Performance Analysis of Smart City Governance: Dynamic Impact of Beijing 12345 Hotline on Urban Public Problems

Abstract

The 12345 hotline service has become a widely accepted smart city governance system of citizen contact with the local authorities in Beijing and has enabled leaders to handle public complaints more effectively. However, there have been few studies on the performance of the 12345 hotline. This study, taking Beijing city as the study area, explains how this system improved urban problem management and the degree of improvement in different urban problem categories. First, we studied the quantity distribution of 12345 cases and the public’s top concerns. Then, based on the VAR model, we analyzed the specific dynamic influence process and degree of 12345 cases on UGMS cases, which reflect urban public problems. The main findings of this research are as follows: (1) Illegal parking/charging problems and issues with the city’s dirty conditions were the two primary concerns of the public. (2) The 12345 system hampered the increase in urban public problems with a lag of 3–5 days. (3) Exceptionally, for the construction management and market regulation categories, the hotline cases had no active effect on urban problem management. This paper can help city authorities to assess the strength and weakness of the 12345 system and decide on improvement plans for urban management.

1. Introduction

In recent decades, the rapid development of urbanization in China has led to increasing urban problems [1], e.g., high population density, traffic congestion, and severe environmental pollution. This has stimulated the public’s discontent and complaints and placed significant pressure on local authorities. Especially in a megacity such as Beijing, the capital of China, a high-density population is an extra challenge to effective urban management, and local citizens have higher requirements for city service. To alleviate these urban problems, in the last decade, a number of smart city initiatives have flourished around the country for more efficient and effective governance of cities [2]. Many cities have implemented and greatly expanded easy-access smart systems for citizens to contact government and report problems with infrastructure and city services. The most prominent of these programs in Beijing is the 12345 hotline service which allows city residents to report problems by calling the number or making an online submission [3]. Similar to 3-1-1 in the U.S. and Canada, 1-2-0 in Korea, and 1-1-5 in Germany [4,5,6], the 12345 hotline service is a part of China’s efforts to streamline administration and optimize non-emergency services in public service areas such as market regulation, social work, and ecological and environmental protection.

The 12345 hotline system is a governmental innovation in China for smart city governance that includes the participation of citizens. The hotline can be contacted at any time for any urban non-emergency service by calling the number or making an online submission. It takes an intelligent approach to respond to residents’ concerns more accurately and rapidly [7]. Based on this smart system, the local government receives requests and complaints encountered by city residents by offering a 24-h government-run public service. At the same time, these requests should be handled and given feedback under supervision according to the classification and jurisdiction of the affairs. Relying on artificial intelligence technology, the system collects millions of case data corresponding to problems and resolution processes. These data offer the opportunity to paint a comprehensive picture of city problems and the potential to improve the urban governance level [8].

Based on public service hotline data, there have been several studies on the distribution of and driving forces behind urban problems within a city [9,10,11,12]. Peng et al. [13] analyzed the problem distribution of urban public management and proposed several pathways to optimize urban governance based on the data collected from the 12345 citizen service hotline in Sanya city, Hainan province. Minkoff [14] examined census-tract-level social, economic, physical, and political attributes to explain the spatial distribution of 311 contacting volumes within New York City. Wang et al. [15] classified urban locations based on the spatial and temporal distribution of 311 service requests to predict socioeconomic and demographic characteristics of a neighborhood. However, little attention has been given to investigations of whether and how this system acts upon urban problem management. Little is known about the performance of this system from the lens of urban governance.

Some scholars abroad have already studied the effects of public service hotlines in other countries and concluded that in many nations, these services are recognized as a crucial tool to strengthen citywide performance management efforts. O’Brien et al. [16] pointed out the American 311 system was used in conjunction with departmental performance to track, evaluate, and correct service patterns in public departments. Taewoo and Theresa [17] implied that the culture of holistic accountability enhanced by the 311 system helped these departments manage urban problems more effectively. However, there is still no specific assessment of these systems in urban governance based on objective data analysis. Especially in China, the degree of impact of the 12345 hotline systems on urban problem management is unknown.

