38 datasets found
  1. Population density in Tokyo Prefecture, Japan 2000-2020

    • statista.com
    Updated Jul 11, 2025
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    Statista (2025). Population density in Tokyo Prefecture, Japan 2000-2020 [Dataset]. https://www.statista.com/statistics/673679/japan-population-density-toyko/
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    Dataset updated
    Jul 11, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Japan
    Description

    In 2020, the population of Tokyo Metropolis amounted to over ***** inhabitants per square kilometer. The number increased from approximately ***** inhabitants per square kilometer in 2000.

  2. e

    Japan - Population density - Dataset - ENERGYDATA.INFO

    • energydata.info
    Updated Jul 23, 2024
    + more versions
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    (2024). Japan - Population density - Dataset - ENERGYDATA.INFO [Dataset]. https://energydata.info/dataset/japan--population-density-2015
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    Dataset updated
    Jul 23, 2024
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Japan
    Description

    Population density per pixel at 100 metre resolution. WorldPop provides estimates of numbers of people residing in each 100x100m grid cell for every low and middle income country. Through ingegrating cencus, survey, satellite and GIS datasets in a flexible machine-learning framework, high resolution maps of population counts and densities for 2000-2020 are produced, along with accompanying metadata. DATASET: Alpha version 2010 and 2015 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and remaining unadjusted. REGION: Africa SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator) PROJECTION: Geographic, WGS84 UNITS: Estimated persons per grid square MAPPING APPROACH: Land cover based, as described in: Linard, C., Gilbert, M., Snow, R.W., Noor, A.M. and Tatem, A.J., 2012, Population distribution, settlement patterns and accessibility across Africa in 2010, PLoS ONE, 7(2): e31743. FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Example - AGO10adjv4.tif = Angola (AGO) population count map for 2010 (10) adjusted to match UN national estimates (adj), version 4 (v4). Population maps are updated to new versions when improved census or other input data become available.

  3. Population density in the Greater Tokyo Area 1985-2015

    • statista.com
    Updated Dec 1, 2016
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    Statista (2016). Population density in the Greater Tokyo Area 1985-2015 [Dataset]. https://www.statista.com/statistics/673621/japan-population-density-greater-toyko/
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    Dataset updated
    Dec 1, 2016
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    1985 - 2015
    Area covered
    Japan
    Description

    The statistic presents the population density in the Greater Tokyo Area in Japan from 1985 to 2015. In 1985, Greater Tokyo's population amounted to ***** inhabitants per square kilometer. This number increased to almost ***** inhabitants per square kilometer in 2015.

  4. Population aged 15 years and older in Tokyo Prefecture, Japan 2014-2023, by...

    • statista.com
    Updated Dec 5, 2024
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    Statista Research Department (2024). Population aged 15 years and older in Tokyo Prefecture, Japan 2014-2023, by gender [Dataset]. https://www.statista.com/topics/9914/tokyo/
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    Dataset updated
    Dec 5, 2024
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Statista Research Department
    Area covered
    Tokyo, Prefectures of Japan
    Description

    In 2023, there were close to 12.6 million people aged 15 years and older in Tokyo Prefecture, of which about 51 percent were women. The population aged 15 years and older in the prefecture decreased in 2021 for the first time in the last decade.

  5. Official land price Tokyo 2025, by district

    • statista.com
    • thefarmdosupply.com
    Updated Dec 5, 2024
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    Statista Research Department (2024). Official land price Tokyo 2025, by district [Dataset]. https://www.statista.com/topics/9914/tokyo/
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    Dataset updated
    Dec 5, 2024
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Statista Research Department
    Area covered
    Tokyo
    Description

    Tokyo's district Chuo had the highest average official land price in 2025, with about 9.2 million Japanese yen per square meter. Next to Chiyoda, Shibuya, Minato, and Shinjuku, Chuo is one of five central business districts in Japan's capital.

