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

State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China

College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China

Department of Mathematics, City University of Hong Kong, Hong Kong, China

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.

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

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 relics, such as the

Reporter, Category, and Processing department. These metadata

Palace Museum and Drum Tower. On the other hand, there were

represented the set of instructions or documentation describing the

many national ministerial committees, lots of ofﬁce buildings and

content, context, quality, structure and accessibility of a dataset in

shopping malls, and a great number of star hotels and restaurants

the UMSMIS.

(Dongcheng District People’s Government, 2014).

In 2009, the gross regional domestic product was 94.54 billion,

representing 7.78% of Beijing’s gross domestic product. The resident 2.2.2. POIs dataset

population of Dongcheng was 563 thousand people, accounting for A point of interest is a point location that represents a spe-

ciﬁc geographic entity in a city. It contains abundant information,

including not only the coordinate information but also many social-

1 economic attributes, for example, the type of service provided at the

The old Dongcheng District was merged together with Chongwen District to

point. Thus, POIs can be treated as reﬂections of human activities

form the new Dongcheng District in 2010. In this study, the term “Dongcheng Dis-

trict” always refers to the old Dongcheng District. in a city.

54 R. Dong et al. / Ecological Indicators 94 (2018) 52–69

Fig. 1. Study area.

In this study, the Beijing POIs dataset was collected by AutoN- expressway was the 2nd ring road, including North 2nd ring road

avi, a corporation offering web mapping and navigation services in and East 2nd ring road. Major roads constitute the skeleton of the

China. Due to the restriction of accessibility, the dataset used in the urban road network and carry the greatest volume of urban trafﬁc.

study retrieved data in spring 2014. However given the relatively Minor roads connect to major roads and enter deep into most urban

stable urban layout of Dongcheng, this dataset could considerably areas. No-navigation roads always belong to organizations or cor-

reﬂect the urban functions of Dongcheng in 2009. The functional porations and are relatively closed. The road dataset was checked

information contained in this dataset was classiﬁed into 17 cate- and ﬁxed to attain topological accuracy. As the main intersections

gories as listed in the table below (Table 4). of urban trafﬁc, crossroads were extracted using the road data and

trafﬁc light data. Furthermore, because one purpose of collecting

crossroads data was to distinguish the different sections of roads,

2.2.3. Road dataset

roundabouts were also contained in the crossroads data. In addi-

The road dataset was also collected by AutoNavi and included

tion, this study selected 12 routes to examine the relationships

road data and trafﬁc light data. The urban roads are mainly classi-

between POEPs and POIs (Table 5). These routes were the main

ﬁed into four hierarchical classes: urban expressway, major road,

roads in Dongcheng District and included 6 East-West routes and

minor road and no-navigation road. In Beijing, urban expressways

are the ring roads encircling the city centre. In this study, urban

R. Dong et al. / Ecological Indicators 94 (2018) 52–69 55

Fig. 2. Distribution of POEPs in Dongcheng District.

6 South-North routes. The routes consisted of urban expressways, of the spatial analysis, we needed to unify these coordinate sys-

major roads and minor roads. tems. Here, the WGS 1984 Web Mercator Coordinate System was

chosen as the underlying coordinate system. Using the coordinate

2.2.4. Boundary dataset transformation and spatial adjustment, we transformed the POEPs

The boundary data for Dongcheng District was derived from dataset to spatially match the other datasets.

AutoNavi. Based on the trials and our experiences, a 50 m × 50 m size ﬁsh-

net covering the study area was created. At this cell size, the details

of the spatial patterns of POEPs could be illustrated sufﬁciently.

2.3. Spatial analysis

Then, the Spatial Join tool was utilised to integrate the POEPs into

the ﬁshnet cell. An attribute ﬁeld containing the number of POEPs

2.3.1. Data preparation

falling within each cell was constructed. Last, the ﬁshnet cells con-

Prior to estimating the spatial pattern of POEPs and exploring the

taining no POEPs were removed. In addition, the Spatial Join tool

relationship between POEPs and POIs, the data preprocessing was

was also applied to aggregate the POEPs to the roads within a dis-

needed. The coordinate system of POEPs data was the Beijing Local

tance of 15 m. Meanwhile, the spatial weights matrices used in the

Coordinate System, while the other datasets referred to the WGS

calculation of Global Moran’s I statistic and Getis-Ord Gi* statistic

1984 Web Mercator Coordinate System. To ensure the correctness

56 R. Dong et al. / Ecological Indicators 94 (2018) 52–69

Table 4

value to identify whether the POEPs were statistically signiﬁcantly

POIs classiﬁcation scheme.

clustered at the global level.

