- Q1: How has the OSM building data in China developed in recent years?
- Q2: Which province- or prefecture-level division(s) has/have relatively more OSM building data?
- Q3: What is/are the potential factor(s) that affect the spatial distribution of OSM building data in China?
- Q4: Which grid cell(s) in urban areas has/have relatively higher OSM building density values?
- Q5: Is there any pattern for those high-density grid cells among different prefecture-level divisions?
2.1. Quality Indicators
2.1.1. OSM Building Count
2.1.2. OSM Building Density
2.2. Methods and Steps
2.2.1. Analysis Based on OSM Building Count
- Step 1: Intersect the OSM building dataset for each year with provincial- and prefecture-level administrative datasets, respectively.
- Step 2: Calculate the OSM building count in each provincial- or prefecture-level division.
- Step 3: Compare the OSM building counts among different provincial- or prefecture-level divisions across different years (2012–2017).
- Step 4: Calculate the correlations between the OSM building count and the four factors (GDP, population, urban land area, and OSM road length) in terms of provincial- and prefecture-level divisions for different years.
2.2.2. Analysis Based on OSM Building Density
- Step 1: Create a 1 × 1 km2 grid across each urban area in China.
- Step 2: Calculate the OSM building density and completeness values for each grid cell (here, the OSM building completeness denotes the ratio of the total area of OSM building data to that of corresponding reference building data in each grid cell).
- Step 3: Plot the relationship between OSM building density and completeness for all the grid cells in each urban area.
- Step 4: Calculate the percentage of grid cells whose OSM building density equals 0%, indicating that the corresponding OSM building completeness is also 0%, or there are no buildings in such grid cells. Calculate the percentage of grid cells whose OSM building density is larger than a certain threshold (this threshold can be determined as the inverse of the slope of the relationship obtained in Step 3), to find out which grid cells have relatively higher density values or tend to be complete.
- Step 5: Compare the percentages of grid cells in urban areas with different OSM building density ranges across different years.
- OSM datasets: Buildings and roads in the OSM datasets of China for 6 years (2012–2017) were obtained from http://download.geofabrik.de/index.html. Each OSM dataset was obtained for the last month (December) of each year.
- Administrative datasets: Provincial- and prefecture-level administrative datasets were downloaded from http://www.gadm.org. A total of 34 provincial-level divisions and 334 prefecture-level divisions in China were used in the analysis.
- Land-use/cover datasets: Globe land-cover/use datasets at 30 m resolution were downloaded (http://globallandcover.com) and “artificial surface” was viewed as the urban areas.
- Socio-economic data: Three types of socio-economic data (population, GDP, and urban land area in terms of provincial- and prefecture-level divisions) across different years were acquired from the National Bureau of Statistics in China (http://www.stats.gov.cn).
4. Results and Discussions
4.1. Analyses Based on OSM Building Count
- For most provincial-level divisions, the number of OSM building data was less than 5000 in 2012. However, for 7 out of 34 provincial-level divisions, the number was 30,000 and higher by 2017. These provinces (Shandong, Jiangsu, Zhejiang, Guangdong, Beijing, Shanghai, and Tianjin) were all located on the eastern coast of China. The heterogeneous distribution of OSM building data in China is evident. For example, by the end of 2017, the number of OSM building data was 175,215 in Jiangsu, and only 1463 in Guizhou.
- The number of OSM building data was less than 2500 in 2012 for 329 out of the 334 prefecture-level divisions in China. However, the number for 21 prefecture-level divisions, mostly located on the eastern coast of China, was greater than 10,000 in 2017. Although the number of OSM building data in Beijing exceeded 40,000, those for 83% of prefecture-level divisions were still less than 2500.
4.2. Analyses Based on OSM Building Density
- The OSM building data in China increased by almost 20 times from 2012 to 2017, especially for those regions located on the eastern coast of China. In most cases, the GDP and OSM road length factors had a moderate correlation with OSM building count.
- Most grid cells in urban areas still have no buildings or their building density is equal to 0%, which indicates that the OSM building dataset in China is far from being complete. From analyzing the high-density grid cells, two typical spatial distribution modes (dispersion and aggregation) were found in multiple prefecture-level divisions.
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|Administrative Division||Year||GDP||Population||Urban Land Area||OSM Road Length|
|2014||0.579 **||0.256||0.315||0.449 *|
|2015||0.720 **||0.420 *||0.454 *||0.519 **|
|2016||0.689 **||0.387 *||0.380 *||0.529 **|
|Prefecture-level||2012||0.639 **||0.338 **||0.294 **||0.671 **|
|2013||0.622 **||0.285 **||0.301 **||0.643 **|
|2014||0.622 **||0.279 **||0.295 **||0.603 **|
|2015||0.627 **||0.258 **||0.279 **||0.585 **|
|2016||0.625 **||0.247 **||0.263 **||0.602 **|
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