  6. a

    Growth of Megacities-Tokyo

    • gis-for-secondary-schools-schools-be.hub.arcgis.com
    • fesec-cesj.opendata.arcgis.com
    • +2more
    Updated Sep 8, 2014
    + more versions
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    ArcGIS StoryMaps (2014). Growth of Megacities-Tokyo [Dataset]. https://gis-for-secondary-schools-schools-be.hub.arcgis.com/maps/1fa848e239a34889b0f943b3891be736
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    Dataset updated
    Sep 8, 2014
    Dataset authored and provided by
    ArcGIS StoryMaps
    Area covered
    Description

    The Global Human Footprint dataset of the Last of the Wild Project, version 2, 2005 (LWPv2) is the Human Influence Index (HII) normalized by biome and realm. The HII is a global dataset of 1 km grid cells, created from nine global data layers covering human population pressure (population density), human land use and infraestructure (built-up areas, nighttime lights, land use/land cover) and human access (coastlines, roads, navigable rivers).The Human Footprint Index (HF) map, expresses as a percentage the relative human influence in each terrestrial biome. HF values from 0 to 100. A value of zero represents the least influence -the "most wild" part of the biome with value of 100 representing the most influence (least wild) part of the biome.

  7. Number of men in Tokyo Prefecture, Japan 2014-2023

    • statista.com
    Updated Dec 5, 2024
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    Statista Research Department (2024). Number of men in Tokyo Prefecture, Japan 2014-2023 [Dataset]. https://www.statista.com/topics/9914/tokyo/
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    Dataset updated
    Dec 5, 2024
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Statista Research Department
    Area covered
    Tokyo, Prefectures of Japan
    Description

    In 2023, the male population in Tokyo Prefecture amounted to around 6.6 million. The number of men in the prefecture declined in 2021 for the first time in the past decade.

  8. Consumer Price Index (CPI) in Tokyo Prefecture, Japan 1990-2024

    • statista.com
    Updated Dec 5, 2024
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    Statista Research Department (2024). Consumer Price Index (CPI) in Tokyo Prefecture, Japan 1990-2024 [Dataset]. https://www.statista.com/topics/9914/tokyo/
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    Dataset updated
    Dec 5, 2024
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Statista Research Department
    Area covered
    Tokyo, Prefectures of Japan
    Description

    In 2024, the Consumer Price Index (CPI) in Tokyo Prefecture rose to 107.9 index points, reaching the highest level in over 30 years. The lowest consumer price level in the observed period was recorded in 1990 at 93.1 points.

  9. f

    Estimation results for detailed periods.

    • plos.figshare.com
    xls
    Updated Jun 4, 2023
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    Kazufumi Tsuboi; Naoya Fujiwara; Ryo Itoh (2023). Estimation results for detailed periods. [Dataset]. http://doi.org/10.1371/journal.pone.0276741.t002
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    xlsAvailable download formats
    Dataset updated
    Jun 4, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Kazufumi Tsuboi; Naoya Fujiwara; Ryo Itoh
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Estimation results for detailed periods.

  10. c

    Living in Chiba

    • city-cost.com
    Updated Dec 19, 2018
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    City-Cost (2018). Living in Chiba [Dataset]. https://www.city-cost.com/stats/chiba
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    Dataset updated
    Dec 19, 2018
    Dataset authored and provided by
    City-Cost
    License

    https://www.e-stat.go.jp/en/terms-of-usehttps://www.e-stat.go.jp/en/terms-of-use

    Area covered
    Chiba, Japan
    Description

    Chiba Prefecture borders the east side of Tokyo and is considered a part of the Tokyo Metropolitan Area. Many large cities can be found in Chiba, including Chiba City, the capital, and Funabashi. Despite a high population density and bedroom communities connected with Tokyo, Chiba Prefecture boasts the second highest agricultural industry output in Japan. The most famous produce of which is peanuts. There are also a large number of manufacturing centers and shipping ports. Financially, Chiba Prefecture is one of the richest prefectures in Japan. Tokyo Disney Resort can be found in Urayasu, Chiba, as well as Narita International Airport in Narita.

  11. Average monthly consumption spending per household in Tokyo, Japan 2015-2024...

    • statista.com
    Updated Dec 5, 2024
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    Statista Research Department (2024). Average monthly consumption spending per household in Tokyo, Japan 2015-2024 [Dataset]. https://www.statista.com/topics/9914/tokyo/
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    Dataset updated
    Dec 5, 2024
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Statista Research Department
    Area covered
    Tokyo
    Description

    In 2024, the monthly consumption expenditures per household in Tokyo Prefecture amounted to around 341,600 Japanese yen on average. In real terms, the monthly consumption expenditure of households in Tokyo declined by 0.8 percent.

