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Analysis of Urban Environmental Problems Based on Big Data from The Ecological Indicators 94 (2018) 52–69 Contents lists available at ScienceDirect Ecological Indicators jo urnal homepage: www.elsevier.com/locate/ecolind Analysis of urban environmental problems based on big data from the urban municipal supervision and management information system a a,b a,b a,b a,b c Rencai Dong , Siyuan Li , Yonglin Zhang , Nana Zhang , Tao Wang , Xinrui Tan , a,∗ Xiao Fu a State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China b College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China c Department of Mathematics, City University of Hong Kong, Hong Kong, China a r t i c l e i n f o a b s t r a c t Article history: In China, urban municipal supervision and management information system (UMSMIS) is a new platform Received 15 November 2015 to implement all-time and all-round urban environmental management. The accumulated data in the Received in revised form operation of UMSMIS contain varieties of knowledge about the urban environment and human life. With 13 September 2016 the development of electronic navigation map, points of interest (POIs) are treated as an important data Accepted 14 September 2016 resource for the urban study. POIs contain not only location information but also social-economic infor- Available online 17 October 2016 mation. They may be associated with the generation of urban environmental problems. To identify the spatial pattern of environmental problems and further explore the relationships between environmental Keywords: problems and POIs, this study analyzed the spatial pattern and composition of points of environmen- Urban environmental management tal problems (POEPs) at three levels, including the global level, local level and road level, in Dongcheng Points of environmental problems Points of interest District, Beijing, China. Then the study explored the relationships between POEPs and POIs at the three Big data levels. The results showed that the spatial distribution of POEPs was statistically significant clustered Urban grid management (p < 0.01) in Dongcheng District, Beijing. The major types of POEPs differed at the three levels and were Spatial statistics consistent with the components of POIs only in some regions. At the road level, this study found that POEPs occurred more along the minor roads and the crossroads had the higher density of POEPs and POIs. Thus the minor roads and crossroads should be paid more attention for supervision. Although there was a significantly positive correlation between the density of POEPs and POIs at the global level, the relationships between POEPs and POIs remained complex at different regions. This research may pro- vide methodologies and technical supports to identify spatial clusters of environmental problems, and further provide suggestions to optimize the allocation of urban management resources and improve the management efficiency. © 2016 Elsevier Ltd. All rights reserved. 1. Introduction modern UEM. In China, urban municipal supervision and manage- ment information system (UMSMIS) is a new platform created to China is undergoing an intensive urbanization process that integrate many environmental management resources to imple- increasingly imposes severe disturbances on urban ecosystem ment all-time and all-round UEM (Ministry of Construction, 2007). and further generates many urban environmental problems (Shao The core of UMSMIS is urban grid management mode that divides et al., 2006; Yang, 2013). This increases the difficulty of urban supervised area into grids based on administrative divisions and environmental management (UEM). Furthermore, the traditional each grid has the specific supervisors (Li et al., 2007). When grid management mode, such as regular or irregular inspections of supervisors report environmental problems through 3S technol- supervision organizations and reporting environmental problems ogy (Remote Sensing, Geographic Information System and Global through telephone or mail by people, cannot meet the demand of Position System) and network communication technology, the command center of UMSMIS will receive information in real time and obtain the precise locations of these problems. Then the ∗ responsibility department will be confirmed and further informed Corresponding author at: State Key Laboratory of Urban and Regional Ecol- ogy, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences. to process these environmental problems. By the establishment of Address: 18 Shuangqing Road, Haidian District, Beijing, 100085, China. UMSMIS, it can make UEM more effective and also can make the E-mail address: [email protected] (X. Fu). https://doi.org/10.1016/j.ecolind.2016.09.020 1470-160X/© 