- The intrinsic quality assessment of an OSM-based LULC dataset is carried out. By contrast, most past studies have used an LULC reference dataset for quality assessment. Our analytical method can be applied to other regions, especially those for which a free LULC reference dataset is unavailable.
- Both the completeness and the diversity patterns of an entire country (China) were mapped and analyzed, and the results indicate that the diversity measure may be used as a supplement for an intrinsic quality assessment.
2. Study Area and Data
2.1. Study Area
3.1. Production and Validation of OSM-Based LULC Dataset
- Step 1: Convert line features into polygon features. According to Zhou et al. , it is feasible to convert a line feature into a polygon feature through buffering, i.e., to create a buffer region around the line feature, after which, the buffer region can be viewed as a polygon feature. The challenge here is to determine appropriate buffer radii for different OSM types because OSM line objects may be tagged with different attribute values (e.g., highways = primary, highways = secondary, and highways = residential). Such an appropriate radius was determined by Zhou et al.  through comparison with a corresponding reference LULC dataset (GMESUA). However, such a reference dataset was not available for our study area, and thus different buffer radii (ranging from 4.5–10 m) for different OSM types were manually determined by referring to the Technical Standard of Highway Engineering of China and the corresponding images on Google Earth (Table 1). The buffer radius was generally positively correlated with the importance of an OSM road type.
- Step 2: Classify OSM objects into corresponding reference classes. Owing to a lack of the LULC reference product, we manually classified all OSM objects (according to their tags) into 12 LULC classes: Agriculture, orchard, forest, grass, commercial, industrial, residential, public use, special use, transportation, water, and other lands (Table 2). All these LULC classes were obtained from the first level of the National Land Use Classification Standards of China.
- Step 3: Merge multiple LULC classes (or layers) into a single layer. This is a necessary step because some polygon objects in OSM may overlap but correspond to different LULC classes; it may therefore be difficult to determine a unique LULC class for the same geographical region. The solution is to make different (12) LULC classes overlap according to their average area, from small to large . To be specific, the feature or class with the smallest average area was placed on the top and that with the largest average area was placed at the bottom. After this process, all LULC classes (or layers) were further merged into a single layer.
3.2. Mapping and Analysis of Completeness and Diversity Patterns
3.2.1. Completeness and Diversity Measures
3.2.2. Mapping and Analysis
- Both the completeness and the diversity patterns were visually analyzed. A number of questions were considered. For instance, which areas had relatively high or low completeness and diversity values? Was there any difference between the completeness and the diversity patterns, in terms of scenarios I and II? was there any correlation between the completeness and the diversity patterns of the same scenario?
- For quantitative assessment, a number of factors were employed to identify factors that could have influenced the completeness and diversity patterns. Three socioeconomic factors [the size of the built-up areas, their population, and gross domestic product (GDP)] were first considered. The corresponding data in 2019 were acquired from the National Bureau of Statistics of China (http://www.stats.gov.cn). These factors were chosen because studies have shown that the completeness of OSM data tends to be high in municipalities with high population density . Completeness has also been positively correlated with the GDP . Thus, it is useful to investigate whether these factors could still be positively correlated with the completeness and diversity patterns of the OSM-based LULC dataset of China. Moreover, the number of contributors (who had edited the OSM data) was calculated based on an analysis of OSM history data (https://planet.openstreetmap.org/planet/full-history/, accessed in January 2019). This number was calculated in terms of each prefecture-level division (for scenario I) and the built-up areas of each prefecture-level division (for scenario II) to determine whether the number of contributors had a positive correlation with the completeness and/or diversity patterns.
4. Results and Analysis
4.1. Production and Validation of the OSM-Based LULC Dataset
4.2. Mapping and Analysis of Completeness and Diversity Patterns
5. Combined Completeness and Diversity Patterns
- Group I (High completeness and high diversity): The completeness was higher than a certain threshold (), as was the diversity ().
- Group II (High completeness and low diversity): The completeness was higher than , but the diversity was lower than .
- Group III (Low completeness and high diversity): The completeness was lower than , but the diversity was higher than ;
- Group IV (Low completeness and low diversity): The completeness was lower than , and the diversity was lower than .
