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Sensors and Materials, Vol. 35, No. 3 (2023) 941–963
MYU Tokyo
S & M 3220

Research on Allocation of Residential Spaceand Public Service Facilities Using Beijing as an Example
Zeyu Li,1,2 Cai Cai,1,2,3* and Yingchun Tao1,2,3
1Beijing Institute of Surveying and Mapping, No. 15 Yangfangdian Road, Haidian District, Beijing 100038, China
2Beijing Key Laboratory of Urban Spatial Information Engineering, Beijing 100038, China

3Analysis and Application of Urban Spatial Big Data in Bogang Yang Staff Innovation Studio,

Beijing 100038, China(Received November 2, 2022; accepted February 14, 2023)

Keywords: big data, residential space, public service facilities

Facing the increasingly strong demand for quality urban residential space, we studied thetemporal needs for and satisfaction with residential space and public service facilities incommunities and residential districts. In this study, data sources including geographical datafrom remote sensing satellite sensors, point of interest (POI), area of interest (AOI), and Baidu
Maps from mobile phone sensors were used, and multi-source data process and analysis
technologies were studied. A multi-level analytical method was established, which included theoverall analysis of the allocation of community public service facilities, the evaluation of three
categories of public service facilities in residential districts, and the analysis of population
mobility and characterizations of residents. A functional mix index and a coordinateddevelopment index were adopted, and an indicator evaluation system based on methods ofcomputing coverage and compliance was proposed. Taking the core area of Beijing as an
example, we analyzed detailed allocation conditions of public service facilities, including their
spatial and temporal insufficiencies. Our results show that residential districts in the core area of
Beijing should consider safe sites and park layouts within the walking range of living areas,
improve the quality of property services, and create a multi-functional living environment. In
this study, we offer methods of studying the distribution of small-scale urban population
structures and evaluating the quality of urban residential space; we thereby provide useful
information for the planning and management of urban public service facilities.

1.

Introduction

High-quality living environments can increase the happiness and comfort of residents.(1) The
COVID-19 epidemic has sounded an alarm bell for humanity. More attention should be paid tothe conditions that support residents’ living facilities and the integrity of layout at the level ofresidential spaces. Since the reform and opening up of China 44 years ago, population flow tocities has accelerated on a large scale as a result of urbanization. Owing to the fierce competition
between population and resource factors, the spatial distribution structure and evolution trend of
population have changed. Currently, research on the scale of countries, provinces, urban*Corresponding author: e-mail: caicai361448336@126.com.

https://doi.org/10.18494/SAM4232

ISSN 0914-4935 © MYU K.K.
https://myukk.org/

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agglomerations, cities, and communities is the main level at which the characteristics of thespatial distribution of populations are assessed. Studies have tended to focus on large-scale
population distribution differences, mesoscale mobility characteristics and network structure,and small-scale work to find a balance between housing and public facilities. The era of big data
makes it possible now to study the spatial structure of the population. The characteristics of highprecision, fast updating, and large sample size can make up for the traditional shortcomings oftime-consuming and labor-intensive studies with small sample size and low spatial property.
Yao detailed the concept of street space and summarized the existing quantitative methods ofassessing the quality of street space.(2) Wu elaborated the methods of improving spatial quality
from the perspective of planning, aiming at two spatial levels of residential area andcommunity.(3) Liang introduced the concept of the circle of life and evaluated the layout ofcommunity-friendly public service facilities from the perspective of efficiency and fairness.(4)Sun et al.(5) put forward countermeasures for the selection of resilient city indicators from three
aspects: organizational resilience, health resilience, and basic resilience. Regarding the spatial
structure of an urban population and influential factors, Yao and Shao(6) pointed out that China’s
population distribution presented a pattern of “large dispersion and small concentration.”
Characteristics of urban locations, especially the developed high-speed rail networks, were themain factors in population changes, and public services were the main welfare factors for
attracting more people. On the basis of data from the sixth census of Nanjing and factor
ecological analysis, Zhao et al.(7) found that housing prices, family situations, universities, andhigh-tech industry clusters can affect many aspects associated with population changes,
including the urban social space, the renewal of old urban areas, population movement, planning,and construction. Using GPS data, Shen and Chai(8) compared the distribution of time between
working and rest days, and studied the characteristics of the activity space of residents in asample community. They found that residents depend on urban space during working or restdays, and the degree of dependence was affected by the location of a residence, regional
consumption level, and employment location. Supporting facilities for promoting the balance
between work and home still needs to be strengthened. With a continuously increasing number
of applications of mobile big data in planning, Niu et al.(9) pointed out the advantages of theapplication to estimate “resident population.” They also clarified the logic and method of fourlevel sample expansion and expounded on the necessity of testing the total amount of data,taking Wuhan City as the sample area for verification. Using mobile phone signaling data, Wanget al.(10) identified the residential and working places within 253 sample residential areas andvisualized the distributions of employment core density and probable commuting distance. They
also summarized the typical distribution pattern and impact factors of employment space inresidential areas.
Currently, quantitative and applicable research results on the daily flow of population inresidential areas are lacking. In addition, residential areas with high daily population mobility
are vulnerable to urban emergencies. Paying more attention to these residential areas will help
improve the quality of residential life and overall resilience. At the same time, it also provides a
new perspective for refining residential area management, which is of practical significance forthe path of big data to promote community governance and service innovation.

