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As air pollution becomes more serious in China, it is critical to study its influencing factors for targeted environmental governance. Existing researchers have conducted various studies on the factors of PM2.5 (fine particulate matter with a diameter less than or equal to 2.5 microns) pollution. However, these studies mostly conducted analyses focused on macroscopic factors and lacked an impact analysis of specific entity distributions on PM2.5 pollution. Furthermore, most existing studies used ordinary regression models that ignored the spatial heterogeneity of influence of various factors on pollution, leading to biased results. To address these issues, focusing on air quality in heavily polluted city (Weifang City), this study aims to measure the influence of spatial differentiation of the impacts of four roughly classified POIs, namely, industry, restaurants, scenic spots, and parking lots, on PM2.5 pollution quantitatively by using a geographically weighted regression (GWR) model (spatial varying-coefficient regression model). The results indicate that the spatial distribution of effects of industry, restaurant and parking lot POIs on PM2.5 concentrations in Weifang are similar. The impact of all four POIs on PM2.5 is spatially nonstationary and have certain spatial trends, parking lots have the greatest influence on PM2.5 pollution. Based on the findings, pollution prevention and control measures are suggested to be designed based on the actual situation. For instance, some counties in Weifang should be encouraged to develop tertiary industries dominated by tourism. This research investigated the spatial impacts of the specific entity distributions on air pollution and provided targeted advice based on findings, which can contribute to policy-making aimed at air pollution mitigation.
Chengming Li; Yuxue Zou; Zhaoxin Dai; Jie Yin; Zheng Wu; Zhaoting Ma. The Impacts of POI Data on PM2.5: A Case Study of Weifang City in China. Applied Spatial Analysis and Policy 2021, 1 -20.
AMA StyleChengming Li, Yuxue Zou, Zhaoxin Dai, Jie Yin, Zheng Wu, Zhaoting Ma. The Impacts of POI Data on PM2.5: A Case Study of Weifang City in China. Applied Spatial Analysis and Policy. 2021; ():1-20.
Chicago/Turabian StyleChengming Li; Yuxue Zou; Zhaoxin Dai; Jie Yin; Zheng Wu; Zhaoting Ma. 2021. "The Impacts of POI Data on PM2.5: A Case Study of Weifang City in China." Applied Spatial Analysis and Policy , no. : 1-20.
Urban built-up areas, where urbanization process takes place, represent well-developed areas in a city. The accurate and timely extraction of urban built-up areas has a fundamental role in the comprehension and management of urbanization dynamics. Urban built-up areas are not only a reflection of urban expansion but also the main space carrier of social activities. Recent research has attempted to integrate the social factor to improve the extraction accuracy. However, the existing extraction methods based on nighttime light data only focus on the integration of a single factor, such as points of interest or road networks, which leads to weak constraint and low accuracy. To address this issue, a new index-based methodology for urban built-up area extraction that fuses nighttime light data with multisource big data is proposed in this paper. The proposed index, while being conceptually simple and computationally inexpensive, can extract the built-up areas efficiently. First, a new index-based methodology, which integrates nighttime light data with points-of-interest, road networks, and the enhanced vegetation index, was constructed. Then, based on the proposed new index and the reference urban built-up data area, urban built-up area extraction was performed based on the dynamic threshold dichotomy method. Finally, the proposed method was validated based on actual data in a city. The experimental results indicate that the proposed index has high accuracy (recall, precision and F1 score) and applicability for urban built-up area boundary extraction. Moreover, this paper discussed different existing urban area extraction methods, and provides an insight into the appropriate approaches selection for further urban built-up area extraction in cities with different conditions.
Chengming Li; Xiaoyan Wang; Zheng Wu; Zhaoxin Dai; Jie Yin; Chengcheng Zhang. An Improved Method for Urban Built-Up Area Extraction Supported by Multi-Source Data. Sustainability 2021, 13, 5042 .
AMA StyleChengming Li, Xiaoyan Wang, Zheng Wu, Zhaoxin Dai, Jie Yin, Chengcheng Zhang. An Improved Method for Urban Built-Up Area Extraction Supported by Multi-Source Data. Sustainability. 2021; 13 (9):5042.
Chicago/Turabian StyleChengming Li; Xiaoyan Wang; Zheng Wu; Zhaoxin Dai; Jie Yin; Chengcheng Zhang. 2021. "An Improved Method for Urban Built-Up Area Extraction Supported by Multi-Source Data." Sustainability 13, no. 9: 5042.