Code Class Then, after they were spatially joined, the ﬁshnet data and the

road data were applied to measure the spatial clusters, i.e. hot spots

A Automobile service (AS)

B Food and beverage (FB) or cold spots, at the local and road levels using the Getis-Ord Gi*

C Shopping (Shop) statistic, respectively. The Getis-Ord Gi* statistic was a useful mea-

D Daily service (DS)

sure for identifying statistically signiﬁcant spatial clusters on a local

E Sports and entertainment (SE)

scale. It measured the locations where POEPs occurred frequently.

F Health (Health)

In addition, a 200 m buffer region was created around each center

G Accommodation (Accom)

H Tourist attraction (TA) point of the hot spot cell. This buffer region was called the hot spot

I Commercial/Housing (CH)

region (HSR).

J Government and social groups (GSG)

K Science and cultural (SC)

L Transportation (TP) 2.3.3. Relationships between POEPs and POIs

M Financial insurance institution (FI) For the density distributions of POEPs and POIs, they were esti-

N Corporate/Business (CB)

mated at Dongcheng District level using KDE, respectively. The grid

O Road-afﬁliated facility (RAF)

size was 50 m × 50 m, and the search radius was 200 m. Then, the

P Place name (PN)

KDE values were normalized by:

Q Public facility (PF) x x - min xnorm = (1) x x

max- min

(Getis and Ord, 1992) were constructed for the ﬁshnet data and the

road data, respectively. Because the inﬂuence of neighbors decayed where x is the KDE value, xmin is the minimum value of KDE and

as the distance increased, the inverse distance weight scheme was xmax is the maximum value of KDE. The correlation coefﬁcient of

chosen with a 200 m threshold distance, which was approximately the KDE values of POEPs and POIs was measured at the Dongcheng

the length of half of a block. District and HSRs levels. Meanwhile, the POIs that fell into HSRs

were extracted to analyze the components of POIs around the hot

2.3.2. Spatial pattern analysis spots of POEPs. The KDE values of POEPs and POIs were reclassi-

First, this study analyzed spatial pattern of POEPs at the global ﬁed into 5 classes using the Natural Breaks method. The 5 classes

level with Global Moran’s I statistic and Average Nearest Neighbor were high, relatively high, moderate, relatively low, and low. Based

(ANN) index. These two statistics both returned a z-score and p- on the reclassiﬁcation results, four groups of density class were

Table 5

Main information of 12 routes.

Route ID Road Section Road Category Direction

Jianguomen Inner Street (JGMI St) Major road

East=West 1 East Chang’an Avenue (ECA Ave) Major road

West Chang’an Avenue (WCA Ave) Major road

Chaoyangmen Inner Street (CYMI St) Major road

Dongsi West Street (DSW St) Major road

East=West 2

Wusi Street (WS St) Major road

Jingshan Front Street (JSF St) Minor road

Workers’ Stadium North Road (WSN Rd) Major road

Dongsishitiao (DSST) Major road

East=West 3 From east to west (The start point

Zhangzizhong Road (ZZZ Rd) Major road

was at east)

Di’anmen East Street (DAME St) Major road

Dongzhimen Outer Street (DZMO St) Major road

Dongzhimen Inner Street (DZMI St) Major road

East=West 4

Jiaodaokou East Street (JDKE St) Major road

Gulou East Street (GLE St) Minor road

East=West 5 North 2nd Ring Road Urban expressway

Jiaolin Alley (JL Aly) Minor road

East-West 6 Hepingli Middle Street (HPLM St) Minor road

Andeli North Street (ADLN St) Minor road

South=North 1 East 2nd Ring Road Urban expressway

Beijing Station Street (BJS St) Major road

Chaoyangmen South Alley (CYMS Aly) Major road

Chaoyangmen North Alley (CYMN Aly) Minor road

South=North 2

Dongzhimen South Alley (DZMS Aly) Major road

Dongzhimen North Alley (DZMN Aly) Major road

Hepingli East Street (HPLE St) Major road

Chongwenmen Inner Street (CWMI St) Major road

Dongdan North Street (DDN St) Minor road

Dongsi South Street (DSS St) Minor road From south to north (The start

South=North 3

Dongsi North Street (DSN St) Minor road point was at south)