In this paper, relying on data from the Beijing 12345 hotline service, we analyzed the quantity distribution of urban management problems extracted from 12345 calls, and for the first time, we used the VAR model to assess the performance of this system from the lens of urban problem management. By estimating the dynamic effect of this system on the alleviating of urban problems, we explored the improvement degree of urban management in different periods and categories. The outcomes of our study can help city authorities to draw out the strength and weakness of the 12345 system and decide on improvement plans for urban management.

2. Materials and Methods

2.1. Site Description

Beijing, the capital of China, is the second-largest city in the country and the center of politics, culture, science, technology innovation, and international communication. Beijing is located in the North China Plain and is formed by 16 districts (refer to Figure 1). The east districts are adjacent to Tianjin; the rest are adjacent to Hebei. With an area of 16,410 square kilometers and a permanent population of 21.5 million inhabitants (as of 2019), the city has 18.6 million urban dwellers and an urbanization rate of 86.6% [18].

Beijing is a world-class megacity, and it faces many challenges regarding its city services and neighborhood conditions. The 12345 hotline service function was launched to gather the public’s complaints and address the issues they may have with urban public service, aiming to optimize government service. Beijing is focused on people-centered initiatives and is investing in plans of action to shape itself into a harmonious first-tier international capital.

2.2. Data Sources

This study employed two types of data: case data from the 12345 hotline service and urban public problem data collected from the Urban Grid Management System (UGMS). The data considered were collected from the Research Institute of Beijing City Management, a subordinate organ of the Municipal Commission of Beijing City Management.

• (1). 12345 data

12345 service request and complaint data cover 16 districts in Beijing and were collected through multiple sources, including phone calls, text messages, and web pages. These 12345 service requests and complaints cover a wide range of public concerns [8]. Thus, these data serve as a useful resource for understanding the delivery of critical city services and neighborhood conditions. In this study, we specifically used 12345 data to verify the performance management effect of the hotline system.

We considered a time frame between July and September 2019, during which the data are relatively stable and less affected by seasons. The data contain 220,000 records, one record for each call. These records carry different information, e.g., service request content and resolution, request type, service request open/close time, and date and location. With that information, we could group the data for any given period and area.

• (2). UGMS data

The Urban Grid Management System (UGMS) is a platform used by public authorities to uncover urban issues in Beijing. In this platform, the entire city is divided into grids according to definite rules, and each grid is inspected by a designated grid supervisor. Every urban problem tracked by the grid supervisor of the area is recorded as a case in the Urban Grid Management System [19,20], which is distributed to the corresponding management department to dispose with feedback. In turn, these data are used to guide supervisors in carrying out the key work of daily inspection. In this study, we use these data to verify the impact of the 12345 hotline system on city management.

UGMS can record information on the time and place of the episode, classify the problem, store a detailed description of it, and provide potential treatment options. This study is based on UGMS data collected in 16 districts from July to September 2019. More than 2 million cases were recorded during this period.

2.3. Research Methods

2.3.1. Classification System of 12345 Cases

To obtain initial insights into the usage of 12345 services in Beijing, the events or problems reported to the hotline were classified into different categories or classes. The categories were pre-defined by specialists when the 12345 system was launched. Over time, more new categories were added along with the development of the channel. According to the latest definitions and scopes, we divided the cases associated with the condition of urban management into seven types: (i) municipal facilities; (ii) city appearance and sanitation service; (iii) urban landscaping; (iv) atmospheric pollution; (v) traffic conditions; (vi) construction management; and (vii) market regulation. Details of this classification system are shown below in Table 1:

2.3.2. Variable Selection Strategy

The daily counts of UGMS cases were defined as dependent variables for training the VAR model to estimate dynamic relationships. The daily counts of 12345 cases were defined as independent variables.

The natural logarithm transformation was applied to transform daily UGMS cases and 12345 cases throughout the whole process. We chose this methodology because the natural logarithm can better reflect the long-term trend of things, and its results will not change the relationships among the original data. In addition, it can linearize the variation trend and eliminate heteroscedasticity [21].

