This web map shows the Mid Year Population Density within the 18 districts of Hong Kong. It is a subset of data made available by the Census and Statistics Department under the Government of Hong Kong Special Administrative Region (the “Government”) at https://DATA.GOV.HK/ (“DATA.GOV.HK”). The source data is in CSV format and has been processed and converted into Esri File Geodatabase format and then uploaded to Esri’s ArcGIS Online platform for sharing and reference purpose. The objectives are to facilitate our Hong Kong ArcGIS Online users to use the data in a spatial ready format and save their data conversion effort.For details about the data, source format and terms of conditions of usage, please refer to the website of DATA.GOV.HK at https://data.gov.hk.

This layer shows the Hong Kong population density in 2021 Population Census. It is a subset of the census data 2021 made available by the Census and Statistics Department under the Government of Hong Kong Special Administrative Region (the “Government”) at https://DATA.GOV.HK/ (“DATA.GOV.HK”). The source data is in XLSX format and has been processed and converted into Esri File Geodatabase format and then uploaded to Esri’s ArcGIS Online platform for sharing and reference purpose. The objectives are to facilitate our Hong Kong ArcGIS Online users to use the data in a spatial ready format and save their data conversion effort.For details about the data, source format and terms of conditions of usage, please refer to the website of DATA.GOV.HK at https://data.gov.hk.

This map is part of an interactive Story Map series about global change in the US.With the global human population expected to exceed 8 billion people by 2030, our species is already irreversibly changing the future of our planet. The US itself is expected to grow by 16.5% to over 360 million people, making it the third largest country in the world, behind India and China. This population increase isn’t distributed evenly - 81% of people will live in cities, urban, and suburban areas, which will continue to shape how resources are produced, transported, and consumed. The percent of foreign-born and second-generation immigrants in the US is also expected to rise in the future, contributing to an increasingly diverse population. Across the globe, immigration will likely account for significant population changes in the near future, as climate change fuels drought, crop failures, and political instability, creating climate refugees particularly among countries who do not have the infrastructure to mitigate or adapt to global change. Numbers aren’t the only thing that matter: people of different socioeconomic backgrounds use resources differently, both within and between countries.If the rest of the world used energy as intensely as the United States does, the world population would need more than 4 entire Earths to provide us with the resources to feed this rate consumption. This unfortunately means that even regions of the US that contribute less towards the problems of global change will still feel their impacts. To ensure a high quality of life for all citizens, we must address not only population growth, but also excess consumption of and reliance on resources across different regions. Geographic, population, and economic differences among regions can provide opportunities for success in the face of global change. Renewable energy sources have created entrepreneurial economic ventures, and communities have found environmental solutions through forming sustainable local food systems. Environmental justice movements are working now to ensure that all citizens have access to nature, recreational areas, and a healthy future for all.

Infectious disease transmission is an inherently spatial process in which a host’s home location and their social mixing patterns are important, with the mixing of infectious individuals often different to that of susceptible individuals. Although incidence data for humans have traditionally been aggregated into low-resolution data sets, modern representative surveillance systems such as electronic hospital records generate high volume case data with precise home locations. Here, we use a gridded spatial transmission model of arbitrary resolution to investigate the theoretical relationship between population density, differential population movement and local variability in incidence. We show analytically that a uniform local attack rate is typically only possible for individual pixels in the grid if susceptible and infectious individuals move in the same way. Using a population in Guangdong, China, for which a robust quantitative description of movement is available (a travel kernel), and a natural history consistent with pandemic influenza; we show that local cumulative incidence is positively correlated with population density when susceptible individuals are more connected in space than infectious individuals. Conversely, under the less intuitively likely scenario, when infectious individuals are more connected, local cumulative incidence is negatively correlated with population density. The strength and direction of correlation changes sign for other kernel parameter values. We show that simulation models in which it is assumed implicitly that only infectious individuals move are assuming a slightly unusual specific correlation between population density and attack rate. However, we also show that this potential structural bias can be corrected by using the appropriate non-isotropic kernel that maps infectious-only code onto the isotropic dual-mobility kernel. These results describe a precise relationship between the spatio-social mixing of infectious and susceptible individuals and local variability in attack rates. More generally, these results suggest a genuine risk that mechanistic models of high-resolution attack rate data may reach spurious conclusions if the precise implications of spatial force-of-infection assumptions are not first fully characterized, prior to models being fit to data.