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In this study, we explored the distribution characteristics of public facilities in residential
spaces in the core area of Beijing, taking communities and residential areas as research units.
Data sources including geographical data from remote sensing satellite sensors, point of interest(POI), area of interest (AOI), and Baidu Maps from mobile phone sensors were used, and multisource data process and analysis technologies were studied for the analysis of coverage, supply
and demand, and population mobility for public service facilities. With data on passenger flow
(dynamic number of people) from Baidu Maps, the characteristics and commuting rules of thepeak population in the morning and evening in 296 residential districts were analyzed. Our
analysis revealed the current characteristics of age, income, consumption level, profession, andtypes of cars in different residential districts, and strengthens our ability to accurately andscientifically manage the classification of populations in residential districts. Our work thereby
provides a method of studying the distribution characteristics of small-scale urban populations.

2.

Study Area and Materials

The core area of Beijing, composed of the Dongcheng and Xicheng Districts, is China’s
political and cultural center and reflects the image of the capital. The Regulatory Detailed
Planning of the Capital Functional Core Area (at the block level) emphasizes strengthening the
supplementation of educational resources for kindergartens and primary schools, balancing thelayout of the medical and health system, promoting the deep integration of property management
and grassroots social governance, optimizing the combined use of the ground bus network andtransportation terminals, enriching green spaces, organizing grassroots commercial networks,
resolving the shortcomings of sports facilities, and improving the abilities for disaster prevention,
mitigation, and relief.(11)The big data used in this study included POI, AOI, website data on house rentals, and Baidu
Map data. POI covers transportation, catering, sports, medical, and other categories; AOI iscommonly used for commercial residential/residential areas; websites for house rentals may
show details about residential areas; as can be seen from Fig. 1, Baidu Map data include
population portraits, which can be understood as the characteristics of populations and views ofpassenger flow processed and analyzed from user information based on massive map services.
The data volume within the core areas of Beijing (Dongcheng and Xicheng Districts) was morethan 1 million records per day in TEXT format. The passenger flow population updated hourlyrefers to the people located at a specific location. Population portrait attributes include coordinate
pair (X, Y), date, timestamp (per hour), portrait tag (category), and tag prediction estimatedpercentage. For analysis and research on residential districts, five labels were selected to depict
the population of residential areas, namely, age range, income capacity, professionalclassification, consumption level, and vehicle use status. Taking residential districts as theresearch unit, a portrait of the passenger flow population was calculated from 19:00 to 21:00 on
working days from July 15 to 31, 2019.

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Fig. 1.

3.

Methods

3.1

Data preprocessing

Current population categories based on Baidu Map data.

All data used in this study were processed into a dataset by data cleaning, data spatialization,and registration. By taking community and residential districts as the research units, information
was calculated from the dataset.
3.2

Overall analysis of allocation of community public service facilities

3.2.1 Functional mix index
To study the mixed functions of community public service facilities, a mixed index wasestablished, and information entropy was introduced to quantify mixed degrees of function.
Information entropy was used to measure the size of changes in uncertainty. If the mixed
degrees of function of the community are simple, the uncertainty is low and the entropy is small.
In contrast, if the mixed degrees of function of the community are complex, the uncertainty ishigh and the entropy is large.(12) The equation for this calculation isn

M = −∑ ( Si * LnSi ) ,

(1)

i =1

where Si indicates the proportion of service facility type i in the total POI of all facilities (i = 1, 2,
3, ..., 9).