The COVID-19 pandemic is a major problem facing humanity throughout the world. The rapid and accurate tracking of population flows may therefore be epidemiologically informative. This paper adopts a massive amount of daily population flow data (from January 10 to March 15, 2020) for China obtained from the Baidu Migration platform to analyze the changes of the spatiotemporal patterns and network characteristics in population flow during the pre-outbreak period, outbreak period, and post-peak period. The results show that (1) for temporal characteristics of population flow, the total population flow varies greatly between the three periods, with an overall trend of the pre-outbreak period flow > the post-peak period flow > the outbreak period flow. Impacted by the lockdown measures, the population flow in various provinces plunged drastically and remained low until the post-peak period, at which time it gradually increased. (2) For the spatial pattern, the pattern of population flow is divided by the geographic demarcation line known as the Hu (Heihe-Tengchong) Line, with a high-density interconnected network in the southeast half and a low-density serial-connection network in the northwest half. During the outbreak period, Wuhan city appeared as a hollow region in the population flow network; during the post-peak period, the population flow increased gradually, but it was mainly focused on intra-provincial flow. (3) For the network characteristic changes, during the outbreak period, the gap in the network status between cities at different administrative levels narrowed significantly. Thus, the feasibility of Baidu migration data, comparison with non-epidemic periods, and optimal implications are discussed. This paper mainly described the difference and specific information under non-normal situation compared with existing results under a normal situation, and analyzed the impact mechanism, which can provide a reference for local governments to make policy recommendations for economic recovery in the future under the epidemic period.
Chengming Li; Zheng Wu; Lining Zhu; Li Liu; Chengcheng Zhang. Changes of Spatiotemporal Pattern and Network Characteristic in Population Flow under COVID-19 Epidemic. ISPRS International Journal of Geo-Information 2021, 10, 145 .
AMA StyleChengming Li, Zheng Wu, Lining Zhu, Li Liu, Chengcheng Zhang. Changes of Spatiotemporal Pattern and Network Characteristic in Population Flow under COVID-19 Epidemic. ISPRS International Journal of Geo-Information. 2021; 10 (3):145.
Chicago/Turabian StyleChengming Li; Zheng Wu; Lining Zhu; Li Liu; Chengcheng Zhang. 2021. "Changes of Spatiotemporal Pattern and Network Characteristic in Population Flow under COVID-19 Epidemic." ISPRS International Journal of Geo-Information 10, no. 3: 145.
Rapid urbanization in China has prompted plenty of urban facilities to be constructed with the expectation of harmonizing with the rapid growth of urban population. However, regarding the spatial interactions produced by cross-area human mobility, the diversity and variability of residents' trip requirements inevitably cause the deviations of the real interaction patterns from the optimal status determined by the current allocation of urban facilities. To maximize the utility of urban facility allocation, we designed a bipartite network-based approach to explore anomalous spatial interaction patterns within cities. First, considering the potential area attractiveness, a weighted origin-destination bipartite network was constructed to structure the spatial interactions between traffic analysis zones. Then, a branch and bound (BnB) based augmenting path algorithm was proposed to optimize the distribution of spatial interactions, which can maximize the urban population carrying capabilities. Finally, anomalous interaction patterns causing both overload and underload were detected through comparisons between the actual and optimal spatial interaction distribution. The experimental results show that the two types of anomalous interaction patterns have significantly different spatial distribution characteristics. Through further analyzing the relationships between the two types of anomalous interaction patterns and urban evolution process, this study can also provide targeted decision supports for the accommodating of urban facility allocations to the distributions of resident trips in space.
Baoju Liu; Min Deng; Jingyi Yang; Yan Shi; Jincai Huang; Chengming Li; Bingwen Qiu. Detecting anomalous spatial interaction patterns by maximizing urban population carrying capacity. Computers, Environment and Urban Systems 2021, 87, 101616 .
AMA StyleBaoju Liu, Min Deng, Jingyi Yang, Yan Shi, Jincai Huang, Chengming Li, Bingwen Qiu. Detecting anomalous spatial interaction patterns by maximizing urban population carrying capacity. Computers, Environment and Urban Systems. 2021; 87 ():101616.
Chicago/Turabian StyleBaoju Liu; Min Deng; Jingyi Yang; Yan Shi; Jincai Huang; Chengming Li; Bingwen Qiu. 2021. "Detecting anomalous spatial interaction patterns by maximizing urban population carrying capacity." Computers, Environment and Urban Systems 87, no. : 101616.
Shipborne gravity can be used to refine altimeter-derived gravity whose accuracy is low in shallow waters and areas with complex submarine topography. As altimeter-derived gravity only within a small radius around the shipborne data can be corrected by traditional methods, a new method based on multi-layer perceptron (MLP) neural network is proposed to refine the altimeter-derived gravity. Input variables of MLP include the positional information at observation points and geophysical information (from our own South China Sea gravity anomaly model (SCSGA) V1.0 and bathymetry model ETOPO1) at grid points around observation points. Output variables of MLP are the refined residual gravity anomalies at observation points. Training shipborne data are classified into four cases to train four MLP models, which are used to predict the refined gravity anomaly model SCSGA V1.1. Then all of the training shipborne data are used for training an MLP model to predict the refined gravity anomaly model SCSGA V1.2. Assessed by testing shipborne data, the accuracy of SCSGA V1.2 is 0.14 mGal higher than that of SCSGA V1.0, and similar to that of SCSGA V1.1. Compared with the original gravity anomaly model (SCSGA V1.0), the accuracy of the refined gravity anomaly model (SCSGA V1.2) by MLP is improved by 4.4% in areas where the training data are concentrated, and also improved by 2.2% in other areas. Therefore, the method of MLP can be used to refine the altimeter-derived gravity model by shipborne gravity, overcoming the problem of limited correction radius for traditional methods.