Yonghegong Street (YHG St) Minor road

Hepingli West Street (HPLW St) Major road

South=North 4 Wangfujing Street (WFJ St) Major road

Meishuguan Back Street (MSGB St) Major road

Jiaodaokou South Street (JDKS St) Major road

South=North 5

Andingmen Inner Street (ADMI St) Major road

Andingmen Outer Street (ADMO St) Major road

South Luogu Alley (SLG Aly) Minor road

South=North 6

North Luogu Alley (NLG Aly) Minor road

R. Dong et al. / Ecological Indicators 94 (2018) 52–69 57

Fig. 3. Distribution of POEPs by subdistrict and in Dongcheng District.

Fig. 4. POEPs density of each subdistrict.

selected: High=High, High=Low, Low=High and Low=Low. In the these four groups were extracted to assist our exploration of the

density group, the former density class meant the density class of relationships between POEPs and POIs. Last, the proﬁles of the KDE

POEPs and the latter was that of POIs. The regions belonging to values of twelve routes were analyzed to illustrate the relation-

58 R. Dong et al. / Ecological Indicators 94 (2018) 52–69

Fig. 5. POEP line density of different road classes.

ships between POEPs and POIs along the main roads in Dongcheng 3.2.2. At the local level – in the HSRs

District (Spicer et al., 2016). Based on the result of Gi* statistic, this study extracted 35

hot spots at the 0.05 signiﬁcance level (Fig. 6). These spots were

coded based on their latitude. There was no statistically signiﬁcant

2.4. Software

cold spot. Among the hot spots, 14 were located in Chaoyangmen

Subdistrict. In this subdistrict, the hot spots concentrated around

The spatial analysis methods, such as Global Moran’s I, Getis-

Yanyue Hutong, Bensi Hutong, Neiwubu Street, Shijia Hutong

Ord Gi* and KDE, were completed by ArcGIS 10.2 (ESRI, 2013).

and Dongluoquan Hutong. Three subdistricts, including Beixinqiao,

The descriptive statistics were calculated and plotted using Python

Dongsi and Jiaodaokou, contained no hot spots. Of the hot spots,

with NumPy, SciPy and Matplotlib packages (Hunter, 2007; Jones

only 5 were situated within 15 m of the major roads or urban

et al., 2015; Van Der Walt et al., 2011; Van Rossum and Python

expressways. Other hot spots were located near the minor roads.

community, 2015).

The POEPs falling into the HSRs were attained. According to the

analysis of the components of these POEPs in the 35 HSRs, the City

3. Results

appearance category had the largest total number of points, fol-

lowed by Public facility and Landscaping (Fig. 7). This result was

3.1. Descriptive statistics of POEPs

slightly different from those for Dongcheng District, of which the

Public facility class accounted for the largest percentage. Building

A total of 4992 points of environmental problems were found

land had the smallest number of points, which was similar to the

in Dongcheng District. The Public facility category had the largest

result for Dongcheng District. For the 35 HSRs, the number of POEPs

percentage of points, which was 54.05% of the total points. Among

presented an undulating curve change from south to north (Fig. 8).

the ten subdistricts, Donghuamen Subdistrict had the greatest

The higher values were concentrated in Chaoyangmen and Hep-

number of points, followed by Hepingli and Beixinqiao (Fig. 3).

ingli Subdistricts. Meanwhile the components of POEPs for each

Chaoyangmen Subdistrict had the highest POEP density, which

HSR were also analyzed (Fig. 9). In Fig. 9, the former number and

was approximately 300 points per square kilometer (Fig. 4). In

the latter number presented in parentheses in the title for each HSR

these subdistricts, the Public facility category always contained the

represented the number of types and the total amount of points,

largest number of points while the Building land category always

respectively. For example, in the title “No.1 HSR (3, 16)”, the num-

had the smallest number. At the road level, minor roads had the

ber 3 indicated the number of types and the number 16 indicated

highest POEPs line density, and the urban expressway had the low-

the total number of points.

est POEPs line density (Fig. 5).