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3.2.2 Coordinated development index
To measure the degree of coordination between the distribution of public service facilities
and the population in a community, a formula that involves the types, total amount, andpopulation distribution of facilities was introduced. The definitions are as follows:
Fi , j , kindexi , j , k =Populationi∑Fi i , j ,k(2)

∑ Population
i

9

,
i

nj

indexi = ∑∑indexi , j , k , and=j 1 =
k 1

i = 1, 2,3,…., 430; j = 1, 2,3,…,9; k = 1, 2,…, n j(3)where Fi,j,k represents the total number of public service facilities in categories j and k ofcommunity i. Populationi represents the number of permanent residents in community i.
indexi,j,k represents the coordinated development index of public service facilities in categories jand k of community i. nj is the number of subcategories included in category j. indexi,j,k > 1means a well-coordinated development and indexi,j,k < 1 means a poorly coordinateddevelopment. indexi represents the coordinated development index of community i and the sumof coordination indexes of various public services.(13)
3.3

Analysis of residential space and public service facilities allocation by residential
district unit

3.3.1. Indicator system
The construction principles of the indicator system include the principles of objectives,
comprehensiveness, scientificity, and operability. The quality of residential space must be
demonstrated by evaluation indicators, and the reasonable selection of indicators is very
important. The study of the quality of residential space is comprehensive and integrated, so theconstruction of a research evaluation system must include all aspects of the quality of residential
space, including infrastructure, residents’ lives, urban security, and ecological environment. The
research system is a technical means for the quantitative analysis and evaluation of the quality ofresidential space. To ensure that the research system enables an objective and comprehensive
evaluation of the quality of residential space, it is necessary to ensure that the selected research
indicators and calculation methods conform to basic science. If the research system seeks toachieve applicability and practicability, operability is an essential key requirement. The
selection, measurement, and calculation of research indicators must not be very complex, and the946

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various parameters in the formulas of the research system must not be difficult to obtain. The
reliability and credibility of the selected values must be ensured.
For analyzing the quality of urban residential space, an indicator system was studied andestablished, including infrastructure, health assessment, and urban quality. Considering thecomprehensiveness, scientificity, and operability, the following indicators were selected:(1) Infrastructure:• Transportation: bus station, entrance, and exit from the subway station;• Convenient commerce: supermarkets, fruit and vegetable markets, convenience stores, andrestaurants;• Education: kindergartens and primary schools;• Medicine: first-class hospitals, health service stations, and pharmacies;• Logistics: delivery outlets;(2) Health assessment:• Safety: emergency shelters, fire stations, and police stations;(3) Urban quality:• Science and education: museums, art galleries, children’s places, libraries, and exhibition
halls;• Sports: sports venues and comprehensive gymnasiums;• Parkland: city squares (communities) and flower beds/artificial green land (residential
areas).
3.3.2 Indicator evaluation method
3.3.2.1 Coverage
If the residential area has public service facilities Fj,k, it means that the residential area has
Fj,k coverage. The equation used is1, ∃Fj ,k ⊂ N1 ( Communityi )Ci , j ,k = ,
0, others(4)

where Communityi represents Community i; Ci,j,k reveals whether Communityi has facilities in
the radius of the living circle Fj,k; “1” means that facilities exist, indicating that it has been
covered; otherwise, the value is 0.(14)On the basis of the life service circle theory, each residential area was taken as a base point,and ArcGIS was used to establish buffer zones with radii of 300, 500, 800, and 1000 m as therange of the living circle of the residential area. Details of the service radius of various facilities
are given in Table 1.

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Table 1Service radius of public service facilities in residential districts.
Evaluation category Facility category
Facility name (service radius) (unit: m)
Traffic
bus stop (500); metro entrance and exit (800)convenience store (500); catering (300);
Conveniencesupermarket (300); vegetable market (500)
Infrastructure
Education
primary school (500); kindergarten (500)Medicine
grade ⅲ-A hospital (1000); health service station (500); pharmacy (300)
Logistics
Express (500)
Health assessment
Securitysafe places of refuge (1000); police station (800); fire station (800)
museum (1000); art museum (1000); children’s place (1000);Science education
library (1000); exhibition hall (1000)
Urban quality
Sportssports venues (500); comprehensive gymnasium (1000)
park (800); city square (1000); residential green space
Green land(either artificial green space or flower beds in residential district)

