A Case Study on Spatio-Temporal Data Mining of ...

sustainability Article

A Case Study on Spatio-Temporal Data Mining of Urban Social Management Events Based on Ontology Semantic Analysis Shaohua Wang 1 , Xianxiong Liu 1 , Haiyin Wang 2,3, * and Qingwu Hu 1,3, * 1 2 3

*

ID

School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China; [email protected] (S.W.); [email protected] (X.L.) Institute of Qingdao Geotechnical Investigation and Surveying, Qingdao 266071, China QingDao Key Laboratory for the Integration and Application of Sea-land Geographical Information, Qingdao 266071, China Correspondence: [email protected] (H.W.); [email protected] (Q.H.); Tel.: +86-189-710-70362 (Q.H.)

Received: 29 April 2018; Accepted: 15 June 2018; Published: 19 June 2018

Abstract: The massive urban social management data with geographical coordinates from the inspectors, volunteers, and citizens of the city are a new source of spatio-temporal data, which can be used for the data mining of city management and the evolution of hot events to improve urban comprehensive governance. This paper proposes spatio-temporal data mining of urban social management events (USMEs) based on ontology semantic approach. First, an ontology model for USMEs is presented to accurately extract effective social management events from non-structured UMSEs. Second, an explorer spatial data analysis method based on “event-event” and “event-place” from spatial and time aspects is presented to mine the information from UMSEs for the urban social comprehensive governance. The data mining results are visualized as a thermal chart and a scatter diagram for the optimization of the management resources configuration, which can improve the efficiency of municipal service management and municipal departments for decision-making. Finally, the USMEs of Qingdao City in August 2016 are taken as a case study with the proposed approach. The proposed method can effectively mine the management of social hot events and their spatial distribution patterns, which can guide city governance and enhance the city’s comprehensive management level. Keywords: city management; spatial-temporal event; ontology; semantic; data mining

1. Introduction Whether the planning and management of a city as a region of human activities is reasonable has seriously affected the long-term development of cities and the happiness index of residents’ lives [1–3]. The management and governance of human society is a large and complex project, especially in modern cities. The rapid development of society, complex urban internal space structure, diversified human activities, and human characteristics created by regional factors have posed great challenges to society management [4–7]. The inevitable result on the modern city of human activities is highly clustered. The human civilization, society, economy, and the culture of highly concentrated spaces are an open and complicated system. This openness and complexity have led to complex management of modern cities. Given that digital earth, smart city, and the development of related technologies are put forward, the management and decision-making support of urban social management are possible [8–16]. Importing the digital and intelligent means into the management of cities is the inevitable trend of modern urban social management [17–19]. The way of using the social management

Sustainability 2018, 10, 2084; doi:10.3390/su10062084

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Sustainability 2018, 10, 2084