Chengcheng Zhu; Jinyun Guo; Jiajia Yuan; Xin Jin; Jinyao Gao; Chengming Li. Refining Altimeter-Derived Gravity Anomaly Model from Shipborne Gravity by Multi-Layer Perceptron Neural Network: A Case in the South China Sea. Remote Sensing 2021, 13, 607 .
AMA StyleChengcheng Zhu, Jinyun Guo, Jiajia Yuan, Xin Jin, Jinyao Gao, Chengming Li. Refining Altimeter-Derived Gravity Anomaly Model from Shipborne Gravity by Multi-Layer Perceptron Neural Network: A Case in the South China Sea. Remote Sensing. 2021; 13 (4):607.
Chicago/Turabian StyleChengcheng Zhu; Jinyun Guo; Jiajia Yuan; Xin Jin; Jinyao Gao; Chengming Li. 2021. "Refining Altimeter-Derived Gravity Anomaly Model from Shipborne Gravity by Multi-Layer Perceptron Neural Network: A Case in the South China Sea." Remote Sensing 13, no. 4: 607.
From late 2019 to early 2020, forest fires in southeastern Australia caused huge economic losses and huge environmental pollution. Monitoring forest fires has become increasingly important. A new method of fire detection using the difference between global navigation satellite system (GNSS)-derived precipitable water vapor and radiosonde-derived precipitable water vapor (ΔPWV) is proposed. To study the feasibility of the new method, the relationship is studied between particulate matter 10 (PM10) (2.5 to 10 microns particulate matter) and ΔPWV based on Global Positioning System (GPS) data, radiosonde data, and PM10 data from 1 June 2019 to 1 June 2020 in southeastern Australia. The results show that before the forest fire, ΔPWV and PM10 were smaller and less fluctuating. When the forest fire happened, ΔPWV and PM10 were increasing. Then after the forest fire, PM10 became small with relatively smooth fluctuations, but ΔPWV was larger and more fluctuating. Correlation between the 15-day moving standard deviation (STD) time series of ΔPWV and PM10 after the fire was significantly higher than that before the fire. This study shows that ΔPWV is effective in monitoring forest fires based on GNSS technique before and during forest fires in climates with more uniform precipitation, and using ΔPWV to detect forest fires based on GNSS needs to be further investigated in climates with more precipitation and severe climate change.
Jinyun Guo; Rui Hou; Maosheng Zhou; Xin Jin; Chengming Li; Xin Liu; Hao Gao. Monitoring 2019 Forest Fires in Southeastern Australia with GNSS Technique. Remote Sensing 2021, 13, 386 .
AMA StyleJinyun Guo, Rui Hou, Maosheng Zhou, Xin Jin, Chengming Li, Xin Liu, Hao Gao. Monitoring 2019 Forest Fires in Southeastern Australia with GNSS Technique. Remote Sensing. 2021; 13 (3):386.
Chicago/Turabian StyleJinyun Guo; Rui Hou; Maosheng Zhou; Xin Jin; Chengming Li; Xin Liu; Hao Gao. 2021. "Monitoring 2019 Forest Fires in Southeastern Australia with GNSS Technique." Remote Sensing 13, no. 3: 386.
Human activities generate diverse and sophisticated functional areas and may impact the existing planning of functional areas. Understanding the relationship between human activities and functional areas is key to identifying the real-time urban functional areas based on trajectories. Few previous studies have analyzed the interactive information on humans and regions for functional area identification. The relationship between human activities and residential areas is the most representative for urban functional areas because residential areas cover a wide range and are closely connected with human life. The aim of this paper is to propose the CARA (Commuting Activity and Residential Area) model to quantify the correlation between human activities and urban residential areas. In this model, human activities are represented by hot spots extracted by the Gaussian Mixture Model algorithm while residential areas are represented by POI (point of interest) data. The model shows that human activities and residential areas present a logarithmic relationship. The CARA model is further assessed by retrieving urban residential areas in Tengzhou City from shared e-bike trajectories. Compared with the actual map, the accuracy reaches 83.3%, thus demonstrating the model’s reliability and feasibility. This study provides a new method for functional areas identification based on trajectory data, which is helpful for formulating the urban people-oriented policies.
Xiaoqian Cheng; Weibing Du; Chengming Li; Leiku Yang; Linjuan Xu. Exploring the Attractiveness of Residential Areas for Human Activities Based on Shared E-Bike Trajectory Data. ISPRS International Journal of Geo-Information 2020, 9, 742 .
AMA StyleXiaoqian Cheng, Weibing Du, Chengming Li, Leiku Yang, Linjuan Xu. Exploring the Attractiveness of Residential Areas for Human Activities Based on Shared E-Bike Trajectory Data. ISPRS International Journal of Geo-Information. 2020; 9 (12):742.
Chicago/Turabian StyleXiaoqian Cheng; Weibing Du; Chengming Li; Leiku Yang; Linjuan Xu. 2020. "Exploring the Attractiveness of Residential Areas for Human Activities Based on Shared E-Bike Trajectory Data." ISPRS International Journal of Geo-Information 9, no. 12: 742.