3.3.3.2 Rate of reaching a standard
The standard-reaching rate comprehensively reflects the substitutability of public service
facilities in real life.(13) In this paper, we define the connotation of “reaching the standard” ofpublic service facilities from three perspectives:(1) “Reaching the standard” can be divided into mandatory items and achievable items. Only
when mandatory items exist simultaneously can they be referred to as reaching the standard,
and achievable items can be regarded as additional supplements to mandatory items.
(2) Some facilities can replace each other. For example, supermarkets and vegetable markets
have similar functions, and either of them can be used. Primary schools and kindergartensare educational configurations for different ages, which are not mutually replaceable andindispensable. Only when they exist simultaneously can they be called “reaching thestandard.”(3) For health, science, and education facilities, each subcategory can be equipped with one
facility to meet the standard.
“Reaching the standard” is specifically defined as (1) “traffic” reaching the standards: eithera bus or subway station is acceptable; (2) “convenience” standards: supermarkets, convenience
stores, and fruit and vegetable markets are available, and at least one catering store is available;(3) “education” standards: primary schools and kindergartens are indispensable, and both must
exist simultaneously; (4) “medical” standards: a first-class hospital, health service station, andpharmacy are all available; (5) “logistics” standards: at least one delivery outlet exists; (6)“safety” compliance: there shall be at least one emergency shelter, fire protection, and public
security area; (7) “science and education” standards: museums, arts, children’s places, books,
and exhibition centers exist; (8) “sports” standards: sports venues and comprehensive
gymnasiums can meet the requirements of either category; (9) “park” standards: parks and citysquares are two types of outdoor sites, and at least flower beds or artificial green spaces areneeded in the community.

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3.4

Sensors and Materials, Vol. 35, No. 3 (2023)

Characteristics of residential populations

The above analysis was conducted from the perspective of space configurations of residential
areas and public service facilities, but the actual demand for public service facilities changes
markedly with the specific type and activity characteristics of residents. The public service
demands of floating populations in residential districts are the focus of the government. In
previous studies, more attention was paid to the registered residence population and permanent
residents, because it was difficult to obtain the data on the floating population. For Beijing, theresearch significance of the floating population is far more significant than that of the permanent
population. Particularly after the outbreak of the COVID-19 epidemic, the characteristics andmobility of the floating population have affected policy formulations by government staff andthe allocation of public service facilities.
A population portrait is equivalent to dividing people into detailed categories, a process thatassists accurate population management. The population portrait can be used to analyze thetrend of population age, gain insight into residents’ interests and preferences, monitor trends inpublic opinion, and identify characteristics of a population to improve the depth and accuracy ofmanagement. For example, an aging community may need more elderly care facilities; thedemand for actual parking spaces is not only related to the number of cars but also to the specifictimes of demand during the day; the management of residential districts with large floating
populations needs to be improved; the allocation of parking time can be optimized to meet theneeds of residents in a limited space. Therefore, in this study, we further analyze thecharacteristics of population mobility in residential areas and explore the satisfaction of residents
with residential areas and public services.
3.4.1 Characteristics of population mobility
Average passenger flow means the dynamic population in the region, “passenger” being
understood as “hourly people”, which can be used to assist in analyzing the mobility of
population.
Average passenger flow refers to the average population in a region at a given time within thespecified time frame of the study, reflecting average dynamic population per hour.(15) The
equation is∑p=n

M Pi , j

i, j

n

,

(5)

where M Pi , j represents the average population in residential area i at time j; pi,j represents the
population in residential area i at time j; and n represents days on which the research was carriedout: ( j = 0, 1 ,2, 3, …, 23; n = 13).
People flow is a ratio and not a specific value, which refers to the average movement ofpeople in and out of an area during a period. The equation isSensors and Materials, Vol. 35, No. 3 (2023)

Fli =

949

MaxM P − MinM P
i, j

i ,k

MaxM P

,

(6)

i, j

where Fli represents the people average movement in residential area i; MaxM P represents thei, jmaximum population in residential area i at time j; MinM P represents the minimum populationi ,kin residential area i at time k ( j = 20, 21, 22, 23; k = 8, 9, 10, 11).
3.4.2 Characteristics of population portrait
Five labels were selected to depict the population portrait of a residential area, namely, age
range, income capacity, professional classification, consumption level, and vehicle use status.
The portraits based on the size of the community enable the analysis of the social attributes ofresidents in detail.(16) The age range of the resident population enables the identification ofcommunities with a large proportion of aging members; the elderly care facilities in such
communities may then be supplemented and improved. The income level reflects the quality oflife.(17) Professional classification can measure the complexity of the owner structure ofresidential areas. For residential areas with a large proportion of self-employed and service
industries, population mobility is relatively large, and this mobility is usually the focus of
epidemic prevention and control in residential areas. The number of cars in a residential area canbe calculated, which is of scientific significance for the construction of parking spaces.
Seven age groups defined in the study were as follows: below 18, 18–24, 25–34, 35–44, 45–
54, 55–64, and 65 years and above.(18) The income capacity was divided into five categories: lessthan 2500 yuan, 2500–3999 yuan, 4000–7999 yuan, 8000–19999 yuan, and more than 20000
yuan a month. Professions were divided into six categories: production operators, civil servants,
professional technicians, managers and entrepreneurs, self-employed, and service personnel.(19)The level of money consumption was divided into low, medium, and high. The use of vehicles
was indicated as one of the two types: with a vehicle and without a vehicle.