  12. Tokyo Vulerability and Healthcare Accessibility

    • figshare.com
    zip
    Updated Jul 8, 2022
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    Siqin Wang (2022). Tokyo Vulerability and Healthcare Accessibility [Dataset]. http://doi.org/10.6084/m9.figshare.20268738.v1
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    zipAvailable download formats
    Dataset updated
    Jul 8, 2022
    Dataset provided by
    Figsharehttp://figshare.com/
    Authors
    Siqin Wang
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Tokyo
    Description

    The ongoing multi-wave COVID-19 pandemic has disproportional impacts on people with different demographic and socioeconomic background, and their access to healthcare facilities. Vulnerable neighborhoods with low healthcare access are places most needed for the enhancement of medical resources and services. Measuring vulnerability to COVID-19 and healthcare accessibility at the fine-grained level serves as the foundation for spatially explicit health planning and policy making in response to future public health crisis. Despite of its importance, the evaluation of vulnerability and healthcare accessibility is insufficient in Japan—a nation with high population density and super-aging challenge. Drawing on the latest 2022 census data at the smallest statistical unit, as well as transport network, medical and digital cadastral data, land use maps, and points of interest data, our study reformulates the concept of vulnerability in the context of COVID-19 and constructs the first fine-grained measure of vulnerability and healthcare accessibility in Tokyo Metropolis, Japan—the most popular metropolitan region in the world. We delineate the vulnerable neighborhoods with low healthcare access and further evaluate the disparity in healthcare access and built environment of areas at different levels of vulnerability. Our outcome datasets and findings provide nuanced and timely evidence to government and health authorities to have a holistic and latest understanding of social vulnerability to COVID-19 and healthcare access at a fine-grained level. Our analytical framework can be employed to different geographic contexts, guiding through the place-based health planning and policy making in the post-COVID era and beyond.

  13. f

    Estimation results for the weekend afternoon.

    • figshare.com
    xls
    Updated May 31, 2023
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    Kazufumi Tsuboi; Naoya Fujiwara; Ryo Itoh (2023). Estimation results for the weekend afternoon. [Dataset]. http://doi.org/10.1371/journal.pone.0276741.t003
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    xlsAvailable download formats
    Dataset updated
    May 31, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Kazufumi Tsuboi; Naoya Fujiwara; Ryo Itoh
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Estimation results for the weekend afternoon.

  14. Labor force in Tokyo Prefecture, Japan 2015-2024

    • statista.com
    Updated Dec 5, 2024
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    Statista Research Department (2024). Labor force in Tokyo Prefecture, Japan 2015-2024 [Dataset]. https://www.statista.com/topics/9914/tokyo/
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    Dataset updated
    Dec 5, 2024
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Statista Research Department
    Area covered
    Tokyo, Prefectures of Japan
    Description

    In 2024, the total labor force in Tokyo Prefecture in Japan was composed of around 8.7 million people. That year, men made about 54 percent of the workforce in the prefecture.

  15. Growth rate of real gross prefectural domestic product of Tokyo, Japan FY...

    • statista.com
    Updated Dec 5, 2024
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    Statista Research Department (2024). Growth rate of real gross prefectural domestic product of Tokyo, Japan FY 2013-2022 [Dataset]. https://www.statista.com/topics/9914/tokyo/
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    Dataset updated
    Dec 5, 2024
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Statista Research Department
    Area covered
    Tokyo
    Description

    In the fiscal year 2022, the real gross prefectural domestic product of Tokyo Prefecture rose by 3.9 percent compared to the previous fiscal year. This was the highest growth rate in the past decade.

  16. n

    Data from: Testing the drivers of the temperature-size covariance using...