Catchment division constitutes the foundation for urban water flood forecasting but represents a technically challenging task. The accurate division of catchments is significant for precisely forecasting urban waterlogging. However, existing catchment division methods usually lead to produce results that do not accurately reflect the actual land-use distributions. In recent years, most research has been performed in smaller study areas (less than 10 km2), in residential areas, parks and campuses, and usually focused on a single landscape type. However, for large highly urbanized areas with complex land uses, due to the spatial heterogeneity and complexity of such areas in terms of building, traffic network and hydrology, etc., there is few studies on sub-catchment division. Moreover, the division results by using existing method usually have deviate with the actual land-type distributions. To address the above-mentioned issues, a sub-catchment division method was here proposed that accounts for land-use types and flow directions, and it is suitable for large urban areas by introducing an auto-adaptive threshold adjustment in a novel algorithm. First, the study area is divided into first- and second-level (FL and SL, respectively) catchments according to the macroscale features such as natural landforms, canals, and pipe network. Second, an amended DEM (Digital Elevation Model) and flow direction data are used to divide the SL catchments into third-level direction-based (D-B) catchments. Finally, a novel land use-based algorithm is proposed to divide the D-B catchments into the “smallest” catchments (S-catchments). A large-scale area (44 km2) in Dongying City of China was employed to validate the proposed method. The experiment showed that the proposed method is suitable for subcatchment divisions in large regions and can ensure that the subcatchments are consistent with the actual distribution of land uses and runoff directions.
Chengming Li; Zixian Fan; Zheng Wu; Zhaoxin Dai; Li Liu; Chengcheng Zhang. Methodology of Sub-Catchment Division Considering Land Uses and Flow Directions. ISPRS International Journal of Geo-Information 2020, 9, 634 .
AMA StyleChengming Li, Zixian Fan, Zheng Wu, Zhaoxin Dai, Li Liu, Chengcheng Zhang. Methodology of Sub-Catchment Division Considering Land Uses and Flow Directions. ISPRS International Journal of Geo-Information. 2020; 9 (11):634.
Chicago/Turabian StyleChengming Li; Zixian Fan; Zheng Wu; Zhaoxin Dai; Li Liu; Chengcheng Zhang. 2020. "Methodology of Sub-Catchment Division Considering Land Uses and Flow Directions." ISPRS International Journal of Geo-Information 9, no. 11: 634.
As air pollution becomes highly focused in China, the accurate identification of its influencing factors is critical for achieving effective control and targeted environmental governance. Land-use distribution is one of the key factors affecting air quality, and research on the impact of land-use distribution on air pollution has drawn wide attention. However, considerable studies have mostly used linear regression models, which fail to capture the nonlinear effects of land-use distribution on PM2.5 (fine particulate matter with a diameter less than or equal to 2.5 microns) and to show how impacts on PM2.5 vary with land-use magnitudes. In addition, related studies have generally focused on annual analyses, ignoring the seasonal variability of the impact of land-use distribution on PM2.5, thus leading to possible estimation biases for PM2.5. This study was designed to address these issues and assess the impacts of land-use distribution on PM2.5 in Weifang, China. A machine learning statistical model, the boosted regression tree (BRT), was applied to measure nonlinear effects of land-use distribution on PM2.5, capture how land-use magnitude impacts PM2.5 across different seasons, and explore the policy implications for urban planning. The main conclusions are that the air quality will significantly improve with an increase in grassland and forest area, especially below 8% and 20%, respectively. When the distribution of construction land is greater than around 10%, the PM2.5 pollution can be seriously substantially increased with the increment of their areas. The impact of gardens and farmland presents seasonal characteristics. It is noted that as the weather becomes colder, the inhibitory effect of vegetation distribution on the PM2.5 concentration gradually decreases, while the positive impacts of artificial surface distributions, such as construction land and roads, are aggravated because leaves drop off in autumn (September–November) and winter (December–February). According to the findings of this study, it is recommended that Weifang should strengthen pollution control in winter, for instance, expand the coverage areas of evergreen vegetation like Pinus bungeana Zucc. and Euonymus japonicus Thunb, and increase the width and numbers of branches connecting different main roads. The findings also provide quantitative and optimal land-use planning and strategies to minimize PM2.5 pollution, referring to the status of regional urbanization and greening construction.
Chengming Li; Kuo Zhang; Zhaoxin Dai; Zhaoting Ma; Xiaoli Liu. Investigation of the Impact of Land-Use Distribution on PM2.5 in Weifang: Seasonal Variations. International Journal of Environmental Research and Public Health 2020, 17, 5135 .
AMA StyleChengming Li, Kuo Zhang, Zhaoxin Dai, Zhaoting Ma, Xiaoli Liu. Investigation of the Impact of Land-Use Distribution on PM2.5 in Weifang: Seasonal Variations. International Journal of Environmental Research and Public Health. 2020; 17 (14):5135.
Chicago/Turabian StyleChengming Li; Kuo Zhang; Zhaoxin Dai; Zhaoting Ma; Xiaoli Liu. 2020. "Investigation of the Impact of Land-Use Distribution on PM2.5 in Weifang: Seasonal Variations." International Journal of Environmental Research and Public Health 17, no. 14: 5135.
