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Visualizing commuter flows in the Charlottesville area fertility rates have changed over time and how they might look in the future. This blog post uses animated graphs to show how U.S.A. This post features an interactive data visualization that shows population pyramids for the Commonwealth of Virginia and each of its localities and regions. Visualizing the age structure of Virginia’s local and regional populations View this post for a few examples of how you can use the tool to visualize some of the key demographic trends reflected in the national projections. Visualizing the nation’s future population The interactive map displays the loss or gain in seats from the previous census, along with the projected change following the upcoming 2020 count. Power and money: Why the census is important Also see some nice work by Alasdair Rae who has produced some excellent 3D visualisations using GHSL.Web Series Data visualizations created by us (but often inspired by other people’s work) Change over time animations would definitely be an interesting approach to explore in the future.
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So far I have only visualised 2015, but have calculated statistics for all the years (turn the interactive statistics on at the top left of the website- I’ll post more about these statistics later). The comprehensive nature of the GHSL data means it can be analysed and applied in many ways, including as a time series as data is available for 1975, 1990, 20. This reflects rich agriculture and prospering cities, but like many urban regions is vulnerable to sea level changes. There is a massive concentration of population along the coast in South India. Huge rural populations surround the delta lands of West Bengal and Bangladesh, focused around the megacities of Kolkata and Dhaka. The intense settlement of Cairo and the Nile Delta is in complete contrast to the arid and empty Sahara. There are around 147 million people living on Java. The term desakota was originally coined by McGee in relation to Java in Indonesia, which has an incredible density of settlement as shown above. This emerging megaregion, including Tianjin, is sometimes termed Jingjinji. The form of Beijing’s wider region is quite different, with a huge lower density corridor to the South West of mixed industry and agriculture which looks like the Chinese version of desakota (“village-city”) forms. Population estimates range from 50-70 million depending on where you draw the boundary. The Yangtze Delta is also home to another gigantic polycentric megaregion, with Shanghai as the focus.
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The megaregions of China are spectacularly highlighted, above the Pearl River Delta including Guangzhou, Shenzhen and Hong Kong amongst many other large cities, giving a total population of around 50 million. Above is the northeastern seaboard of the USA, with urban settlements stretching from Washington to Boston, famously discussed by Gottman in the 1960s as a meglopolis.Įurope’s version of a megaregion is looser, but you can clearly see the corridor of higher population density stretching through the industrial heartland of the low countries and Rhine-Ruhr towards Switzerland and northern Italy, sometimes called the ‘blue banana’. The GHSL is great for exploring megaregions. A few highlights are included here and I will post in more detail later when I have explored the dataset more fully. There are clearly many applications of this data in understanding urban geographies at different scales, urban development, sustainability and change over time. The World Population Density map is exploratory, as the dataset is very rich and new, and I am also testing out new methods for navigating statistics at both national and city scales on this site. As usual, my first thought was to make an interactive map, now online at. This is the first time that detailed and comprehensive population density and built-up area for the world has been available as open data. A brilliant new dataset produced by the European Commission JRC and CIESIN Columbia University was recently released- the Global Human Settlement Layer (GHSL).