Journal of Geo-information Science >
Land Use Classification Based on Massive Human-Activity Spatio-temporal Data
Received date: 2015-10-10
Request revised date: 2015-10-28
Online published: 2015-12-20
Copyright
Macroscopically monitoring the status of urbanization and fast acquiring the land covers or land use in urban areas is essential for urban planning, management and scientific policy-making. The rapidly developing remote sensing technologies have been recognized as an essential approach to carry out this work because of their vital ability to capture the physical features of different land use, such as the spectral and textural properties. However, these technologies could not reveal the heterogeneity of urban development and differentiate the vitality in and among cities with the similar physical properties interpreted from remote sensing images. Human-activity based sensing technologies nowadays have been recognized as a promising alternative to resolve these problems. Spatio-temporal distribution of human activities could be derived from mobile phone records and smart card records stored in the public transportation systems, social media or social networking services (SNSs), and etc. They are good indicators for the social function of land use and urban vitality. We proposed types of indices to bridge the relationships between the intensities of human activities and land covers. Similar to the spectral bands of remote sensing images, more than thirty social bands were generated in this paper to describe the social characteristics of ground objects by aggregating and gridding human activities into pixels. According to the spectral profiles of eight land covers, a supervised classification approach was then applied to infer the land covers of the research area. Validation experiments were conducted in Shenzhen, China using a large-scale of people’s historical login information on Tencent QQ, which is the most popular SNS, during 2013. Results showed that the land cover of Shenzhen could be determined with a detection rate of 72% according to an urban planning map of Shenzhen. Compared to the classification results from remote sensing images, the human-activity based sensing technologies can obtain more detailed insight into the urban form, city skeleton, and the heterogeneity of development and vitality in different urban areas.
Key words: huamn activity; digital footprints; social network data; land use; land cover; classification
LU Guozhen , CHANG Xiaomeng , LI Qingquan , ZHAO Qingliang . Land Use Classification Based on Massive Human-Activity Spatio-temporal Data[J]. Journal of Geo-information Science, 2015 , 17(12) : 1497 -1505 . DOI: 10.3724/SP.J.1047.2015.01497
Fig. 1 Framework of urban land user classification using human-activity tracking data图1 基于人类时空活动提取土地利用类型的流程图 |
Tab. 1 The description of human activity bands表1 人类活动指数描述 |
指数 | 波段 | 波段全称 | 公式 | 统计方式 | 指数含义 |
---|---|---|---|---|---|
NV | NWV | Number of Whole Visitors | 反映格网被访问的人数,用以提取诸如博物馆、公园、旅游景点等访问人数很高的区域 | ||
NLV | Number of Local Visitors | ||||
NOV | Number of Outside Visitors | ||||
TIV | TDW | Total Visit Days of Whole Visitors | 用户访问总天数 | 反映格网被访问的人次,用于提取诸如工作地、住宅地等被频繁访问的区域 | |
TDL | Total Visit Days of Local Visitors | ||||
TDO | Total Visit Days of Outside Visitors | ||||
TTW | Total Visit Times of Whole Visitors | 用户访问总次数 | |||
TTL | Total Visit Times of Local Visitors | ||||
TTO | Total Visit Times of Outside Visitors | ||||
AIV | ADW | Average Visit Days of Whole Visitors | 用户访问总天数 | 访问人次与访问人数的比值 | |
ADL | Average Visit Days of Local Visitors | ||||
ADO | Average Visit Days of Outside Visitors | ||||
ATW | Average Visit Times of Whole Visitors | 用户访问总次数 | |||
ATL | Average Visit Times of Local Visitors | ||||
ATO | Average Visit Times of Outside Visitors | ||||
ADWPLR | Averge Visit Days of Whole Visitors by Per-capita Local Resident | 用户访问总天数 | 访问人次与本地用户密度的比值 | ||
ADLPLR | Averge Visit Days of Local Visitors by Per-capita Local Resident | ||||
ADOPLR | Averge Visit Days of Outside Visitors by Per-capita Local Resident | ||||
ATWPLR | Average Visit Times of Whole Visitors by Per-capita Local Resident | 用户访问总次数 | |||
ATLPLR | Average Visit Times of Local Visitors by Per-capita Local Resident | ||||
ATOPLR | Average Visit Times of Outside Visitors by Per-capita Local Resident |
Fig. 2 Example of three bands and their false color composite image derived from human-activity sensing images图2 “类高光谱影像”的示例波段及合成的假彩色影像 |
Fig. 3 Spectral profiles derived from human sensing images of nine major land cover types in Shenzhen area图3 深圳地区的“类高光谱影像”的波谱响应曲线 |
Fig. 4 Result of land use classification in Shenzhen图4 深圳市的土地利用分类结果图 |
Fig. 5 Producer’s and user’s accuracies of the classification图5 分类结果的用户精度、生产者精度 |
Tab. 2 Confusion matrix of the classification表2 分类结果的混淆矩阵 |
建筑群 | 道路 | 市内绿地 | 其他 | |
---|---|---|---|---|
建筑群 | 0.6807 | 0.2037 | 0.0903 | 0 |
道路 | 0.1171 | 0.5955 | 0.0602 | 0 |
市内绿地 | 0.1431 | 0.1913 | 0.7398 | 0.0024 |
其他 | 0.0591 | 0.0095 | 0.1097 | 0.9976 |
The authors have declared that no competing interests exist.
[1] |
|
[2] |
|
[3] |
|
[4] |
|
[5] |
|
[6] |
|
[7] |
|
[8] |
|
[9] |
|
[10] |
从鬼城看中国城市化前景.
|
[11] |
|
[12] |
|
[13] |
|
[14] |
|
[15] |
|
[16] |
|
[17] |
|
[18] |
|
[19] |
|
[20] |
|
[21] |
|
[22] |
|
[23] |
|
[24] |
|
[25] |
|
[26] |
|
[27] |
|
[28] |
|
[29] |
|
[30] |
|
[31] |
微信光芒万丈,拥有8亿月活跃用户的QQ到底在想什么?.
|
[32] |
2014年深圳市常住人口中户籍与非户籍人口数.
|
[33] |
腾讯QQ同时在线人数突破2亿.
|
[34] |
|
[35] |
|
[36] |
|
/
〈 |
|
〉 |