When it comes to feature retention in multi-scale representations of ocean flow fields, not all data points are equal. Therefore, this paper proposes a method of selecting data points based on their importance. First, an autocorrelation analysis is performed on flow speed and the rate of change in flow direction. Then, the magnitude of speed and variation in the rate of change in flow direction are classified. Feature regions are determined according to autocorrelation aggregation and classification analysis. Then, rough set theory and evidence theory are applied, using these results to determine the weights of different points. Finally, these weights are used to construct multi-scale representations of ocean flow fields, which effectively retain flow-field characteristics.
Bo Ai; Decheng Sun; Yanmei Liu; Chengming Li; Fanlin Yang; Yong Yin; Huibo Tian. Multi-Scale Representation of Ocean Flow Fields Based on Feature Analysis. ISPRS International Journal of Geo-Information 2020, 9, 307 .
AMA StyleBo Ai, Decheng Sun, Yanmei Liu, Chengming Li, Fanlin Yang, Yong Yin, Huibo Tian. Multi-Scale Representation of Ocean Flow Fields Based on Feature Analysis. ISPRS International Journal of Geo-Information. 2020; 9 (5):307.
Chicago/Turabian StyleBo Ai; Decheng Sun; Yanmei Liu; Chengming Li; Fanlin Yang; Yong Yin; Huibo Tian. 2020. "Multi-Scale Representation of Ocean Flow Fields Based on Feature Analysis." ISPRS International Journal of Geo-Information 9, no. 5: 307.
As air pollution becomes progressively more serious, accurate identification of urban air pollution characteristics and associated pollutant transport mechanisms helps to effectively control and alleviate air pollution. This paper investigates the pollution characteristics, transport pathways, and potential sources of PM2.5 in Weifang based on PM2.5 monitoring data from 2015 to 2016 using three methods: Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT), the potential source contribution function (PSCF), and concentration weighted trajectory (CWT). The results show the following: (1) Air pollution in Weifang was severe from 2015 to 2016, and the annual average PM2.5 concentration was more than twice the national air quality second-level standard (35 μg/m3). (2) Seasonal transport pathways of PM2.5 vary significantly: in winter, spring and autumn, airflow from the northwest and north directions accounts for a large proportion; in contrast, in summer, warm-humid airflows from the ocean in the southeastern direction dominate with scattered characteristics. (3) The PSCF and CWT results share generally similar characteristics in the seasonal distributions of source areas, which demonstrate the credibility and accuracy of the analysis results. (4) More attention should be paid to short-distance transport from the surrounding areas of Weifang, and a joint pollution prevention and control mechanism is critical for controlling regional pollution.
Chengming Li; Zhaoxin Dai; Xiaoli Liu; Pengda Wu. Transport Pathways and Potential Source Region Contributions of PM2.5 in Weifang: Seasonal Variations. Applied Sciences 2020, 10, 2835 .
AMA StyleChengming Li, Zhaoxin Dai, Xiaoli Liu, Pengda Wu. Transport Pathways and Potential Source Region Contributions of PM2.5 in Weifang: Seasonal Variations. Applied Sciences. 2020; 10 (8):2835.
Chicago/Turabian StyleChengming Li; Zhaoxin Dai; Xiaoli Liu; Pengda Wu. 2020. "Transport Pathways and Potential Source Region Contributions of PM2.5 in Weifang: Seasonal Variations." Applied Sciences 10, no. 8: 2835.
Smart city evaluation is a critical component in smart city construction and plays an important role in guiding and promoting smart development of cities. Currently, existing research and applications of smart city evaluation are still in the exploration stage. They mainly focus on evaluation of one single aspect, use indicators with distinct regional characteristics and poor extensibility, and cannot be well-integrated with common and shareable smart city frameworks; these limitations have led to biased evaluation results. Based on a common and shareable smart city framework, this paper proposes a well-integrated, universal, strongly practical, and highly extensible evaluation system. Then, using the above-mentioned evaluation system, 17 smart cities in China are assessed. This application demonstrates that the evaluation system plays an important guiding role for better understanding the overall smart city platform construction situation in China, performing horizontal comparisons and establishing benchmarks among smart cities. Comparative analyses of indicators demonstrate that future smart city construction in China should pay more attention to novel innovations, the construction of dynamic information resources and spatiotemporal big data.
Chengming Li; Zhaoxin Dai; Xiaoli Liu; Wei Sun. Evaluation System: Evaluation of Smart City Shareable Framework and Its Applications in China. Sustainability 2020, 12, 2957 .
AMA StyleChengming Li, Zhaoxin Dai, Xiaoli Liu, Wei Sun. Evaluation System: Evaluation of Smart City Shareable Framework and Its Applications in China. Sustainability. 2020; 12 (7):2957.
Chicago/Turabian StyleChengming Li; Zhaoxin Dai; Xiaoli Liu; Wei Sun. 2020. "Evaluation System: Evaluation of Smart City Shareable Framework and Its Applications in China." Sustainability 12, no. 7: 2957.