    • data.niaid.nih.gov
    • datadryad.org
    • +1more
    zip
    Updated Dec 12, 2019
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    Martino E. Malerba; Dustin J. Marshall (2019). Testing the drivers of the temperature-size covariance using artificial selection [Dataset]. http://doi.org/10.5061/dryad.wstqjq2gp
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    zipAvailable download formats
    Dataset updated
    Dec 12, 2019
    Dataset provided by
    Monash University
    Authors
    Martino E. Malerba; Dustin J. Marshall
    License

    https://spdx.org/licenses/CC0-1.0.htmlhttps://spdx.org/licenses/CC0-1.0.html

    Description

    Body size often declines with increasing temperature. Although there is ample evidence for this effect to be adaptive, it remains unclear whether size shrinking at warmer temperatures is driven by specific properties of being smaller (e.g. surface to volume ratio) or by traits that are correlated with size (e.g. metabolism, growth). We used 290 generations (22 months) of artificial selection on a unicellular phytoplankton species to evolve a 13-fold difference in volume between small-selected and large-selected cells and tested their performance at 22°C (usual temperature), 18°C (-4), and 26°C (+4). Warmer temperatures increased fitness in small-selected individuals and reduced fitness in large-selected ones, indicating changes in size alone are sufficient to mediate temperature-dependent performance. Our results are incompatible with the often-cited geometric argument of warmer temperature intensifying resource limitation. Instead, we find evidence that is consistent with larger cells being more vulnerable to reactive oxygen species (ROS). By engineering cells of different sizes, our results suggest that smaller-celled species are pre-adapted for higher temperatures. We discuss the potential repercussions for global carbon cycles and the biological pump under climate warming.

    Methods Materials and Methods

      Study species and culturing conditions
    

    As a model species, we chose the green microalgal species Dunaliella tertiolecta (Butcher) because it is cosmopolitan, tolerates a wide range of climates (from tropical to sub-polar), has an intermediate body size compared to other phytoplankton species, and grows well in the laboratory (Guiry 2019). We sourced this species from the Australian National Algae Culture Collection (ANACC; strain code CS-14) and started cultures from multiple cells per independent lineage (i.e. not clonal), cultured in autoclaved F/2 medium (with no silica) from 0.45µm-filtered seawater (Guillard 1975). We kept the environment constant using a temperature-controlled room at 21±1 °C, under a photoperiod of 14-10 h day-night and a light intensity of 150 µM photos m-2 s-1, using low-heat 50 W LED flood lights (Power-liteTM, Nedlands Group, Bedfordale, Australia).

      Artificial selection for size
    

    For details on the artificial selection methods, see Malerba et al. (2018c). Briefly, the method relies on larger cells forming a pellet at the bottom of test tubes at lower centrifugal forces compared to smaller cells, which instead will remain in solution (i.e. differential centrifugation). On 25th April 2016, we inoculated 72 lineages with the same ancestral population of D. tertiolecta into aseptic 75 cm2 plastic cell culture flasks (Corning, Canted Neck, Nonpyrogenic). Since then, we selected lineages twice a week, each Monday and Thursday: 30 lineages were large-selected, 30 small-selected and 12 were the control. The selection differential for both larger and smaller cells was approx. 10% shift between cell volume before and after artificial selection. Control cultures experienced identical conditions (including centrifugation) without being size-selected. At the end of selection, all cultures were diluted approx. 3-5 times in fresh F/2 medium. Lineages were not axenic, but we kept bacterial loads to minimal levels by resuspending pelleted cells in autoclaved medium twice a week and by handling samples using sterile materials under a laminar-flow cabinet (Gelman Sciences Australia, CF23S, NATA certified).

    For this experiment, we used cells sampled from 12 randomly selected lineages for each of the three size-selection treatments after 290 generations (22 months) of artificial selection. To remove any environmental effects and non-genetic phenotypic differences from artificial selection, before starting trials all cells grew for three generations (a week) under common garden conditions with no centrifugation (neutral selection). Following neutral selection, we measured the mean cell volume for all 36 lineages, using optic light microscopy at 400x after staining cells with lugol’s iodine at 2%. We calculated cell volume from at least 200 cells per culture in Fiji v2.0 (Schindelin et al. 2012) assuming prolate spheroid shape, as recommended for this species by Sun and Liu (2003).