4.

Results

4.1

Community results

4.1.1 Results of functional mix index
From each sub-item, as can be seen from Table 2, the functional mix index of community M1is the highest, both higher than the next highest average of 1.13 and with the highest maximumof 1.78. The average mix index of the community is 1.47, which is much higher than those ofother types of communities, but only accounts for 10.93% of the total number of communities in
the core area. It is characterized by the highly mixed and balanced distribution of various
facilities. M3 and M4 communities with medium functional mix indices account for 48.13% of
the total number of communities. The mix of parks and sports facilities in such communities is950

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Table 2
Grading list of the mixed index of community public service facility functions.
Type
Average functional mix index
1.47 (max = 1.78)
M1
1.13 (max = 1.27)
M2
0.91 (max = 1.01)
M3
0.70 (max = 0.80)
M4
0.43 (max = 0.57)
M5
0.00
M6

low, and residents’ outdoor sports venues are limited. There are seven communities with afunctional mix index of zero, which have no public service facilities in their areas.
4.1.2 Results of the coordinated development index
As can be seen from Fig. 2, an average coordinated development index of less than 0.5
accounts for 23.48% of the total number of communities, and the lowest coordinated development
index in the San Li He Community is 0.06. The average coordinated development index is
between 0.5 and 1, accounting for 35.82% of the total number of communities. The coordinateddevelopment index overall is at an intermediate level. The per capita coverage of such
communities with regard to places of refuge and scientific, educational, and cultural facilities
needs to be strengthened. The average coordinated development index greater than one accounts

Fig. 2.

(Color online) Spatial distribution map of coordinated development indices.

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for 40.70% of the total number of communities, which indicates that these communities haveadvantages over other communities. From the perspective of spatial distribution, as can be seenfrom Fig. 3, the average coordination index of Dongcheng District has clear advantages
compared with that of Xicheng District. The development of Xicheng District is relatively
comprehensive. Compared with Dongcheng District, Xicheng District has disadvantages interms of fire stations, parks, and kindergartens.
4.2

Residential district results

4.2.1 Results of coverage
From the perspective of Dongcheng and Xicheng, as can be seen from Fig. 4, 36% of the 25
indicators have very high residential area coverages, all exceeding 85%. In particular, thecoverage of catering, convenience stores, bus routes, and police stations is 100%, indicating thatthe overall level of residents’ living facilities is good. A total of 53% of residential areas have acoverage of 71–85%, and the coverage of public facilities is at an intermediate level. The sports
venues, children’s places, and flower beds in such areas need further improvement. Theircoverage rate is 55–70%, accounting for 9% of residential areas. The coverage of safe sheltersand fire stations in such areas also needs improvement,(20) as it is only 1% of the residential
areas. The coverage of each sub-item of residential quarters needs to be further strengthened
from the coverage rates of sports venues, emergency shelters, and urban squares. Particularly
during the pandemic, the emergency shelters within the walking range of the residential areas
are particularly important.

Fig. 3. (Color online) Per capita coordination index of various public facilities of Dongcheng and Xicheng
Districts.

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Fig. 4.

(Color online) Spatial distribution map of coverage of residential areas.

4.2.2 Results of reaching a standard rate
Among the nine major public service facilities, the average rate of reaching the standard inresidential areas is 91.51%, and 67.45% of residential areas are above the average level. In termsof sub-items, the complete percentages of transportation, convenience, education, medical care,and logistics are 99.38, 99.79, 78.06, 98.97, and 92.58%, and the complete percentages of safety,
science and education, sports, and parks are 62.82, 99.59, 85.38, and 75.70%, respectively. Thus,
security facilities and parks need to be strengthened. In addition, according to the informationfrom websites such as Linkhome, the properties of residential communities in core areas areroughly divided into those with or without clear property companies. The proportion of property
companies with specific information is 60.66%. It is not clear if the property companies can bedivided into four categories, namely, the properties managed by units, the properties managed
by housing management offices, street offices (the properties managed by committees), and theproperties managed by owners, accounting for 13.18, 1.65, 8.44, and 16.07%, respectively.
4.3

Characteristics of populations

We analyzed the population of large residential areas (area > 1000 m2) having the same
population mobility characteristics. The spatial distribution map of residential districts is shownin Fig. 5. There are 296 residential districts, and they are distributed among 198 community
neighborhood committees, which reflect the characteristics of residential population attributes

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Fig. 5.