Among the 35 HSRs, No.11 HSR had the largest number of POEPs,

while No.1 HSR had the smallest number. With consideration of

3.2. Spatial pattern of POEPs

the composition of POEPs in each HSR, the major environmen-

tal problems category changed from south to north. For the ﬁrst

3.2.1. At the global level – in Dongcheng District

six HSRs, from No.1 to No.6, Public facility problems were the

The result of Global Moran’s I statistic showed that the z-score

major problems. For most of the HSRs located in or near Chaoyang-

was 7.31 greater than 2.58 and p value was less than 0.01. This

men Subdistrict, from No.7 to No.21, City appearance problems

denoted that POEPs in Dongcheng District were signiﬁcantly clus-

accounted for the largest percentage. For the HSRs located in

tered. A similar result was achieved using the ANN index. The

Dongzhimen Subdistrict, from No.23 to No.26, and for the last three

observed average nearest neighbor distance was 23.29 m, which

HSRs, No.33–35, Landscaping problems were the major problems.

was less than the expected mean distance 46.54 m. The z-score was

In addition, among the environmental problems, Public facil-

−67.52, which was less than −2.58, and the p value was less than

ity problems were found in every HSR. City appearance problems,

0.01. So POEPs at the Dongcheng District level were also signiﬁ-

Landscaping problems and Transportation problems were identi-

cantly clustered at the 0.01 level.

R. Dong et al. / Ecological Indicators 94 (2018) 52–69 59

Fig. 6. Distribution of 35 hot spots using the Gi* statistic.

ﬁed in 31 HSRs, 30 HSRs and 29 HSRs, respectively. Building land of Transportation, Public facility, City appearance and Landscap-

problems only appeared in 11 HSRs. ing types, while only Transportation and Public facility types were

identiﬁed around major roads. In these POEPs along minor roads,

the major types were Transportation and Public facility, while the

3.2.3. At the road level

major POEPs type was Transportation for major roads.

Using the Gi* statistic, this study found that 25 road sections

were statistically signiﬁcant high value clusters (p < 0.05) (Fig. 10).

Most of them were located in Chaoyangmen Subdistrict, and this 3.3. Relationships between POEPs and POIs

result was comparable to the distribution of HSRs. Among these

roads, 18 roads were minor roads, with a length of approximately 3.3.1. At the global level – in Dongcheng District

5.54 km, representing 72.83% of the total. Five roads were major Based on the KDE results, the frequency distributions of the

roads, and one was a no-navigation road. This result illustrated that KDE values of POEPs and POIs were plotted (Fig. 12). As the result

in Dongcheng District, environmental problems were concentrated showed, the frequency distributions for KDE values of POEPs and

in the minor roads that went deep into urban living areas. Mean- POIs were not normally distributed. Hence, this study utilized the

while, the types of POEPs around these roads differed (Fig. 11). The Spearman’s rank correlation coefﬁcient statistic to measure the

environmental problems found along the minor roads consisted correlation between the KDE values of POEPs and POIs.