After log transformation, H1 and G1 represented the daily general 12345 cases and available UGMS cases. Furthermore, H2–H8 and G2–G8 describe the daily categorical cases of 12345 patients and UGMS cases. The specific indicators are shown in Table 2:

2.3.3. Dynamic Relationship Analysis Based on the VAR Model

The VAR model is often used to predict interrelated time-series variables and analyze the impact effect of random disturbance factors on the system and is the most common model used to examine and predict multiple related social variables [22,23,24,25]. The modeling concept of the VAR model is based on a regression model that considers each endogenous variable in the system as a function of all variable lag terms [26]. In our research, the VAR model was used to analyze the dynamic impact process of 12345 cases on UGMS cases. The specific steps of our methodology are listed below:

• (1). Stationarity test

The premise of the VAR model is that each variable is stationary or co-integrated. Therefore, we performed the stationarity test of all parameters. This method was applied to test whether the properties of time-series data change with time. If the stationarity test is successful, these properties remain unchanged, and vice versa. Furthermore, the unit root method is usually applied as part of this process [27], and the ADF test is the most common unit root test. In this study, we used the ADF unit root method to test the stationarity of the quantity series of 12345 cases and UGMS cases. The formula of the ADF test model is as follows:

(1)$\mathrm{Z}\Delta y_t=c+\rho y_{t-1}+{\displaystyle \sum }_{i=1}^p{\varnothing }_i\Delta y_{t-\left(i-1\right)}+{\epsilon }_t$

where $t$ is a time variable, $c$. is a constant term, and ${\epsilon }_t$. is the error term. When $\rho$ = 0, it is a non-stationary series; otherwise, it is a stationary series.

• (2). VAR model building

The VAR model investigates the dynamic interaction among multiple variables and constructs a regression model by taking each endogenous variable in the system as a function of all variable lag terms [21]. Therefore, setting the lag order is crucial for the progress of the model building. The most popular method to choose the lag order is to use information criteria. In this context, an information criterion is designed to consistently find the model that better fits the data from a group of models. The decision about how many lag orders are to be included in the regression depends on the model selection criterion, which is determined by minimizing the Schwartz Bayesian Information Criterion (BIC) or the Akaike Information Criterion (AIC) or dropping lags until one is considered statistically significant [28,29].

The formula of the VAR model is as follows:

(2)$Y_t=A_1{Y}_{t-1}+A_2{Y}_{t-2}+L+A_p{Y}_{t-p}+{\epsilon }_t.$

where Y is the vector of the K-dimensional endogenous variable, A is the coefficient matrix, and P is the order of lag of the endogenous variable.

• (3). VAR model verification

After constructing the VAR model, we also needed to test its stability. The test principle of the stability of the VAR model is that the absolute value of all AR roots is less than 1. AR roots graph is a common method to test the stability of the VAR model established. According to the method, the estimated VAR is stable if all roots have a modulus less than one and lie inside the unit circle. If the VAR is not stable, certain results (such as impulse response standard errors) are not valid. There will be roots, where the number of endogenous variables is the largest lag. If one estimates a VEC with cointegrating relations, roots should be equal to unity [30].

• (4). Impulse response analysis and variance decomposition analysis

Based on the VAR model, impulse response and variance decomposition graphs can be outlined to analyze the influence process and contribution level among multiple variables. The impulse response graph reflects the model’s dynamic characteristics, such as the specific influence process and direction of one variable on another. Differently, variance decomposition is used to further analyze the contribution and importance of each structural impact to the fluctuation of variables.

3. Results

3.1. Case Distribution based on 12345 Service Categories

The number distribution of categorical cases was derived (see Table 3) based on the classification system of 12345 cases (see Table 3), which can characterize the unique way people use the hotline and indicate which particular concerns are the most popular among Beijing citizens. The counts of seven classification factors were given in the three months covered by the research database. Further counts were tabulated separately for the 37 case types within seven classification factors. The classification factors, constituent types, and counts are listed below in Table 3:

As illustrated above, most of the citizens complained about issues with traffic conditions and city appearance and sanitation services, with rates of 25.25% and 22.13%, which were significantly higher than other categories, followed by market regulation and municipal facilities reaching 22,313 and 19,391 complaints, respectively. Among all the categories, illegal parking or charging and dirty conditions were the most prominent, with more than 15,000 registered complaints.

3.2. Extraction of Model Variables

The time-series variables of daily UGMS and 12345 cases (within the pre-established period) were counted, and the natural logarithm was transformed (see Figure 2) for training the VAR model to estimate the dynamic relationship between the different group cases. Figure 2a represents the daily general 12345 cases and UGMS case series, and Figure 2b–h represent the daily categorical cases for both the 12345 hotline and UGMS.