      Temperature trials
    

    After neutral selection at 21±1 °C, we diluted samples from the 36 lineages ten-fold, resuspended into fresh F/2 medium and standardized for initial total biovolume (i.e. population density × mean cell volume; unit µm3 µL-1) – which is a better predictor for resource use than population density. We submerged all cultures inside transparent and side-illuminated water baths at a controlled temperature of either 22°C (usual temperature), 18°C (-4), and 26°C (+4; see Fig. 2 for experimental design). This temperature range mirrors usual yearly fluctuations in sub-tropical regions where this species is often found. Each temperature included three independent water baths, each holding 12 lineages (four per size-selection treatment; see Fig. 2). We manipulated the temperature of each water bath using a submergible aquarium heater placed behind the samples so as not to affect light exposure. We ensured that daily temperature fluctuations were within ±1°C. The experiment took place in a temperature-controlled room and samples experienced identical light conditions of 14-10 h day-night photoperiod with a light intensity of ca. 100 µM photos m-2 s-1.

    We monitored all samples for mean cell volume, population density and total biovolume at day 0, 3 and 6. We recorded population density (i.e. cells µL-1) with a flow cytometer (FlowCore, BD LSRII; BD Biosciences, Franklin Lakes, NJ, USA) using a blue laser (488 nm) and CountBright absolute counting beads (Thermo Fisher, Waltham MA, USA) as internal standards in each sample. We inferred the mean cell volume (µm3) of a population using a calibration curve between the mean of the cytometric histogram for the forward scatter (after standardizing for the mean of the beads) and the mean cell volume measured using optical light microscopy (R2 = 0.84, F1,106 = 540.9, p < 0.001). Finally, we calculated the total biovolume (µm3 µL-1) by multiplying the population density by the mean cell volume. In total, the dataset included 972 observations (3 artificial selections x 3 temperatures x 3 baths x 4 replicates x 3 times x 3 demographic parameters).

      ROS assays
    

    We adopted the methods for quantifying intracellular Reactive Oxygen Species (ROS) from Dao and Beardall (2016). After three generations of neutral selection (i.e. no centrifugation), we standardized six lineages per size-selection treatment to the same total biovolume and washed three times all populations into saline 40mM TRIS-HCl buffer (pH 7 and 35 ppt). Then, we dark-incubated cells in 50µM of fluorescent probe 2’,7’ dichlorodihydrofluorescein diacetate (DCFH-DA) for 90 minutes at 37°C. After resuspending the pellet in buffer, we sonicated cells for 10 minutes. We measured the fluorescence of the supernatant in a spectrophotometer (Hitachi F-7000, Tokyo, Japan) at wavelengths of 485 nm (excitation) and 525 nm (emission). Values were converted into fluorescence units of dichloro-fluorescein (nM) using a 7-point calibration curve (2nd degree polynomial: R2 = 0.999). The buffer was the negative control and dichlorofluorescein (DCF) from 5 to 75 nM was the positive control. We standardized ROS fluorescence in a sample for cell density and for the volume of the cell’s nucleus. Nucleus size was estimated from fluorescent microscopy (excitation at 325-375nm and emission at 435-485nm) with Leica DMi8 at 400x after fixing cells with 2% glutaraldehyde and resuspending the biomass in DAPI at 0.1 µg mL-1. The allometric relationship was: log10 nucleus volume = 0.479× log10 cell volume-0.122.

      Data analysis
    

    We calculated three parameters that estimate the species short-term fitness from each time-series. The daily cell production (cells µL-1 day-1) of a lineage indicates rate of change in population densities between day 0 and 3. The daily biovolume production (µm3 µL-1 day-1) is the slope of total biovolume between day 0 and 3. The population carrying capacity (cells µL-1) was the final cell density of each lineage after the time-series reached a stable state (6 days). Finally, we analysed the effects of temperature on mean cell volumes (µm3) between day 0 and 3 among size-selection treatments.