In the middle of 2023, about 60 percent of the global population was living in Asia.The total world population amounted to 8.1 billion people on the planet. In other words 4.7 billion people were living in Asia as of 2023. Global populationDue to medical advances, better living conditions and the increase of agricultural productivity, the world population increased rapidly over the past century, and is expected to continue to grow. After reaching eight billion in 2023, the global population is estimated to pass 10 billion by 2060. Africa expected to drive population increase Most of the future population increase is expected to happen in Africa. The countries with the highest population growth rate in 2024 were mostly African countries. While around 1.47 billion people live on the continent as of 2024, this is forecast to grow to 3.9 billion by 2100. This is underlined by the fact that most of the countries wit the highest population growth rate are found in Africa. The growing population, in combination with climate change, puts increasing pressure on the world's resources.

This paper examines the spatial distribution pattern and influencing factors of Martial Arts Schools (MASs) based on Baidu map data and Geographic Information System (GIS) in China. Using python to obtain the latitude and longitude data of the MASs through Baidu Map API, and with the help of ArcGIS (10.7) to coordinate information presented on the map of China. By harnessing the geographic latitude and longitude data for 492 MASs across 31 Provinces in China mainland as of May 2024, this study employs a suite of analytical tools including nearest neighbor analysis, kernel density estimation, the disequilibrium index, spatial autocorrelation, and geographically weighted regression analysis within the ArcGIS environment, to graphically delineate the spatial distribution nuances of MASs. The investigation draws upon variables such as martial arts boxings, Wushu hometowns, intangible cultural heritage boxings of Wushu, population education level, Per capita disposable income, and population density to elucidate the spatial distribution idiosyncrasies of MASs. (1) The spatial analytical endeavor unveiled a Moran’s I value of 0.172, accompanied by a Z-score of 1.75 and a P-value of 0.079, signifying an uneven and clustered distribution pattern predominantly concentrated in provinces such as Shandong, Henan, Hebei, Hunan, and Sichuan. (2) The delineation of MASs exhibited a prominent high-density core centered around Shandong, flanked by secondary high-density clusters with Hunan and Sichuan at their heart. (3) Amongst the array of variables dissected to explain the spatial distribution traits, the explicative potency of ‘martial arts boxings’, ‘Wushu hometowns’, ‘intangible cultural heritage boxings of Wushu’, ‘population education level’, ‘Per capita disposable income’, and ‘population density’ exhibited a descending trajectory, whilst ‘educational level of the populace’ inversely correlated with the geographical dispersion of MASs. (4) The entrenched regional cultural ethos significantly impacts the spatial layout of martial arts institutions, endowing them with distinct regional characteristics.

When we were first informed about doing a project with ArcGIS, I immediately thought of a project about the Chinese Mystery Snail. I had become mildly interested in these snails in the summer of 2019 after an invasives program, and I thought that it would be a great topic to research and collect data on. I knew that they were an invasive in Squam Lake, and I had seen them in the Squam River, so I was interested in finding out how common they were throughout the Squam area. Procedure:The first step in my procedure was doing some research on the Chinese Mystery Snail in New Hampshire. I wanted to find out the basic details about this snail as well as where it has been found. To my surprise, there didn't seem to be too much research on the extent of the spread. Since I had seen the snails in the Squam River, I initially thought to do my data collection in Little Squam Lake. However, as I checked the shores of public access points around the lake, I could not find any evidence of these snails. This caused me to move my study to the Squam River where I knew I would find snails. I then decided what sort of information I wanted to collect along with tracking the number and location of snails. I knew that I wanted to collect the depth where they were found, and I also decided to track the type of substrate they were found in. I also thought that I might be able to collect water samples from each location to test what pH, temperature, and other qualities of the water the snails may prefer, but I did not have the materials available to do so. I created a survey that included the depth, substrate, if they were alive or dead, the location, and a place to take photos of my findings. As I started to record data, I realized that some of the features on my survey were not best for the data I was trying to collect. As a result, I had to type the number of snails I found in the comments section rather than in a specific spot. I also did not have enough variation in my type of substrate, so I had to type in the different substrates I found in the "other" category. I only had time to primarily record data for one side of the river, so there are probably other points along the river on the other side where the snails are located. Also, the weather and light conditions were not the best on the day where I collected data, so there could also be spots where I missed snails due to my lack of visibility in the area. I recorded the data by using the Survey123 app on my phone, imputing my data there along with taking the pictures with each point. When I was out recording data, I used a kayak to get around, going slowly along the banks of the river to see if I could see any snails. When I found a snail, I would stop my kayak, take a picture of the snail(s) that I found, and then have my mom, who was kayaking with me, measure the depth of the snails while I inputted the other data into the survey. After I collected all of my data, I uploaded it into a map on ArcGIS Online, creating all of the points where I had found snails. Since I was kayaking and therefore did not have any wifi or service, there were some points that I had to manually shift a small amount because the GPS had been a few feet off and placed the points on land instead of in the water. I then searched for layers that showed the outline of the river and included that in my map. I also wanted to show the bathymetry of the Squam River, but there were no layers where this was recorded in the river, only in the lakes. After adjusting my points and starting to make the map, I realized that there were too many categories for just one map. This caused me to create three maps in order to show the data I wanted to include in my project. I chose to show the population density, the depth, and the type of substrate the snails were found in. For the maps showing the depth and the type of substrate, I also included the population density in order to show which depths and substrates the snails tended to prefer. I then created my Story Map, putting all of the maps in, along with quickly creating a few extra maps to help visualize the information. I included all of the research I had previously done, and did a little more research in areas where I felt I needed a bit more information. I included photos from my day collecting data into the Story Map and formatted it in order to make the most sense with clear transitions between subjects.