As shown in Figure 2, the daily general counts of the 12345 and UGMS case series had regular fluctuations, but their wave directions remained consistent. The daily categorical counts of the 12345 and UGMS case series, except for construction management, also had highly consistent volatility. The results indicate that there was a specific correlation between them.

3.3. Dynamic Impact Analysis on Urban Management Performance

3.3.1. Stationarity Test

As shown in Table 4, the ADF test method was used to test the stationarity of the quantity series of 12345 and UGMS cases. The 1% critical values of time-series data were more significant than their ADF statistical values, indicating that all the null hypotheses were rejected at a substantial level of 1%. Therefore, the data series are stationary, so the general and categorical data of 12345 and UGMS cases could be directly used in the VAR model analysis.

3.3.2. Building a VAR model

Based on time-series data of 12345 daily UGMS cases, VAR models were established with BGMS cases as dependent variables. Eviews software (version 14.0) was used in the modeling process, and the lag order was set to 2. The VAR models created for general cases and categorical cases are listed below:

VAR model of general cases:

(3)$G1=-0.3744\times H1\left(-1\right)+0.6522\times H1\left(-2\right)+0.5382\times G1\left(-1\right)-0.5469\times G1\left(-2\right)+7.9257$

VAR model of municipal facilities:

(4)$G2=0.0007\times H2\left(-1\right)+0.0646\times H2\left(-2\right)+0.4308\times G2\left(-1\right)-0.3353\times G2\left(-2\right)+5.0797$

VAR model of sanitation services and disposal of wastes:

(5)$G3=-0.0005\times H3\left(-1\right)+0.2567\times H3\left(-2\right)+0.4253\times G3\left(-1\right)-0.4812\times G3\left(-2\right)+8.3927$

VAR model of urban landscaping:

(6)$G4=-0.3179\times H4\left(-1\right)-0.0205\times H4\left(-2\right)+0.5051\times G4\left(-1\right)-0.3276\times G4\left(-2\right)+5.5724$

VAR model of urban environment:

(7)$G5=0.1471\times H5\left(-1\right)+0.1735\times H5\left(-2\right)+0.2870\times G5\left(-1\right)-0.2782\times G5\left(-2\right)+3.1362$

VAR model of traffic conditions:

(8)$G6=-0.4981\times H6\left(-1\right)+0.3170\times H6\left(-2\right)+0.5937\times G6\left(-1\right)-0.3133\times G6\left(-2\right)+6.4971$

VAR model of construction management

(9)$G7=-0.0006\times H7\left(-1\right)+0.3352\times H7\left(-2\right)+0.4411\times G7\left(-1\right)-0.3781\times G7\left(-2\right)+4.2408$

VAR model of market regulation

(10)$G8=-0.0803\times H8\left(-1\right)+0.3221\times H8\left(-2\right)+0.3806\times G8\left(-1\right)-0.3002\times G8\left(-2\right)+6.3658$

The stability of the above VAR models was tested by using Eviews 14.0 software to draw the AR roots graph (refer to Figure 3). As illustrated, the modules of the reciprocal of AR characteristic roots are all in the unit circle, indicating that the VAR models are stable. Once that was confirmed, the impulse response and variance decomposition analyses could be performed.

3.3.3. Impulse Response Analysis

The corresponding impulse response graphs (see Figure 4) were obtained via Eviews and show the dynamic impact progress of 12345 cases on UGMS cases. In the figures, the vertical axis represents the response degree of UGMS cases, and the horizontal axis represents the lag period of the impact effect of 12345 cases. The solid line is the impulse response degree, and the dotted line is the deviation degree of two positive and negative standard deviations. We selected the 10-period lag to observe the degree of influence between variables.

As illustrated in Figure 4, the impact effect of 12345 general cases on UGMS general cases is consistent with that of most categorical cases except for the construction management and market regulation cases. On the first day, the impact is positive, but it gradually weakens and turns negative from the third day. The impact is still negative from the fourth day until the fifth period, where the impact turns positive again and gradually stabilizes. This shows that 12345 cases prevented the increase in UGMS cases with a lag of 3 to 5 days, indicating the 12345 service hotline has an inhibition impact on urban public problems.