2016 Elsevier Ltd. All rights reserved. R. Dong et al. / Ecological Indicators 94 (2018) 52–69 53 Table 1 department responsibility clearer. With the operation of UMSMIS, Subdistricts of Dongcheng District. a large amount of environmental problems events are recorded as point features in the system and this big data set contains varieties ID Subdistrict of knowledge of the urban environment and human life. Through 01 Donghuamen Subdistrict analyzing the spatial distribution and composition of these points 02 Jianguomen Subdistrict of environmental problems (POEPs), it can provide information to 03 Chaoyangmen Subdistrict 04 Jingshan Subdistrict allocate urban management resources and improve urban plan- 05 Dongsi Subdistrict ning. 06 Jiaodaokou Subdistrict Points of interest (POIs) are landmarks and attractions on the 07 Andingmen Subdistrict electronic map which can arouse the users’ interest. The attributes 08 Beixinqiao Subdistrict 09 Dongzhimen Subdistrict contained in POIs not only include geographical location informa- 10 Hepingli Subdistrict tion, but also include social-economic information, such as name, category and service provided at the point (Ordnance Survey, 2013). Thus POIs can be treated as the reflection of human activities and Table 2 have been applied to identify urban land use and analyze urban POEPs classification scheme. functions (Zhao et al., 2011; Li et al., 2015). Bakillah et al. (2014) Code Class used POIs as ancillary data to estimate population at building level. 1 Transportation (TP) Li et al. (2015) had analyzed the spatial distribution patterns of 2 Building land (BL) urban functions in Beijing based on the data of POIs. Malleson and 3 Public facility (PF) Andresen (2016) explored the impacts of the components of POIs on 4 City appearance (CA) the crime rate in London. Indeed, the occurrences of POEPs may be 5 Landscaping (LN) mainly associated with population distribution, geographical fea- tures, and human activities (She et al., 2013). Consequently, the Table 3 hypothesis is that the occurrences of POEPs may be driven by POIs. Main attributes contained in the POEPs record. Therefore examining the relationships between POEPs and POIs Attribute Content may contribute to the prediction of generation of environmental problems and further prevent them from happening. Problem ID 479213 Reported Time 2009-10-30 16:28:50 This study takes the Dongcheng District of Beijing in China as Event One rainwater manhole cover is lost in a case to examine the relationships between environmental prob- the southeast corner of No.10 building lems and POIs in an urban area. The paper attempts to fulfill two on Zhangzizhong Road main objectives. First, we apply the spatial statistics to identify the Reporter Administrator spatial patterns of POEPs at three levels: global, local, and road level. Category Public facility Subclass Rainwater manhole cover Second, based on the Kernel Density Estimation (KDE) of POEPs Subdistrict Jingshan Subdistrict and POIs, we explore the relationships between POEPs and POIs Responsible Grid Wangzhima Grid at the three levels. Results from this study are expected to pro- X Coordinate 504537.5 vide suggestions to optimize the allocation of urban management Y Coordinate 307262.7 resources. Processing department Municipal Engineering Management Office of Dongcheng District The rest of this paper is organized as follows. In Section 2, the datasets and spatial statistical methods are introduced, and the steps for data processing are presented. Section 3 reports the main 3.21% of Beijing’s total resident population in the same year. The findings. The discussions are given in Section 4. Section 5 provides population density was 22218 people per square kilometer (Beijing the main conclusions. Municipal Bureau of Statistics and NBS Survey Office in Beijing, 2010). 2. Material and methods 2.2. Data 2.1. Study area 1 2.2.1. POEPs dataset The study area of this paper is Dongcheng District of Beijing. UMSMIS in Dongcheng District had come into service since Dongcheng District was located in the east of Beijing’s urban center 2 2004. In this study, the records of POEPs from 2009-06-01 to (Fig. 1). It covered 25.34 km and was divided into 10 subdistricts 2009-11-30 were collected from UMSMIS (Fig. 2). These POEPs (Table 1). As one of the central districts of the capital, Dongcheng were classified into five classes (Ministry of Construction, 2007) District was an area that integrated politics, developed commer- (Table 2). Each record had a standard set of attributes. In this study, cial services and culture tourism with plentiful resources. On the we focused on the attributes listed in Table 3, such as the Event, one hand, Dongcheng had abundant cultural
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