Point of interest (POI) matching is critical but is the most technically difficult part of multi-source POI fusion. The accurate matching of POIs from different sources is important for the effective reuse of POI data. However, the existing research on POI matching usually adopts weak constraints, which leads to a low POI matching accuracy. To address the shortcomings of previous studies, this paper proposes a POI matching method with multiple determination constraints. First, according to various attributes (name, class, and spatial location), a new calculation model considering spatial topology, name role labeling, and bottom-up class constraints is established. In addition, the optimal threshold values corresponding to the different attribute constraints are determined. Second, according to the multiattribute constraint values and optimal thresholds, a constraint model with multiple strict determination constraints is proposed. Finally, actual POI data from Baidu Map and Gaode Map in Dongying city is used to validate the method. Comparing to the existing method, the accuracy and recall of the proposed method increase 0.3% and 7.1%, respectively. The experimental results demonstrate that the proposed POI matching method attains a high matching accuracy and high feasibility.
Chengming Li; Li Liu; Zhaoxin Dai; Xiaoli Liu. Different Sourcing Point of Interest Matching Method Considering Multiple Constraints. ISPRS International Journal of Geo-Information 2020, 9, 214 .
AMA StyleChengming Li, Li Liu, Zhaoxin Dai, Xiaoli Liu. Different Sourcing Point of Interest Matching Method Considering Multiple Constraints. ISPRS International Journal of Geo-Information. 2020; 9 (4):214.
Chicago/Turabian StyleChengming Li; Li Liu; Zhaoxin Dai; Xiaoli Liu. 2020. "Different Sourcing Point of Interest Matching Method Considering Multiple Constraints." ISPRS International Journal of Geo-Information 9, no. 4: 214.
Traffic congestion, especially during peak hours, has become a challenge for transportation systems in many metropolitan areas, and such congestion causes delays and negative effects for passengers. Many studies have examined the prediction of congestion; however, these studies focus mainly on road traffic, and subway transit, which is the main form of transportation in densely populated cities, such as Tokyo, Paris, and Beijing and Shenzhen in China, has seldom been examined. This study takes Shenzhen as a case study for predicting congestion in a subway system during peak hours and proposes a hybrid method that combines a static traffic assignment model with an agent-based dynamic traffic simulation model to estimate recurrent congestion in this subway system. The homes and work places of the residents in this city are collected and taken to represent the traffic demand for the subway system of Shenzhen. An origin-destination (OD) matrix derived from the data is used as an input in this method of predicting traffic, and the traffic congestion is presented in simulations. To evaluate the predictions, data on the congestion condition of subway segments that are released daily by the Shenzhen metro operation microblog are used as a reference, and a comparative analysis indicates the appropriateness of the proposed method. This study could be taken as an example for similar studies that model subway traffic in other cities.
Zhenwei Luo; Yu Zhang; Lin Li; Biao He; Chengming Li; Haihong Zhu; Wei Wang; Shen Ying; Yuliang Xi. A Hybrid Method for Predicting Traffic Congestion during Peak Hours in the Subway System of Shenzhen. Sensors 2019, 20, 150 .
AMA StyleZhenwei Luo, Yu Zhang, Lin Li, Biao He, Chengming Li, Haihong Zhu, Wei Wang, Shen Ying, Yuliang Xi. A Hybrid Method for Predicting Traffic Congestion during Peak Hours in the Subway System of Shenzhen. Sensors. 2019; 20 (1):150.
Chicago/Turabian StyleZhenwei Luo; Yu Zhang; Lin Li; Biao He; Chengming Li; Haihong Zhu; Wei Wang; Shen Ying; Yuliang Xi. 2019. "A Hybrid Method for Predicting Traffic Congestion during Peak Hours in the Subway System of Shenzhen." Sensors 20, no. 1: 150.
Trajectory data include rich interactive information of humans. The correct identification of trips is the key to trajectory data mining and its application. A new method, multi-rule-constrained homomorphic linear clustering (MCHLC), is proposed to extract trips from raw trajectory data. From the perspective of the workflow, the MCHLC algorithm consists of three parts. The first part is to form the original sub-trajectory moving/stopping clusters, which are obtained by sequentially clustering trajectory elements of the same motion status. The second part is to determine and revise the motion status of the original sub-trajectory clusters by the speed, time duration, directional constraint, and contextual constraint to construct the stop/move model. The third part is to extract users’ trips by filtering the stop/move model using the following rules: distance rule, average speed rule, shortest path rule, and completeness rule, which are related to daily riding experiences. Verification of the new method is carried out with the shared electric bike trajectory data of one week in Tengzhou city, evaluated by three indexes (precision, recall, and F1-score). The experiment shows that the index values of the new algorithm are higher (above 93%) than those of the baseline methods, indicating that the new algorithm is better. Compared to the baseline velocity sequence linear clustering (VSLC) algorithm, the performance of the new algorithm is improved by approximately 10%, mainly owing to two factors, directional constraint and contextual constraint. The better experimental results indicate that the new algorithm is suitable to extract trips from the sparse trajectories of shared e-bikes and other transportation forms, which can provide technical support for urban hotspot detection and hot route identification.
Xiaoqian Cheng; Chengming Li; Weibing Du; Jianming Shen; Zhaoxin Dai. Trip Extraction of Shared Electric Bikes Based on Multi-Rule-Constrained Homomorphic Linear Clustering Algorithm. ISPRS International Journal of Geo-Information 2019, 8, 526 .