    We used linear mixed models to estimate the effects of temperature at each artificial selection treatment on mean cell size and the three fitness parameters. In all models, fixed effects included Temperature (continuous, from 18 to 26) and Artificial Selection Treatment (discrete, either small-selected, large-selected or control). We ensured that treating Temperature as a discrete categorical factor did not change any of the conclusions (see Fig. S1). For mean cell size, we monitored cultures for 6 days: to focus on phenotypic plasticity and exclude evolutionary effects, we only analysed observations at the start of the experiment and after 3 days of growth, with Time (continuous, from 0 to 3) as an additional fixed factor in the model. Initial models included all interaction terms. If not significant, higher-order interaction terms were removed from the model. Final models also included a random intercept for each lineage nested within treatment. We initially included a random slope with temperature, but was later removed from the final models after being consistently selected against by model selection with Akaike Information Criterion (Burnham and Anderson 2002). We calculated probability values for the linear mixed-models using an analysis of deviance with type II Wald chi-square test and Kenward-Roger approximation to calculate the degrees of freedom (see Table S1).

    We

  17. Number of foreign residents in Tokyo Prefecture, Japan 2024, by region of...

    • statista.com
    Updated Dec 5, 2024
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    Statista Research Department (2024). Number of foreign residents in Tokyo Prefecture, Japan 2024, by region of origin [Dataset]. https://www.statista.com/topics/9914/tokyo/
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    Dataset updated
    Dec 5, 2024
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Statista Research Department
    Area covered
    Tokyo, Prefectures of Japan
    Description

    As of January 2024, more than 257,000 residents from China were registered in Tokyo Prefecture, accounting for the largest share of foreign nationals. The second-largest group of foreign nationals living in the prefecture were from South Korea.

  18. R

    Japan Cardiac Surgery Instruments Market Size | Growth Analysis 2036

    • researchnester.com
    Updated Jan 6, 2025
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    Research Nester (2025). Japan Cardiac Surgery Instruments Market Size | Growth Analysis 2036 [Dataset]. https://www.researchnester.com/reports/japan-cardiac-surgery-instruments-market/6927
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    Dataset updated
    Jan 6, 2025
    Dataset authored and provided by
    Research Nester
    License

    https://www.researchnester.comhttps://www.researchnester.com

    Description

    The cardiac surgery instruments market in Japan size was valued at USD 141.3 million in 2024 and is anticipated to surpass USD 357.1 million by the end of 2036, expanding at a CAGR of 8% during the forecast period, i.e., 2025-2036. Tokyo is the largest healthcare market in Japan due to the high population density that puts pressure on the healthcare delivery systems for cardiac surgery instruments.

  19. Average monthly gross income per working household in Tokyo, Japan 2015-2024...

    • statista.com
    Updated Dec 5, 2024
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    Statista Research Department (2024). Average monthly gross income per working household in Tokyo, Japan 2015-2024 [Dataset]. https://www.statista.com/topics/9914/tokyo/
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    Dataset updated
    Dec 5, 2024
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Statista Research Department
    Area covered
    Tokyo
    Description

    In 2024, working households in Tokyo Prefecture earned 772 thousand Japanese yen on average per month. The gross income of working households in the prefecture reached a decade high.

  20. Monthly consumption spending index of households in Tokyo, Japan 2023, by...

    • statista.com
    Updated Dec 5, 2024
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    Statista Research Department (2024). Monthly consumption spending index of households in Tokyo, Japan 2023, by category [Dataset]. https://www.statista.com/topics/9914/tokyo/
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    Dataset updated
    Dec 5, 2024
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Statista Research Department
    Area covered
    Tokyo
    Description

    In 2023, households in Tokyo Prefecture spent 2.04 times more on education compared to the average monthly household expenditures in Japan. The monthly housing expenses were 1.42 times more among households in the prefecture compared to the nation's average.

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Statista (2025). Population density in Tokyo Prefecture, Japan 2000-2020 [Dataset]. https://www.statista.com/statistics/673679/japan-population-density-toyko/
Organization logo

Population density in Tokyo Prefecture, Japan 2000-2020

Explore at:
Dataset updated
Jul 11, 2025
Dataset authored and provided by
Statistahttp://statista.com/
Area covered
Japan
Description

In 2020, the population of Tokyo Metropolis amounted to over ***** inhabitants per square kilometer. The number increased from approximately ***** inhabitants per square kilometer in 2000.

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