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information database of massive municipal administration departments and analyzing the daily behavior patterns of urban residents are important for urban sustainable development. The spatial distribution characteristics of social security problems and the urban inner space structure can provide decisive support for government departments in providing social management content based on urban production, economy, society, culture, and population management. The data mining of human activities and urban social management events from the city management of mining has become a hot research topic for the urban social management [20–23]. Noulas et al. [24,25] collected tens-of-millions of user check-in data to analyze the user history, moving trajectory for the prediction of the future migration trend of users, and then presented a user interest site recommendation. Ji et al. [26] proposed a themed street clustering method to detect the themed streets of a specific region with the user’s mobile phone data from social networks. Farhad and Laylavi [27] designed a multi-elemental location inference method with the geotagged data from Twitter and tried to predict the location of tweets to provide auxiliary data for emergency response. Hu et al. [28] proposed an urban commercial area mining and analysis approach by crawling location-based check-in data from social networks such as Weibo to provide reliable decision-making support in urban planning and economic development. Wang et al. [29] designed a POI significance calculation algorithm using the check-in data from social networks. They analyzed the behavior rules of users, and then studied the distribution rules of urban landmarks on the spatial level, which can be well applied in the intelligent urban management and smart city services. With the diversity of data sources, for a wide variety of urban social management data, domestic and foreign scholars have designed a variety of analytical methods applied to different areas of urban social management. Zhang et al. [30] used the Markoff forecast model to predict the urban heat island proliferation tendency and provided the decision-making support to mitigate an urban heat island. Kazak [31] integrated scenario analysis, land use modelling and GIS for the assessment of areas for the potential exposure to the Urban Heat Island (UHI) effect, which can be used for the decision making of urban management. Ai [32] established a BP neural network model to forecast the development trend analysis of haze weather using the historical data of 2.5 PM. Zhao et al. [33] analyzed the two-magnitude five pollutant data and researched and analyzed the winter haze event of the North China Plain and its mechanism. Liu et al. [34] constructed the gray Markov chain model, applied it in the traffic volume forecast domain, and realized the traffic volume high accuracy forecast. Das and Winter [35] designed a hybrid knowledge-driven framework, which integrates fuzzy logic and neural networks to analyze vehicle GPS trajectory data and achieve the real-time detection of city traffic patterns; the framework is of great significance to the traffic and transportation planning work of the city. Deng [36] extracted the law of travel behaviors of residents by analyzing people’s travel trajectory data. Using the residents’ travel habits, they forecast the traffic demand of the city and provide theoretical support for the traffic control department in urban transportation planning. Bergman and Oksanen [37] combined the motion track data and Open Street Map (OSM) and applied them in the automatic travel route planning. Zhang et al. [38] analyzed the various areas of city residents in the travel law and in different periods by processing urban taxi track data in time-sharing segmentation to obtain information from all city residents who commute. Ishikawa and Fujinami [39] collected a large number of mobile phone users who upload travel data and identified the user’s circumvention of certain roads through a large number of pedestrian trajectories. They achieved the detection of abnormal roads, such as road pavement cracks, holes, and other issues. Numerous studies show that the spatio-temporal data mining based on social media data and urban management has been fully applied in various fields of city management, such as emergency response [17,40], urban commercial zone and landmark detection for city planning [38,39], environment monitoring [38–40], traffic planning [41–44] and road maintenance [45] etc. At present, most of the spatio-temporal data mining researches of urban management are based on indirect data, such as social media data; geo-tagged check-in data; travel data from buses, taxis and subways; cell phone calling data etc., which can only analyze the pattern of a certain phenomenon


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in social management from one side [46–50]. The results of indirect spatio-temporal data mining in urban management have some limitations. For example, it can only reflect a small point of the social comprehensive treatment, and the effectiveness of the results needs to be verified through the actual situation. In the process of smart city construction, information technology and mobile internet have been introduced into the field of social management and comprehensive control [51,52]. Thus, the real time collection and analysis of various events during the social management can be generated on time from the Smart City platform. In city management, the primary-level staff and volunteers can obtain a large number of firsthand information such as the status of infrastructure services, public security, disputes and other management logs, which have geographical coordinates. These geotagged social management events become direct spatio-temporal data in urban social management. The data mining results are more reliable than indirect data, do not need verification, and can provide better social management and comprehensive control in decision services of city management. Therefore, how to make good use of spatio-temporal data from urban social management to explore the existing problems in current social management and comprehensive control, is of great significance. It can help relevant city departments adjust the social management policies and enhance the ability and level of urban management. This paper takes the spatio-temporal data of the urban social management events in the Huangdao District of Qingdao city as the research sample to dig out the spatial distribution pattern and the event distribution pattern of hot events in social management, such as the status of infrastructure services, social security, production safety, disputes, and other incidents. Moreover, analyzing the internal cause and external expression through spatiotemporal visualization to provide decision support for the social management and comprehensive control of the city. 2. Materials and Methods The concept system of social comprehensive governance is huge and complex, and there are various kinds of events. This paper focuses on the extraction of interesting hot events and the spatio-temporal information mining, which is only one of the many entry points in this field. It has a broad research space in the information mining of the social comprehensive management events based on space-time management, whether in content or method. The smart city platform adds a geographic coordinate tag for a variety of events and log data generated from the city management process, but these data records are from inspectors, volunteers in the city management, and even citizens; the events are described as unstructured natural language. This case study proposes a spatio-temporal data mining approach based on the urban social management events to extract unstructured natural language information, to find the event spatio-temporal distribution pattern, and to provide visualized decision support for the social management and comprehensive control of the city. The technical framework of the proposed approach is shown in Figure 1. Figure 1 shows that the data mining of spatio-temporal urban social management events (USMEs) includes four steps. First, the quality analysis and preprocessing of spatiotemporal data in the social comprehensive management of the city, which excludes invalid data sets, are introduced in Section 2.1. Second, ontology semantic reasoning is used for unstructured natural language records to obtain the structured event database, which is introduced in Section 2.2. Third, through the time and space exploratory analysis of event-events, the time and spatial laws of urban events are extracted, as introduced in Section 2.3. Finally, the mining laws and patterns of urban events are visualized, and the decision support for urban management is provided, as introduced in Section 3.