AMA StyleXiaoqian Cheng, Chengming Li, Weibing Du, Jianming Shen, Zhaoxin Dai. Trip Extraction of Shared Electric Bikes Based on Multi-Rule-Constrained Homomorphic Linear Clustering Algorithm. ISPRS International Journal of Geo-Information. 2019; 8 (12):526.
Chicago/Turabian StyleXiaoqian Cheng; Chengming Li; Weibing Du; Jianming Shen; Zhaoxin Dai. 2019. "Trip Extraction of Shared Electric Bikes Based on Multi-Rule-Constrained Homomorphic Linear Clustering Algorithm." ISPRS International Journal of Geo-Information 8, no. 12: 526.
Spatio-temporal indexing is a key technique in spatio-temporal data storage and management. Indexing methods based on spatial filling curves are popular in research on the spatio-temporal indexing of vector data in the Not Relational (NoSQL) database. However, the existing methods mostly focus on spatial indexing, which makes it difficult to balance the efficiencies of time and space queries. In addition, for non-point elements (line and polygon elements), it remains difficult to determine the optimal index level. To address these issues, this paper proposes an adaptive construction method of hierarchical spatio-temporal index for vector data. Firstly, a joint spatio-temporal information coding based on the combination of the partition and sort key strategies is presented. Secondly, the multilevel expression structure of spatio-temporal elements consisting of point and non-point elements in the joint coding is given. Finally, an adaptive multi-level index tree is proposed to realize the spatio-temporal index (Multi-level Sphere 3, MLS3) based on the spatio-temporal characteristics of geographical entities. Comparison with the XZ3 index algorithm proposed by GeoMesa proved that the MLS3 indexing method not only reasonably expresses the spatio-temporal features of non-point elements and determines their optimal index level, but also avoids storage hotspots while achieving spatio-temporal retrieval with high efficiency.
Chengming Li; Zheng Wu; Pengda Wu; Zhanjie Zhao; Wu; Li; Zhao. An Adaptive Construction Method of Hierarchical Spatio-Temporal Index for Vector Data under Peer-to-Peer Networks. ISPRS International Journal of Geo-Information 2019, 8, 512 .
AMA StyleChengming Li, Zheng Wu, Pengda Wu, Zhanjie Zhao, Wu, Li, Zhao. An Adaptive Construction Method of Hierarchical Spatio-Temporal Index for Vector Data under Peer-to-Peer Networks. ISPRS International Journal of Geo-Information. 2019; 8 (11):512.
Chicago/Turabian StyleChengming Li; Zheng Wu; Pengda Wu; Zhanjie Zhao; Wu; Li; Zhao. 2019. "An Adaptive Construction Method of Hierarchical Spatio-Temporal Index for Vector Data under Peer-to-Peer Networks." ISPRS International Journal of Geo-Information 8, no. 11: 512.
Recent advances in the fusion technology of remotely sensed data have led to an increased availability of extracted urban information from multiple spatial resolutions and multi-temporal acquisitions. Despite the existing extraction methods, there remains the challenging task of fully exploiting the characteristics of multisource remote sensing data, each of which has its own advantages. In this paper, a new fusion approach for accurately extracting urban built-up areas based on the use of multisource remotely sensed data, i.e., the DMSP-OLS nighttime light data, the MODIS land cover product (MCD12Q1) and Landsat 7 ETM+ images, was proposed. The proposed method mainly consists of two components: (1) the multi-level data fusion, including the initial sample selection, unified pixel resolution and feature weighted calculation at the feature level, as well as pixel attribution determination at decision level; and (2) the optimized sample selection with multi-factor constraints, which indicates that an iterative optimization with the normalized difference vegetation index (NDVI), the modified normalized difference water index (MNDWI), and the bare soil index (BSI), along with the sample training of the support vector machine (SVM) and the extraction of urban built-up areas, produces results with high credibility. Nine Chinese provincial capitals along the Silk Road Economic Belt, such as Chengdu, Chongqing, Kunming, Xining, and Nanning, were selected to test the proposed method with data from 2001 to 2010. Compared with the results obtained by the traditional threshold dichotomy and the improved neighborhood focal statistics (NFS) method, the following could be concluded. (1) The proposed approach achieved high accuracy and eliminated natural elements to a great extent while obtaining extraction results very consistent to those of the more precise improved NFS approach at a fine scale. The average overall accuracy (OA) and average Kappa values of the extracted urban built-up areas were 95% and 0.83, respectively. (2) The proposed method not only identified the characteristics of the urban built-up area from the nighttime light data and other daylight images at the feature level but also optimized the samples of the urban built-up area category at the decision level, making it possible to provide valuable information for urban planning, construction, and management with high accuracy.
Xiaolong Ma; Chengming Li; Xiaohua Tong; Sicong Liu. A New Fusion Approach for Extracting Urban Built-up Areas from Multisource Remotely Sensed Data. Remote Sensing 2019, 11, 2516 .
AMA StyleXiaolong Ma, Chengming Li, Xiaohua Tong, Sicong Liu. A New Fusion Approach for Extracting Urban Built-up Areas from Multisource Remotely Sensed Data. Remote Sensing. 2019; 11 (21):2516.
Chicago/Turabian StyleXiaolong Ma; Chengming Li; Xiaohua Tong; Sicong Liu. 2019. "A New Fusion Approach for Extracting Urban Built-up Areas from Multisource Remotely Sensed Data." Remote Sensing 11, no. 21: 2516.