60 R. Dong et al. / Ecological Indicators 94 (2018) 52–69

31) exhibited strong positive correlations with correlation coefﬁ-

cients greater than 0.70. Regarding these 4 HSRs, one was located

in Chaoyangmen Subdistrict, one was in Hepingli Subdistrict, and

two HSRs (No.24 and No.26) were situated in Donghuamen Sub-

district. Moreover, the sum components of POIs in the 35 HSRs

were examined (Fig. 15). In terms of the number of points, the ﬁve

largest classes were Food and beverage, Public facility, Daily ser-

vice, Government and social groups and Shopping. By contrast, the

ﬁve smallest classes were Sports and entertainment, Health, Place

name, Tourist attraction and Automobile service. Regarding the

frequency of occurrence, Food and beverage, Public facility, Daily

service, Shopping, Transportation, Science and cultural and Accom-

modation were the most frequent types (Fig. 16). The frequencies

of occurrence of these classes in the 35 HSRs were 34, 34, 34, 33, 31,

31 and 31, respectively. The ﬁve least frequent classes were Corpo-

rate/Business, Financial insurance institution, Place name, Tourist

attraction and Automobile service, with the following frequencies

of occurrence: 17, 13, 13, 6 and 3, respectively. This result was con-

sistent with the main functions of Dongcheng District. Then, the

study focused on the components of POIs in the 4 HSRs with strong

signiﬁcant correlations (Fig. 16). In HSR No.18, situated around

Fig. 7. Components of POEPs in the 35 HSRs.

Yanyue Hutong, the main types of POIs were Public facility, Science

and cultural and Food and beverage, while the main types of POEPs

Fig. 13 illustrated the relationship between the KDE values of

were Public facility and City appearance. In HSR No.24, located

POEPs and POIs. The correlation coefﬁcient was 0.53, and the p

around Beijing Workers’ Gymnasium, the main types of POIs were

value was zero. The result suggested a positive correlation between

Food and beverage, Public facility and Shopping, while the main

the density of POEPs and the density of POIs and it supported the

types of POEPs were Public facility and Landscaping. In HSR No.26,

abovementioned hypothesis to some extent.

the main types of POIs were Commercial/Housing, Public facility

and Food and beverage, This HSR was a typical residential area. The

3.3.2. At the local level – in the HSRs

major type of POEPs was Landscaping. In HSR No.31, located near

After measuring the correlation of KDE values of POEPs and POIs

Minwangbei Hutong, the main types of POIs were Food and bev-

at the Dongcheng District level, this study further analyzed the

erage, Shopping and Daily service, and the major types of POEPs

relationships between KDE values of POEPs and POIs at the HSR

were City appearance and Public facility. These results illustrated

level. Fig. 14 displayed the Spearman’s rank correlation between

that the major types of POEPs were consistent with the compo-

the KDE values of POEPs and POIs in each HSR. The result showed

nents of POIs. Furthermore, the similar phenomenon was observed

that 12 HSRs presented statistically signiﬁcant positive correla-

in some HSRs with signiﬁcantly positive correlations. For exam-

tion (p < 0.01), while 4 HSRs having statistically signiﬁcant negative

ple, in HSR No.7 and HSR No.8, the major types of POIs were Food

correlation (p < 0.01). For the 12 HSRs with positive correlations,

and beverage, Shopping, Daily service and Transportation, while

8 HSRs (No.7–8, No.11, No.13–14, No.17–19) were situated in or

the major types of POEPs were City appearance and Public facil-

near Chaoyangmen Subdistrict, 3 HSRs (No.24–26) were located

ity. Meanwhile, the number of POEPs belonging to Transportation

in Dongzhimen Subdistrict and one (No.31) was located in Hep-

was higher than other HSRs. However, in some HSRs, the relation-

ingli Subdistrict. Among these 12 HSRs, 4 HSRs (No.18, 24, 26,

Fig. 8. Histogram of the distribution of POEPs in the 35 HSRs.

R. Dong et al. / Ecological Indicators 94 (2018) 52–69 61

Fig. 9. Distribution of POEPs in each HSR.

ship between POEPs and POIs was confusing. For example, in HSR 3.3.3. At the road level

No.12 with a signiﬁcantly negative correlation, there were a few Because most of the hot spot roads detected by the Gi* statis-

POIs but the POEPs belonging to City appearance types were higher tic were located near the 35 HSRs, and a few POIs fell into the

comparable to other HSRs. 15 m buffer zone of hot spot roads, this study examined the rela-

In addition, the regions belonging to four groups of density tionships between POEPs density and POIs density along the 12

class were obtained (Fig. 17). No cell belonged to High–High routes (Fig. 18). Fig. 19 elucidated that the KDE values of POEPs and

group. High=Low regions situated near several HSRs. Low=High POIs around crossroads were generally higher than those around

regions were mainly located around Wangfujing Street, which was other parts of road. Moreover, in most parts of these routes, the

a famous commercial pedestrian street with many shopping malls KDE values of POEPs were higher than those of POIs. Further-

and ofﬁce buildings that resulted in many POIs existing in a rela- more the degree of POEPs density ﬂuctuation was generally greater

tive small region. Thus the POIs density was high. Low–Low regions than that of POIs density ﬂuctuation. However, in the former half

were mainly the cultural relics, parks and relatively closed institu- of South=North 4 route, that was the core part of Wangfujing

tions. Street, the KDE values of POIs were higher than that of POEPs.