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2

1

Ontology Processing

Quality Analysis and Preprocessing De-noising with attribute

Ontology Construction USMEs as natural

Ontology Reasoning

language

De-noising by position

Urban Social Management Events

Structured USMEs Database

3 Data Mining ESDA

Hotspots

Event-Event (S)

Data Mining Result

Event-Event (T)

Visualization

Event-Position

4

Urban Governance Events

Figure 1. Flowchart of the Proposed Approach. Figure 1. Flowchart of the Proposed Approach.

2.1. Qualiti Analysis and Noise Processing of USMEs 2.1. Qualiti Analysis and Noise Processing of USMEs The geographical features of urban social management events are composed of spatial The geographical features of urban social management characteristics, temporal characteristics and attribute characteristicsevents (Figureare 2). composed of spatial characteristics, temporal characteristics and attribute characteristics (Figure Figure 2 shows that the spatial characteristics of USMEs including the2). text description of the Figure 2 shows that the spatial characteristics of USMEs including the text description of the time occurrence position, the street and area where it belongs, and the specific coordinates of time the occurrenceposition position,ofthe street andAs area it belongs, and thecharacteristic specific coordinates of the occurrence occurrence the event. thewhere most important spatial of social management position of the coordinates event. As the importantfactors spatialfor characteristic of socialand management events, location aremost the necessary event visualization subsequentevents, data location coordinates are the necessary factors for event visualization and subsequent data mining. mining. The temporal characteristic of an event is the occurrence of an event time and date. The The temporal characteristic of an event is occurrence ofnon-temporal an event timecharacteristics and date. The attribute characteristics of events describe thethe non-spatial and ofattribute events. characteristics of events describe the non-spatial and non-temporal characteristics of events. It It records the basic description of the event in the text form, that is, the kind of social events thatrecords have the basic “Event description of the event in the text form, that is, the kindtext-based of social events that have occurred. occurred. description” records the principal of the event; event descriptions have “Event description” records the principal of the event; text-based event descriptions high amounts high amounts of unstructured event information with great data mining values and have are indispensable of the unstructured event withmanagement great data mining values and are indispensable forand the for achievement of ainformation complete social event. Misuse, equipment performance, achievement of athe complete socialproblems management event. Misuse, equipment performance, andquality failure failure can cause data quality of social comprehensive management. The data can cause the data quality problems of social comprehensive management. The data quality of an of an event must be addressed. A social management event consists of three basic elements: time, event and mustevent be addressed. A social event consists of three basic elements: time, place, place, description, whichmanagement is indispensable. The “event description” field records the and event description, which is indispensable. The “event description” field records the principal principal of the event, which is the heart of the event. When the data is checked, the priority is higherof the event, which is the heart the event. WhenFigure the data is checked, the priority is and higher than the than the time of occurrence andofplace of the event. 3 shows the quality analysis processing time of of occurrence and place of the event. Figure 3 shows the quality analysis and processing method method social comprehensive quality events. of social comprehensive quality events.


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Event location Coordinate: X,Y Spatial features Street

Geographical features of social management events

Area

Temporal characteristics

Event time

Important attribute Attribute characteristics

Event description

Event number Other attribute

Record number Emergency level

Figure2.2.Geographical Geographicalfeatures features of of social social management Figure managementevents. events.