Data for: Decision-making Analysis for Green Roof Layout and Functional Types with Optimal Expected Ecosystem Services Benefits in High-Density Cities: A Case Study in the Central City of Guangzhou, China. We use multi-source spatial data from various websites to evaluate ecosystem services demand in the main city of Guangzhou, China. The data includes: (1)Carbon dioxide (CO₂) emissions. Source: https://edgar.jrc.ec.europa.eu/. Accessed July 25, 2024. (2)Precipitation. Source: http://www.geodata.cn. Accessed July 19, 2024. (3)Air Quality Index (AQI). Source: http://www.cnemc.cn/. Accessed July 28, 2024. (4)Landsat 8 and 9 satellite imagery (30m resolution). Source: https://www.usgs.gov/. Accessed July 24, 2024. (5)Digital Elevation Model (DEM). Source: https://www.gscloud.cn/. Accessed July 20, 2024. (6)Land cover (30m resolution). Source: http://globeland30.org/home.html. Accessed July 19, 2024. (7)Road vector data. Source: https://ditu.amap.com/. Accessed July 12, 2024. (8)Building vector data. Source: https://lbsyun.baidu.com/products/map. Accessed January 8, 2024. (9)Population Density (100m resolution). Source: https://www.worldpop.org/. Accessed July 10, 2024. (10)POl vector data. Source: https://ditu.amap.com/. Accessed July 12, 2024.

The urban spatial structure in this study refers to the combination of different categories of land use, and the purpose of the study is to reveal the intrinsic correlation characteristics between urban land use structural combination forms and urban functions. Through the integration of land and population maps and other multi-source data, with the help of exploratory spatial data analysis and other models, this research deals with the land use spatial structure characteristics of Changchun city and its coordination relationship with urban functions. Main conclusions of the study are as follows. The overall density of the land use in the central urban area of Changchun shows patterns of the core being higher than the periphery, the large-scale agglomeration being significant and the small-scale relatively scattered, and the pattern of the mixed land use function index has obvious differentiation characteristics. The study shows that, in the context of the spatial pattern, the overall coupling coordination degree of the land use structure index and the urban function index shows a trend of a gradual decrease, from the core to the periphery. In the context of category differences, the coupling coordination of the land use structure with the population distribution and the Baidu thermal distribution is relatively high, and the coupling coordination with various service facilities is relatively low. Finally, in the context of scale differences, all types of coupling coordination degrees have significant sensitivity to the spatial scales. A large scale significantly reflects the overall decrease in the coupling coordination degrees from the core to the periphery, while a small scale shows the polycentric pattern characteristics of the urban spatial structure.

Urban land structure, urban function measurement index selection, and data sources.

The earliest point where scientists can make reasonable estimates for the population of global regions is around 10,000 years before the Common Era (or 12,000 years ago). Estimates suggest that Asia has consistently been the most populated continent, and the least populated continent has generally been Oceania (although it was more heavily populated than areas such as North America in very early years). Population growth was very slow, but an increase can be observed between most of the given time periods. There were, however, dips in population due to pandemics, the most notable of these being the impact of plague in Eurasia in the 14th century, and the impact of European contact with the indigenous populations of the Americas after 1492, where it took almost four centuries for the population of Latin America to return to its pre-1500 level. The world's population first reached one billion people in 1803, which also coincided with a spike in population growth, due to the onset of the demographic transition. This wave of growth first spread across the most industrially developed countries in the 19th century, and the correlation between demographic development and industrial or economic maturity continued until today, with Africa being the final major region to begin its transition in the late-1900s.

Distribution of markers and marker density across chromosomes in the common wheat map developed in Yanda1817 × Beinong6 RILs population.