In South=North 6 route, the KDE values of POEPs and POIs along

62 R. Dong et al. / Ecological Indicators 94 (2018) 52–69

Fig. 10. Spatial distribution of hot spot roads.

South Luogu Alley were higher than those along North Luogu Alley. ber of points. Meanwhile, in the 35 HSRs, the major POEPs types

The South Luogu Alley is a famous tourism attraction, while North had the characteristic of heterogeneity that it changed from south

Luogu Alley is not such famous. Furthermore, a relatively strong sta- to north. The major POEPs type was Public facility in No.1–No.6, and

tistically signiﬁcant positive correlation was found between POEPs it changed to City appearance in No.7–No.21. Furthermore, Land-

density and POIs density on the South=North 6 route. scaping problems were the major problems in No.23–No.26 and

the last three HSRs. Therefore when allocating the management

resources at different locations, the major POEPs types should be

4. Discussion

considered to optimize the management efﬁciency.

4.1. The components of POEPs differed at different levels

4.2. POEPs occurred more along the minor roads

The components of POEPs may have multi-scale characteristic

such that the major POEPs type differed at multiple levels. At the We observed that POEPs were mainly distributed along the road

District and Subdistrict levels, the Public facility type accounted network in Dongcheng District. At the road level, the minor roads

for the largest percentage, while at the local level (i.e., in the 35 had the highest line density of POEPs. Furthermore, most of the 35

HSRs), City appearance problems displayed the largest total num- hot spots detected by the Getis-Ord Gi* statistic were also located

R. Dong et al. / Ecological Indicators 94 (2018) 52–69 63

near minor roads. Of the 25 hot spot roads, 18 were minor roads.

Meanwhile, when compared to major roads, the environmental

problems occurred more along the minor roads, in terms of not

only the number of points but also the number of POEPs types.

This phenomenon may be determined by the function of the minor

roads. The minor roads connected the major roads and entered deep

into most areas of the city; especially in Dongcheng District, there

were many Hutongs that belonging to minor road. Thus the count

of POEPs along minor roads was larger than other road classes

and the management department should consider building more

infrastructures in these areas.

4.3. Relationships between POEPs and POIs were complex

Although there was a signiﬁcantly positive correlation between

the KDE values of POEPs and POIs at the global level (i.e., Dongcheng

District level), the similar correlation was not found across all 35

Fig. 11. Components of POEPs along different road classes. HSRs. Of the 35 HSRs, 16 exhibited a signiﬁcant correlation between

the KDE values of POEPs and POIs at the 0.01 level and at the 0.05

level the number was 24. Among the 16 HSRs, there were 4 HSRs

having the negative correlation and 12 HSRs having the positive

correlation, especially of the 12 HSRs, 4 with a strong positive corre-

lation. In these 4 HSRs with a strong positive correlation, the major

POEPs types were mainly consistent to the components of POIs.

These results demonstrated that the major types of POEPs were

consistent with the components of POIs to some extent. Regarding

the HSRs with no signiﬁcant correlation at the 0.05 level, exclu-

sion of HSR No.23, in the other HSRs the KDE values of POIs kept

constant. This may denote that the distribution of POIs was uniform

distribution, while the POEPs density differed at different locations.

In HSR No.23, there were many corporations in a small region that

leading to a high KDE values of POIs, while the POEPs density was

relative stable. In the HSRs with a few POEPs and many POIs, it could

be interpreted as that there had been allocated many management

resources. However, in some HSRs the relationships between POEPs

and POIs were confused. In addition, the results of four groups of

density class illustrated that parks and relatively closed organi-

Fig. 12. Frequency distributions for KDE values of POEPs and POIs (after normaliza- zations were mainly belonging to Low–Low region. These results

tion).

indicated that the relationships between POEPs and POIs were com-

Fig. 13. Spearman’s rank correlation for the KDE value of POEPs and POIs.