However, for the construction management and market regulation categories, 12345 cases could not prevent the increase in UGMS cases (see Figure 4g–h). As shown in Figure 4g, the impact effect of 12345 cases on UGMS cases is sustainedly positive, indicating that the hotline construction management cases follow a consistent, increasing trend with UGMS construction management cases on the same day and with a lag of several days. As shown in Figure 4h, the impact is first positive and then gradually weakens until it disappears and stabilizes. This dynamic indicates that 12345 market regulation cases have no inhibition impact on the UGMS market regulation cases.

3.3.4. Variance Decomposition Analysis

It can be seen from Figure 5 that the degree of influence of 12345 general cases on UGMS general cases is at a high level, ranging between 50% and 60% in each period. Among all the categories of cases, city appearance, sanitation services, and market regulation have the highest influence degree at a rate of 60%. The next items on the list are atmospheric pollution and traffic condition, with influence rates ranging between 40 and 50%. Municipal facilities and urban landscaping have a lower degree of influence, i.e., below 40%. We also noticed that construction management has little influence on the context overall, with a degree that almost reached zero.

3.3.5. Comprehensive Analysis on the Influence

Based on the impulse response and variance decomposition analyses conducted above, we concluded that the 12345 service hotline generally prevented the increase in urban public problems with a lag of 3–5 days. Compared with the influence of UGMS cases, the dynamic influence of 12345 cases on UGMS cases in different periods plays a dominant role, with an influencing degree ranging between 50% and 60% in different periods. Among all the categories of cases, except for the construction management and market regulation categories, 12345 categorical cases correspondingly prevented the increase in urban different category issues with a lag of 3–5 days. However, 12345 categorical cases have different dynamic influences on UGMS categorical cases. ‘City appearance and sanitation services’ has the highest influence degree, which is maintained at approximately 60%. Municipal facilities has the lowest degree of influence, which is maintained at 20%.

Compared with UGMS’s construction management cases, 12345’s ‘construction management’ category, instead of inhibition impact, exhibited a consistent increased trend with a lag of several days. However, its positive influence was very weak, and its degree almost reached zero.

Regarding the market regulation category, 12345 cases have no inhibition impact on the UGMS market regulation cases either. Compared with the influence of UGMS cases, the dynamic influence of 12345 cases on UGMS cases plays a dominant role, with a stable degree maintained at 60% (see Figure 5h). On the other hand, we did not detect an inhibition impact in this context, and the impact gradually disappeared with a lag of the following days (see Figure 4h).

4. Discussion

According to the quantity distribution analysis of 12345 cases, we concluded that citizens in Beijing mostly complain about ‘traffic conditions’ and ‘city appearance and sanitation services’. These two items were the top two on the list of seven categorical urban public issues. Compared with previous studies, this outcome is almost consistent with that of Peng et al., who found that traffic conditions, waste and waste bins, and construction management were the main urban problems by analyzing 12345 data in Sanya city, Hainan Province. However, it is different from the outcomes of studies conducted abroad. Minkoff implied that street infrastructure condition was the most serious problem in New York City, while few traffic condition problems were complained about by the public [14]. In Beijing, even though several regulations have been formulated to punish behaviors such as illegal parking and littering, which cause traffic conditions and city appearance problems, these phenomena cannot be prevented. The possible reason for this is lack of awareness of legal obligations with respect to the correct behavior.

In this paper, the dynamic effect of the 12345 hotline service on alleviating urban problems was analyzed based on objective data. Compared with previous studies using questionnaires and interview methods [17], this research considered a more objective statistical method. Based on the VAR model, the 12345 hotline service is demonstrated to be an effective new measure of political participation to alleviate these urban public problems. Our findings are in accordance with those of Taewoo and Theresa [17], who pointed out that the culture of holistic accountability enhanced by the American 311 service helped the departments manage city problems more efficiently. However, they did not demonstrate this effect based on objective data, and the impact level of this system was unknown. In our study, the measurable effect of the 12345 hotline service was assessed. Except for construction management and market regulation categories, most urban public problems were alleviated with a lag of 3–5 days, and the influence rate of the 12345 hotline service remained high. This result may be explained by the fact that based on the smart operation process of the system, many 12345 cases have been resolved, and most residents received feedback during the days following their complaints. Subsequently, the corresponding urban public problems were ameliorated, which improved the urban management level.