Figure 3 shows that the quality disposal of urban social management data is based on the “event Figure 3 shows thesteps quality of urban social management data is based on the “event description” field, that whose are disposal as follows: description” field, whose steps are as follows: Step 1: Iterate through each record of the database, and check the “event description” and the Step feature 1: Iterate through record of the database, and check the “event description” and the spatial field of eacheach event. spatial feature eachare event. Step 2: Iffield the of fields complete and without error, jump directly to (3) and process each field, Step 2: If are described completein and jump directly to repaired, (3) and process according tothe the fields principle thewithout previouserror, article. If a field can be jump toeach (3). Iffield, it according to repaired, the principle described in the previous article. If a field can be repaired, jump to (3). If it cannot be discard the record. Step 3: Matchdiscard the record each record in the new database. If records in the new data do not cannot be repaired, the with record. repeat that record, add the record to therecord new database; otherwise, discard the record. Step 3: Match the record with each in the new database. If records in the new data do not Step 4: Iterate through the original database until all records are processed. repeat that record, add the record to the new database; otherwise, discard the record. Step 4: Iterate through the original database until all records are processed. 2.2. Information Extraction of USMEs Based on Ontology Semantic Reasoning

2.2. Information Extraction of USMEs on Ontology Semantic Reasoningevents, but the information We rely solely on the location Based and time data of urban management that besolely mined,on is the limited. However, a wealth is contained in thebut textthe fields, which Wecan rely location and time data of of information urban management events, information describe article designs its conceptual architecture diagram and ontology by that can beevents. mined,This is limited. However, a wealth of information is contained in themodel text fields, analyzing the type structure system of urban social management events. The ontology of social which describe events. This article designs its conceptual architecture diagram and ontology model are built through theurban ontology-building tool. Jena is an open source program by management analyzing theevents type structure system of social management events. The ontology of social development framework, which provides a powerful semantic ontology reasoning with OWL and management events are built through the ontology-building tool. Jena is an open source program RDFS as ontology description language [53,54]. Thus, the Jena framework is used to design and development framework, which provides a powerful semantic ontology reasoning with OWL and implement the semantic reasoning and event information extraction based on the ontology. RDFS as ontology description language [53,54]. Thus, the Jena framework is used to design and implement the semantic reasoning and event information extraction based on the ontology.


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Social comprehensive management event log

Traverse log

Check attribute field

Abandon the log

No

No

Field is correct and complete?

Yes

Is it repairable? Yes Attribute repair

New database match

Yes

Is it repeated?

No Data storage Figure 3. Quality management processes for urban social management events. Figure 3. Quality management processes for urban social management events.

2.2.1. Conceptual Architecture Diagram of USMEs Ontology 2.2.1. Conceptual Architecture Diagram of USMEs Ontology The ontology conceptual system of the event type is the foundation of ontology construction. The ontology conceptual system of the event type is the foundation of ontology construction. This study divides urban social management events (USMEs) by the types and their conceptual This study divides urban social management events (USMEs) by the types and their conceptual architecture of ontology types (Figure 4). architecture of ontology types (Figure 4). Figure 4. shows that urban social management event types can be divided into the following: Figure 4 shows that urban social management event types can be divided into the population management, public security, service of livelihood, infrastructure maintenance, following: population management, public security, service of livelihood, infrastructure maintenance, environmental hygiene management, urban management, and dispute resolution. Among these environmental hygiene management, urban management, and dispute resolution. Among these types, population management, public security, public service, infrastructure maintenance, and types, population management, public security, public service, infrastructure maintenance, environmental hygiene management have broad categories of events, and it can be divided into many and environmental hygiene management have broad categories of events, and it can be divided subclasses of events. Moreover, each subclass can contain a large number of entities. Given the into many subclasses of events. Moreover, each subclass can contain a large number of entities. diversity of social structure, the conflicts of disputes occur frequently. Thus, the dispute mediation Given the diversity of social structure, the conflicts of disputes occur frequently. Thus, the dispute events are listed separately as the focus of the event. mediation events are listed separately as the focus of the event.