953(Color online) Spatial distribution map of residential districts.

in different districts. There is a relatively uniform distribution and sufficient number of theflowing population in these districts. The peak flowing population (20:00–23:00) has 40 more
people than the low flowing population (8:00–11:00), and the shortfall population between peakand low time is greater than 20% of the peak population.
4.3.1 Results of hourly characteristics
The hourly population fluctuations in a residential area were calculated from the passenger
flow data. All the recorded points in the period from 0:00 to 23:00 for each residential quarter ona given day were calculated and summarized, and the operations on the data over 13 days wererepeated to obtain the hourly population of the residential quarter during the study period. The
average passenger flow population at the corresponding time point is regarded as the hourly
characteristics of the average passenger flow in the residential area. The maximum population at
the time interval from 8:00 to 11:00 over the 13 days was regarded as the early peak population,and the maximum population at the average time interval from 20:00 to 23:00 over the 13 days
was considered as the late peak population.(21)As can be seen from Figs. 6–8, at 5:00 in the morning, residents gradually get up, whichcauses the increase in population, and the peak passenger flow population gradually appears
from 6:00 to 8:00. On working days, the population gradually falls back and fluctuates steadily.
After work, from 17:00 to 18:00, the population gradually increases, and a peak appears between
20:00 and 22:00. It can be seen from the change in hourly passenger flow population in the954

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Fig. 6.

Fig. 7.

(Color online) Hour by hour change in population flow in the residential district of Xin Feng Jie 1.

(Color online) Hour by hour change in population flow in the residential district of Zhong Xin Jia Yuan.

Fig. 8.

(Color online) Population of morning and evening peaks.

residential areas that there are two passenger flow population peaks in a day, which is in linewith the “double peak” feature of commuter travel on weekdays. In terms of population, theidentified number of passengers in the late peak is more than that in the morning peak. The timeSensors and Materials, Vol. 35, No. 3 (2023)

Table 3
Proportion of peak current population at time intervals.
Time interval
7:00
8:00
9:00
Morning peak
54.70%
32.21%
2.68%
18:00
19:00
20:00
Evening peak
7.38%
8.39%
12.75%

955

10:00
0.34%
21:00
58.39%

11:00
10.07%
22:00
12.75%

—
—
23:00
0.34%

points of the morning peak are relatively concentrated at 7:00 and 8:00. The passenger flow
growth rate fluctuates considerably, while the late peak time points are relatively scattered, withsmall changes and fluctuations in growth rate, mainly appearing at 20:00, 21:00, and 22:00.
More details are given in Table 3, the residential areas with morning peaks at 7:00 and 8:00
account for 54.70 and 32.21% of the population, whereas the residential areas with evening peaks
at 20:00, 21:00, and 22:00 account for 12.75, 58.39, and 12.75%, respectively.
4.3.2. Average characteristics
The analysis of the changes in passenger flow and population in the residential area duringcorresponding periods depicts the population flow of residents in the residential area on working
days. It can be seen from the daily hourly fluctuation of the passenger flow population that, after
8:00, the population gradually decreases because the residents go to work, and the size of thepassenger flow population gradually reaches its lowest value in the morning. In other periods,
the passenger flow population fluctuates slightly. After 20:00, with the residents returning
home, the amount of passenger flow gradually reaches a peak. We calculated the low value ofthe morning passenger flow population (8:00–11:00) and the peak value of the evening passenger
flow population (20:00–23:00) of the residential area during 13 working days to represent themobility of the population.
In general, as can be seen from Fig. 9, the average mobility of the passenger flow populationin all residential areas is about 30.89%. Specifically, residential areas with a passenger flow
population mobility of less than 30% account for 49.66% of the total number of residential areas,
residential areas with mobility between 30 and 40% account for 36.58% of the total number ofresidential areas, and residential areas with mobility greater than 40% account for 13.76% of thetotal number of residential areas. The mobility of passenger flow and population on weekdays
reflects the regular travel frequency of residents; the dynamic tracking and supervision ofresidential areas with high mobility should be strengthened.
4.3.3. Portrait characteristics
The five portrait labels are age range, income capacity, professional classification,
consumption level, and car use status. The current population portrait in the residential districtof Xin Feng Jie 1 is shown in Fig. 10.

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Fig. 9.

(Color online) Liquidity ranking chart of populations in residential areas.

(a)

(b)

(d)

(c)

(e)

Fig. 10. (Color online) Current population portrait in the residential district of Xin Feng Jie 1. (a) Age range. (b)Income capacity. (c) Professional classification. (d) Consumption level. (e) Car use status.