Even though the 12345 hotline service generally exerted its dominant, active inhibition effect on urban public problems, for some categorical issues, the influence of this service was not so significant. In categories such as ‘municipal facility’ and ‘urban landscaping’, the influence rate of the 12345 hotline service remained below 40%, since the public is not particularly concerned with these aspects. This is contrary to the previous study from O’Brien et al. [16] and Minkoff et al. [17], who showed that the public is seriously concerned about municipal facility problems in New York City.

The 12345 hotline did not truly impact construction management and market regulation, and it is likely that public concerns regarding these two aspects of the city have not been effectively resolved by the implementation of the 12345 system. According to Table 3, the public registered many complaints about construction noises, which is also the main public concern in many cities both at home and abroad, according to Peng [13] and O’Brien [16]. In Beijing, this issue often occurs at night, so it is difficult for the government to manage. In the market regulation category, unlicensed peddlers and street vendors were two dominant problems. These are also primary issues for urban managers due to vendors’ noncooperation and mobility in different places. This is probably due to the lack of strict policy constraints. Local authorities should launch stricter policies and fine systems to force the people to obey the rule.

Our methodology successfully explored the role of the 12345 hotline service in urban management performance. However, there is still a lot of room for improvement in the analysis. We only selected a representative period of data for analysis, and we would be able to draw more comparative conclusions if we could extend the scope of time. Future studies can also consider geospatial distribution to examine spatial development patterns and trends related to the 12345 hotline, shedding light on more possibilities to provide direct guidance for hotline managers.

5. Conclusions

In this study, we analyzed the categorical distribution of 12345 cases registered in Beijing city and further analyzed the potential active effect of the 12345 hotline system on urban management performance. The findings suggest several courses of action for urban management improvement.

First, the local government should pay more attention to the urban problems of parking/charging issues and dirty conditions, which are top public concerns. Even though this study demonstrated that the 12345 hotline platform could help alleviate these problems, its effectiveness was delayed by 3–5 days. Local authorities should provide more efficient management strategies. Sufficient parking spaces should be provided in conjunction with stricter management.

Second, for less concerning problems, including municipal facilities and urban landscaping issues, whose fluctuations were influenced by the 12345 hotline complaints to a low degree, the government should assume the main responsibility. Government procurement can be achieved by paying service providers for cleaning and repairing in public places.

Third, for the urban issues of construction management and market regulation, which could not be actively improved by the 12345 hotline system due to a lack of effective treatment, even though they were public concerns, more stringent policies and fine systems should be considered to force the people to obey the rules. Greater publicity is required to increase residents’ awareness of their obligations with respect to the regulations.

Author Contributions

Conceptualization, J.W. and Z.F.; methodology, N.X., H.Z., H.L. and R.Y.; formal analysis, N.X., H.Z., H.L. and R.Y.; investigation, N.X., H.Z., H.L. and R.Y.; writing—original draft preparation, N.X.; writing—review and editing, N.X., H.Z., H.L., J.W., Z.F. and R.Y.; visualization, N.X., H.Z., H.L., J.W., Z.F. and R.Y.; supervision, J.W. and Z.F.; funding acquisition, N.X. and J.W. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Acknowledgments

We are grateful to the staff of the Beijing City Management Municipal Institute and the teachers at the Laboratory of Forest Management and “3S” technology, Beijing Forestry University.

Conflicts of Interest

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures and Tables

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Figure 1. Location of Beijing Province with 16 districts. * Sixteen districts from 1 to 16 are Yanqing, Huairou, Miyun, Changping, Shunyi, Pinggu, Mengtougou, Shijingshan, Haidian, Chaoyang, Xicheng, Dongcheng, Fengtai, Fangshan, Daxing, and Tongzhou.

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Figure 2. Time-series variables of daily UGMS cases and 12345 cases.

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Figure 3. Stability test of VAR models.

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Figure 3. Stability test of VAR models.

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Figure 4. Impulse response graph of 12345 cases on UGMS cases.

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Figure 5. Variance decomposition graph of 12345 cases on UGMS cases.

