographic information about our users, so we can’t make any statements about the representativeness of our usage against the overall population. This information will be available for a limited time during the COVID‑19 pandemic.
    • 7月 2022
      ソース: Petroleum Association of Japan
      アップロード者: Knoema
      以下でアクセス: 25 8月, 2022
      データセットを選択
      日本における2015年1月〜2016年12月の石油販売実績(府県別)の月次データ。 Calendar Yearには1月から12月のデータが含まれます。 会計年度には4月から3月のデータが含まれます。