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4.3.3.1 Age portrait
As can be seen from Fig. 11, the ranges of 25–34 years, 35–44 years, and 45–54 years
constitute the main age ranges of population concentration. These three age ranges are the main
working groups. The sum of the estimated percentages of the group in the 25–54 year ranges andother age stages accounts for about 75 and 25% of the population of the communities,
respectively. The total estimated percentage of 25–54 years old exceeds 60% in 97.30% of theresidential areas.
The age above 55 is defined as the early aging period. In residential districts, the estimatedpercentage of the population having an average age between 55 and 64 and 65 or above is 5.48%.
The estimated percentage of the average age below 18 is 3.21%, and the estimated percentage ofthe population with ages above 55 is higher than that below 18 in 247 residential districts. The
top three residential communities with the highest percentages of the population over 55 years
old are No. 21 Yard A of Bai Wan Zhuang Street, Dong Lang Xia Hutong Community; No. 7
Yard A of Yue; and Tan North Small Street, accounting for 14.96, 14.18, and 12.87% of the
populations, respectively.
4.3.3.2 Income portrait
As can be seen from Fig. 12, the income profile of the passenger flow population is divided
into five levels: less than 2500 yuan, 2500–3999 yuan, 4000–7999 yuan, 8000–19999 yuan, andFig. 11. (Color online) Ranking of residential districts by percent of population in the 55 to 64 age group.

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Fig. 12. (Color online) Ranking of residential districts in terms of house price and income level (top 50 with RMB
20000 and above).

20000 yuan and above per month. The average estimated percentages of the profile of theresidential area for each income range are 15.51, 13.26, 20.96, 29.82, and 20.45%, respectively.
On the whole, the estimated percentages of people’s income levels are relatively balanced at a
middle-income level, and the estimated percentage of people with an income level of 8000–
19999 yuan is slightly higher. People with an income of less than 4000 yuan and more than 30%
in residential areas are estimated to be 38% of the population, and people with an income
between 4000 and 19999 yuan and more than 50% are estimated to be 57% of the population.
People with an income of more than or equal to 20000 yuan are estimated to be more than 30%of the population, accounting for 6.08% of the residential area.
Web crawler technology using a LinkedIn mobile app was used to obtain information on
house prices (August and September 2021) in the residential districts. A few missing pieces ofhouse price data were supplemented by An Ju Ke, I Love My Home, and other website
information. The comparison of the probable income levels with the average housing priceshows that the overall trend of housing price is higher or close to the trend in the income of thoseat 20000 yuan and above, indicating that the purchasing ability of people with an income level of20000 yuan and above is matched with the market price of housing.
4.3.3.3 Consumption portrait
The consumption level of the passenger flow population is divided into low, medium, andhigh, according to an average weekly consumption in the ranges of less than 110 yuan, 110–506

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Fig. 13. (Color online) Consumption ranking of residential districts (top 50 areas in terms of high consumption
level).

yuan, and more than 506 yuan. As can be seen from Fig. 13, the estimated percentages of thelow, medium, and high average consumption levels are 19.30, 28.75, and 51.95%, respectively.
The consumption portrait of the residential area reflects the differences between the low,
medium, and high consumption groups, and the overall consumption level is biased towards thehigh consumption trend. The costs of living and survival expenditures account for a large
proportion of consumption in Beijing.(22,23)
The estimated percentage of high consumption level, accounting for 66.21% of the residential
districts, is greater than the sum of the estimated percentages of medium and low consumption
levels. The top three residential districts with high consumption levels are the communities of
Zhong Xin Xi Yuan, Zhong Xin Jin Yuan, and Xi Fu Tao Yuan, with estimated percentages of
69.79, 67.00, and 66.54%, respectively.
4.3.3.4 Professional portrait
Six professional portraits were defined: production operators, civil servants, professional
technicians, managers and entrepreneurs, self-employed, and service personnel. As can be seenfrom Fig. 14, the average estimated percentages of the population in these categories are 1.60,
32.62, 46.48, 0.79, 5.64, and 12.87%, respectively. Civilians and professionals account for morethan 70% of the population in 99.66% of the residential areas.
As for the professional structure of the populations in residential areas, more attention shouldbe paid to service personnel and self-employed workers. The practitioners in these two
professional areas are highly mobile and have a relatively large number of contacts daily.

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Fig. 14. (Color online) Ranking of the top 50 districts in employment in the service industry and self-employed.