Table 1

Classification system of 12345 cases.

Category	Content
Municipal facilities	Including five items related to the damage of facilities under the ownership or control of the Beijing government.
City appearance and sanitation service	Including five items related to incivilities regarding trash disposal and environmental damage.
Urban landscaping	Including two items related to damaged or dirty conditions in public gardens and parks.
Atmospheric pollution	Including two items related to the release of pollutants into the air, which is detrimental to human health.
Traffic condition	Including five items related to illegal behaviors or damaged facilities that cause traffic issues.
Construction management	Including five items related to unsafe or damaged conditions due to the construction project.
Market regulation	Including five items related to lack of market restrictions or legal adjustments along streets.

Table 2

Selected analysis indicators.

Relationship Analysis	Daily 12345 Cases	Daily UGMS Cases
General cases	H1	G1
Municipal facilities	H2	G2
City appearance and sanitation service	H3	G3
Urban landscaping	H4	G4
Atmospheric pollution	H5	G5
Traffic condition	H6	G6
Construction management	H7	G7
Market regulation	H8	G8

Table 3

Case distribution based on 12345 service categories.

Num	Category	Category Proportion/%	Case Type	Case Type Proportion/%
1	Traffic condition	25.25	Illegal parking/charging	17.74
			Traffic signal condition	5.02
			Illegal operation	2.08
			Blocked driveway	0.41
2	City appearance and sanitation service	22.13	Dirty condition	13.46
3	Market regulation	18.79	Trash barrel condition	5.05
			Illegal outdoor advertising	1.91
			Sanitation condition	1.16
			Lighting	0.35
			Street sign condition	0.19
			Street vendor	6.81
			Unlicensed peddler	6.73
			Poor market condition	3.62
			Building use	0.87
			Commercial network request	0.44
			Others	0.31
4	Municipal facilities	16.55	Street condition	6.52
			Manhole cover condition	4.21
			Street light condition	3.61
			Power pole condition	0.93
			Plumbing	0.48
			Bridge condition	0.35
			Facility maintenance	0.32
			Underground tunnel condition	0.14
5	Construction management	8.91	Noise	4.75
			Safety condition	1.81
			Road occupation	0.8
			Others	0.66
			Derelict material	0.45
			Construction dust	0.44
6	Urban landscaping	6.95	Damaged tree	4.84
			Garden condition	2.11
7	Atmospheric pollution	1.42	Pollution discharge	0.33
			Smoke emission	0.32
			Dust emission	0.3
			Open barbecue	0.19
			Industrial waste	0.14
			Waste incineration	0.09
			Automobile exhaust	0.02
			Coal combustion fly ash	0.02

Table 4

ADF Unit Root Test for the data stationarity of 12345 cases and UGMS cases.

Variable	Order	ADF Statistical Value	1% Critical Value	5% Critical Value	10% Critical Value	Conclusion
G1	0	−7.8328	−3.5047	−2.8940	−2.5841	Stationary
H1	0	−6.6785	−3.5047	−2.8940	−2.5841	Stationary
G2	0	−7.4511	−3.5047	−2.8940	−2.5841	Stationary
H2	0	−6.7428	−3.5039	−2.8936	−2.5839	Stationary
G3	0	−8.2442	−3.5047	−2.8940	−2.5841	Stationary
H3	0	−7.2523	−3.5047	−2.8940	−2.5841	Stationary
G4	0	−7.9736	−3.5047	−2.8940	−2.5841	Stationary
H4	0	−7.4492	−3.5039	−2.8936	−2.5839	Stationary
G5	0	−7.0001	−3.5039	−2.8936	−2.5839	Stationary
H5	0	−7.4053	−3.5039	−2.8936	−2.5839	Stationary
G6	0	−6.6962	−3.5047	−2.8940	−2.5841	Stationary
H6	0	−5.9805	−3.5039	−2.8936	−2.5839	Stationary
G7	0	−7.8991	−3.5047	−2.8940	−2.5841	Stationary
H7	0	−6.6987	−3.5056	−2.8943	−2.5843	Stationary
G8	0	−6.9527	−3.5039	−2.8936	−2.5839	Stationary
H8	0	−6.5224	−3.5039	−2.8936	−2.5839	Stationary

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