64 R. Dong et al. / Ecological Indicators 94 (2018) 52–69

Fig. 14. Spearman’s rank correlation for the KDE value of POEPs and KDE value of POIs in each HSR.

plex, and the occurrences of POEPs were not only driven by POIs. 4.5. Data limitation and uncertainty analysis

The further work needed to consider more factors, such as urban

form and population distribution. On account of lacking the POIs data in 2009, the POIs dataset

used in this study captured data in spring 2014, that didn’t

match the content time of POEPs. Although the urban layout of

4.4. The crossroad had a higher density of POEPs and POIs than

Dongcheng District was stable during 2009–2014 and then the POIs

other parts of a road

data in spring 2014 could basically reﬂect the urban functions of

Dongcheng District, this inconsistency may introduce some uncer-

As the intersection of urban trafﬁc, the crossroad had the higher

tainty into the analysis results. In addition, because this study didn’t

dense of human activities. Based on the proﬁles of 12 routes, the

acquire the urban grid data used in urban grid management system,

KDE values of POEPs and POIs around crossroads were generally

as the substitution a 50 m × 50 m ﬁshnet was created to identify the

higher than other parts of roads. Moreover, in most parts of these

hot spots. Thus the analysis results in this paper should be carefully

routes, the KDE values of POEPs were higher than POIs. And the

veriﬁed when applied to guide the UEM. Finally, the urban environ-

ﬂuctuation degree of KDE values of POEPs around crossroads was

mental supervisors were employed by their respective subdistricts.

generally larger than that of POIs. Thus the crossroads should also

Although the subdistricts had the same operating guidelines, in

be paid more attention for supervision.

R. Dong et al. / Ecological Indicators 94 (2018) 52–69 65

Fig. 15. Histogram of the distribution of POIs in the 35 HSRs.

Fig. 16. Histogram of the distribution of POIs in each HSR.

66 R. Dong et al. / Ecological Indicators 94 (2018) 52–69

Fig. 17. Distribution of four groups of density classes.

R. Dong et al. / Ecological Indicators 94 (2018) 52–69 67

Fig. 18. Distribution of the 12 routes.

terms of speciﬁc operating procedures, an environmental problem The spatial distribution of POEPs was statistically signiﬁcant clus-

may be recorded in one subdistrict but not recorded in another tered (p < 0.01) in Dongcheng District. Most of the hot spots and

subdistrict. This may impact the production of POEPs dataset and hot spot roads concentrated in Chaoyangmen Subdistrict, which

further inﬂuence the validity of the analysis results. should be allocated more management resources. The major types

of POEPs differed at different levels so that the components of POEPs

should be considered when conducting environment management.

5. Conclusions

Although there was a signiﬁcantly positive correlation between the

density of POEPs and POIs at the global level (p < 0.01), the major

To optimize the allocation of urban management resources and

types of POEPs were consistent with the components of POIs only

guide urban environmental management, this study had attempted

in some regions. At the road level, this study found that most of

to explore the spatial pattern of POEPs in Dongcheng District of

POEPs occurred around the minor roads and the crossroads had

Beijing, and examine the relationships between POEPs and POIs.

68 R. Dong et al. / Ecological Indicators 94 (2018) 52–69

Fig. 19. Proﬁles of the 12 routes.

the higher density of POEPs and POIs. Future research should con- Beijing Municipal Bureau of Statistics, NBS Survey Ofﬁce in Beijing, 2010. Beijing

Area Statistical Yearbook 2010. China Statistics Press, Beijing.

sider the impacts of other factors, such urban form and population

Dongcheng District People’s Government, 2014. Overview Dongcheng, http://

distribution, on the occurrences of POEPs.

www.bjdch.gov.cn/n8775435/n8817900/index.html (accessed May 1, 2016).

ESRI, 2013. ArcGIS Desktop: Release 10.2. Environmental Systems Research

Institute, Redlands, CA.

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