- Group I: For both scenarios I and II, most prefecture-level divisions were municipalities, e.g., Beijing (Figure 6a), Shanghai, and Tianjin, capital cities, e.g., Guangzhou, Nanjing, Chengdu, and Changsha, and the relatively developed cities (Shenzhen, Qingdao and Xiamen), and regions on the east coast. These divisions probably received more attention from the volunteers, and thus both their completeness and diversity values were relatively high.
- Group II: The prefecture-level divisions of this group varied across scenario. In scenario I, most divisions were located in the east, center, and northeast of China owing to a large area percentage of forest. In scenario II, they were located in the southwest, northwest, and northeast of China owing to a large area percentage of residential land. This was nearly consistent with what is shown in Figure 2a,c.
- Groups III and IV: The prefecture-level divisions of these groups had low completeness values for several reasons: Some divisions (e.g., Haixi and Naqu) were characterized by a large land area, and thus the volunteers would have needed more time and effort to map these divisions well. In addition, some divisions (e.g., Leshan (Figure 6b) and Songyuan (Figure 6c)) were less well known, especially compared with those in Group I, and thus would probably have received less attention by volunteers. Furthermore, most divisions showed a relatively high diversity value, which indicates that in most cases was no dominant LULC class. However, some divisions (e.g., Figure 6c) featured a relatively large area percentage of water (79.3% for the left graph in Figure 6c) or residential lands (85.6% for the right graph in Figure 6c), which resulted in a low diversity value.
6.1. Quality Measures
- The OA of the OSM-based LULC dataset of China was as high as 82.2%, which illustrates that the generated LULC dataset for the country was effective, and is comparable to those for European study areas in past work.
- Both the completeness and the diversity patterns varied with prefecture-level division. Moreover, the completeness patterns were significantly different from the corresponding diversity patterns. In particular at the scale of built-up areas, divisions with high completeness values might not have been mapped well owing to a low diversity value.
- The correlations between diversity patterns and each of the three socioeconomic factors, and the number of contributors were not only higher than those considering for completeness patterns, but also significantly positive. Thus, the diversity pattern is a better reflection of socioeconomic factors and the spatial pattern of contributors.
- Both the completeness and the diversity patterns can be combined into different groups (high completeness and high diversity, high completeness and low diversity, low completeness and high diversity, and low completeness and low diversity). The combined patterns benefit both OSM users and volunteers in that they provide a better understanding of OSM-based LULC datasets.
Conflicts of Interest
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|OSM Road Type||Buffer Radius (m)|
|No.||Typical OSM Tags||LULC Reference Classes|
|5||ATM; bank; bar; cafe; cinema; commercial; computer shop; fast food; furniture shop; giftshop; hostel; mall; nightclub; restaurant; retail; supermarket; tower||commercial|
|8||artwork; college; hospital; library; museum; police; post office; school; sports center; stadium; toilet; university; zoo||public use|
|9||attraction; Christian; memorial; military; monument; Muslim; prison; shelter||special use|
|10||airport; bus station; parking; primary; rail; secondary; tertiary; trunk; unclassified||transportation|
|11||beach; dam; dock; reservoir; river; water; waterfall; wetland||water|
|Agricultural||Orchard||Forest||Grass||Commercial||Industrial||Residential||Public Use||Special Use||Transportation||Water||Others||PA (%)|
|UA (%)||86.7||81.0||91.5||35.0||76.2||83.0||82.3||81.2||47.8||90.3||91.0||60.2||OA (%) = 82.2|
|Pattern||Size of Built-Up Areas|
|Gross Domestic Product|
|Number of Contributors, Scenario I|
|Number of Contributors, Scenario II|
(completeness; scenario I)
|0.115 *||−0.006||0.157 **||0.116 *||/|
(diversity; scenario I)
|0.285 **||0.282 **||0.248 **||0.249 **||/|
(completeness; scenario II)
|−0.262 **||−0.245 **||−0.029||/||−0.113 *|
|0.367 **||0.375 **||0.412 **||/||0.429 **|
|Number of contributors, |
|0.681 **||0.659 **||0.855 **||1.000 **||1.000 **|
|Number of contributors, |
|0.722 **||0.645 **||0.799 **||1.000 **||1.000 **|
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