Smart City is a new concept that uses information and communication technology (ICT) to promote the smartification of urban construction, planning and services. Currently, a number of cities have conducted studies on smart cities, but they have mostly focused on analyzing the conceptual connotations or applications in specific domains and lack a shareable and integrated framework, which has led to significant barriers for individual smart projects. By analyzing the framework and applications of Smart City, this paper proposes a common, shareable and integrated conceptual framework. Then, based on this framework, it further proposes a unified portal platform that can balance multiple stakeholders, including the government, citizens and businesses, as well as for common, custom and other application modes. Finally, the implementation of Smart Weifang based on this platform is discussed. The applications indicate that this shareable platform can effectively eliminate the data and technological barriers between different smart city systems while also avoiding redundant financial investments. The investigation of this proposed framework and platform is highly significant for the unified construction of smart cities and the intensification of the hardware environment, thus representing a true achievement in the transition from ‘information islands’ to ‘information sharing and interconnection’ for urban informatization.
Chengming Li; Xiaoli Liu; Zhaoxin Dai; Zhanjie Zhao. Smart City: A Shareable Framework and Its Applications in China. Sustainability 2019, 11, 4346 .
AMA StyleChengming Li, Xiaoli Liu, Zhaoxin Dai, Zhanjie Zhao. Smart City: A Shareable Framework and Its Applications in China. Sustainability. 2019; 11 (16):4346.
Chicago/Turabian StyleChengming Li; Xiaoli Liu; Zhaoxin Dai; Zhanjie Zhao. 2019. "Smart City: A Shareable Framework and Its Applications in China." Sustainability 11, no. 16: 4346.
Extraction of the skeleton line of complex polygons is difficult, and a hot topic in map generalization study. Due to the irregularity and complexity of junctions, it is difficult for traditional methods to maintain main structure and extension characteristics when dealing with dense junction areas, so a skeleton line extraction method considering stroke features has been proposed in this paper. Firstly, we put forward a long-edge adaptive node densification algorithm, which is used to construct boundary-constrained Delaunay triangulation to uniformly divide the polygon and extract the initial skeleton line. Secondly, we defined the triangles with three adjacent triangles (Type III) as the basic unit of junctions, then obtained the segmented areas with dense junctions on the basis of local width characteristics and correlation relationships of each Type III triangle. Finally, we concatenated the segments into strokes and corrected the initial skeleton lines based on the extension direction features of each stroke. The actual water network data of Jiangsu Province in China were used to verify the method. Experimental results show that the proposed method can better identify the areas with dense junctions and that the extracted skeleton line is naturally smooth and well-connected, which accurately reflects the main structure and extension characteristics of these areas.
Chengming Li; Yong Yin; Pengda Wu; Wei Wu. Skeleton Line Extraction Method in Areas with Dense Junctions Considering Stroke Features. ISPRS International Journal of Geo-Information 2019, 8, 303 .
AMA StyleChengming Li, Yong Yin, Pengda Wu, Wei Wu. Skeleton Line Extraction Method in Areas with Dense Junctions Considering Stroke Features. ISPRS International Journal of Geo-Information. 2019; 8 (7):303.
Chicago/Turabian StyleChengming Li; Yong Yin; Pengda Wu; Wei Wu. 2019. "Skeleton Line Extraction Method in Areas with Dense Junctions Considering Stroke Features." ISPRS International Journal of Geo-Information 8, no. 7: 303.
Filling dot maps with a regular pattern is a key step in visual representation of land use data. The traditional methods cannot adapt well to shape features of complex areas, leading to an unreasonable symbol arrangement in the inner region and area boundaries during symbol filling. For this reason, a dot symbol auto-filling method of complex areas considering shape features is proposed in this paper. First, based on the constrained Delaunay triangulation, the internal structure of a complex area is divided into three simple filling areas denoted tile type, narrow type, and point type. Next, according to the geometric shape features, these three type areas are filled with plane, line, and point level of symbols respectively. Finally, the dot symbols near to boundaries are adjusted on the basis of the boundary constraint to optimize the symbol-filling effect. Based on the national data of a region in Guizhou Province, the method proposed in this paper is compared to the traditional symbol filling methods for validation. The experimental results show that the proposed method improves the dot symbol sufficiency of complex areas, and the edge of dot symbol group closely adhere to the boundary and conform to the extension characteristics of boundary without creating spatial conflicts such as spatial overlap, the filling result can better reflect the shape features of the areas.
Yong Yin; Chengming Li; Pengda Wu. Dot Symbol Auto-Filling Method for Complex Areas Considering Shape Features. ISPRS International Journal of Geo-Information 2019, 8, 158 .
AMA StyleYong Yin, Chengming Li, Pengda Wu. Dot Symbol Auto-Filling Method for Complex Areas Considering Shape Features. ISPRS International Journal of Geo-Information. 2019; 8 (3):158.
Chicago/Turabian StyleYong Yin; Chengming Li; Pengda Wu. 2019. "Dot Symbol Auto-Filling Method for Complex Areas Considering Shape Features." ISPRS International Journal of Geo-Information 8, no. 3: 158.