Daily management usually does not focus on service personnel and self-employed workers,
but these professionals will carry greater risks. From our analysis results, even though theservice industry and the self-employed do not account for much of the profession in theresidential area, they should be the focus of supervision. Understanding the professional portraitof the population in the residential areas has great significance for safety prevention, control, andissues of personnel management.
4.3.3.5 Car portrait
The problem of difficult parking is becoming increasingly prominent owing to theinsufficient construction of parking spaces in the past and accelerated urbanization. The demand
gap for parking was quantitatively analyzed using the car portrait of passenger flow and the
movement of the population.(24) The average estimated percentages of having a car or not are
22.12 and 77.88%, respectively.
The average estimated percentages of having car can be divided into three levels: 10–20%,
20–30%, and >30%. As can be seen from Fig. 15, 31.76% of the residential districts have cars in
the range of 10–20%, 64.19% of the residential districts have cars in the range of 20–30%, and4.05% of the residential districts have cars in the range of >30%. To suggest effective methods toease parking problems, it is necessary to determine the existing stock of parking spaces and theappropriate upper limit of possible capacity.
The research of Zhao et al.(13) involved a comparison between various districts and counties
in Beijing with respect to the assessment of public service facilities, and they concluded that thecore area was better than other districts on the whole. Our research has refined the scale of thisresearch, compared the difference in the level of public service facilities between communities
and residential districts in the core area, and expanded the analysis with respect to the commuter
population. Our results should help improve the accuracy of the management of these issues.

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Fig. 15. (Color online) Portrait of car ownership in the top 50 residential districts.

5.

Conclusions

Accurately estimating the demand for shared service facilities is the only way to insure theirdevelopment scientifically.(25) We measured public service facilities across two dimensions ofresidential space: the community and the residential district, which represents further explorationof how to optimize the configuration of facilities. In this study, data sources including
geographical data from remote sensing satellite sensors, POI, AOI, and Baidu Maps from mobile
phone sensors were used, and multi-source data process and analysis technologies were studied.
The use of multi-source big data diminishes the deviation between analysis using simulated data
and real demands for public services. The overall level of facilities in the core area of Beijing ishigh. Communities should strengthen the construction of park sports and other supporting
facilities to form diversified and independent living areas. The per capita supply capacity ofsafety facilities, such as shelters, should be improved to ensure a rapid response to various
emergencies. Residential districts should comprehensively consider the layout of safe sites andparks within the walking distance of the residential circle, improve the quality of property

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services, and create a multi-functional living environment. On the basis of passenger flow
population and passenger flow portrait data from Baidu Maps, the spatial characteristics of
population activities in the residential areas studied were enhanced using GIS spatial analysis
and quantitative analysis methods. In addition, the hourly fluctuation, average mobility, andpopulation attributes of individuals in residential areas during working days were explored, andthe spatial structure and characteristics of the population were also analyzed. In this study, we
provide methods of studying the characteristics of the structure within small-scale urban
populations and evaluating the quality of urban residential spaces. The results provide useful
information for the planning of urban public service facilities.
We evaluated communities and residential districts, but we did not carry out thecomprehensive ranking of communities and residential districts because comprehensive andscientific scoring standards require long time periods for their demonstration. In future
research, data from the communities will be further refined and analyzed, the satisfaction withand gaps in elderly care facilities will be probed, and electric bicycle charging piles, culturalactivity centers, and community service centers will be analyzed by considering therequirements of a 15 min walking circle and the impact of the construction of digitalcommunities.

Acknowledgments
This study was funded by Beijing Key Laboratory of Urban Spatial Information Engineering,No. 2020204.

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About the Authors		Zeyu Li received her master’s degree from Liaoning Technical University,
China, in 2019. From 2017 to 2019, she studied at the Chinese Academy ofSurveying and Mapping. Since 2019, she has been working at the Beijing
Institute of Survey and Mapping, and since 2021, she has been a middle-levelengineer. Her research interests are in urban spatial analysis.
(1479781035@qq.com)
Cai Cai received her B.S. degree from Wuhan University in 2010 and her
Ph.D. degree from Peking University, China, in 2016. From 2014 to 2015, she
was a research scholar at Columbia University, USA. Since 2016, she has beenworking at the Beijing Institute of Survey and Mapping and as a researcher at
the Beijing Key Laboratory of Urban Spatial Information Engineering, where
she has been a senior engineer since 2018. Her research interests are in urban
spatial analysis and urban remote sensing. (caicai361448336@126.com)
Yingchun Tao received her B.S. degree in 2004, her M.S. degree in 2007, and
her Ph.D. degree in 2019 from Peking University, China. Since 2007, she has
been working at the Beijing Institute of Surveying and Mapping and as a
researcher at the Beijing Key Laboratory of Urban Spatial Information
Engineering, where she has been a professor-level senior engineer since 2018.
Her research interest is in urban spatial analysis. (taoyc@bism.cn)