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Garbage can Power outages Tear torn wire disorderly Steal electricity High-pressure system

Road maintenance

Communication base station

Street light damage

Post office box Communication pipeline

Electric tower Electrical safety

Satellite receiver

Wiring aging

ray of light

……

Road potholes

……

Power supply system

Square health

Water conservation

Five promises front of shop

Water supply network

Overloaded vehicle

Sewage treatment

Overloaded vehicle

River cleanning

Garbage classification

Traffic facility

Afforest

Illegal building

Construction waste

Roadside stall business

Haze

Notice board

Mobile stall

Air odor

City Appearance

Vehicle Exhaust

Adlet

Crop straw burning

Large-scale activity

……

Atmospheric dust

……

Water-related affair

……

Hygiene

Infrastructure maintenance

Family conflict

Factory fumes

Road cleanning

Life sewage

refuse processing plant

……

Communication facility

Sewage

Steal water

Public phone booth

Homestead dispute

Violation of breeding

Square health

Water failure

Air

Neighborhood dispute Medical dispute Labor dispute

Forbidden object

Contract disputes

Commodity spot check

Land dispute

Food stall

Traffic accident dispute

…… Entertainment venue

Property dispute

……

……

City management

Dispute resolution

……

Environmental hygiene management

Social manegement event

Population management

Service of livelihood

Public security

Household management

Personnel flow

Daily stability

Public place management

Dangerous goods management

Heresy control

Social insurance

Certificates service

Education service

Else

Household registration change

Tourism

Idle personnel control

Entertainment venue

Fireworks and crackers

Falun gong

FiveGuarantees

Permanent residence registration

Kindergarten

Consultation

Birth control work

Family visit

Square

Inflammable liquid

Almighty god

Minimum living standard

Identity card

Primary school

Certificates service

Toxic material

The disciples would

Low-cost housing service

Birth control

Middle school

On-site repair

Professional education

Agricultural popular science

Special education

Disabled people

Laid-off workers education

Film to the countryside

Marriage registration

Departure and entry

Job dispatch

Foreigner

……

Migrant worker ……

Monitor the handling of complaint reporting

Ballroom

Medical disturbance

Cinema

Law litigant

Traffic situation

Visit the full release of prisoners ……

Stadium

Controlled knife Radioactive substance ……

……

Huhan faction New testament mission ZhuShenjiao ……

Aging rescue

Marriage registration

Double-laid-off reemployment

Adoption registration

Disabled People

Medical insurance procedure

rural medical security

Low income certification

……

……

library ……

Art to the countryside ……

Figure 4. Conceptual Architecture Diagram of USMEs. Figure 4. Conceptual Architecture Diagram of USMEs.

2.2.2. Ontology Expression and Modeling of USMEs 2.2.2. Ontology Expression and Modeling of USMEs According to the ontology concept model of urban social management, the present study adopts According the ontology model of urban socialmodel management, the present study adopts a five-element to group to expressconcept the ontology. The ontology of the five-tuple model (TGDO ) of athe five-element group to express the ontology. The ontology model of the five-tuple model (T GDO ) of urban social management event type is defined as Equation (1)): the urban social management event type is defined as Equation (1)): (1) =< , , , , >, TGDO = < TCc, TR, TP, TCs, TI >, (1) where TCc are the type concepts that represent a collection of event types; where TR are the type relations that represent a relationship collection of event types between concept TCc are the type concepts that represent a collection of event types; and concept, concept and instance, and instance and instance; TR are the type relations that represent a relationship collection of event types between concept TP are the type properties that represent the relationship attribute of the event type and its data and concept, concept and instance, and instance and instance; attribute, such as the relationship attribute between “complaint reporting and handling” and “daily maintenance”;


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TP are the type properties that represent the relationship attribute of the event type and its data attribute, such as the relationship attribute between “complaint reporting and handling” and “daily maintenance”; Sustainability 2018, 10, x FOR PEER REVIEW 8 of 24 TCs is the type constraint that represents the constraint set of the event ontology, including the value type ofisthe range, and base, such that the event coordinates cannot be outside TCs theproperty, type constraint that represents the constraint set of the event ontology, including the the research area; value type of the property, range, and base, such that the event coordinates cannot be outside the TI are the type individuals that represent the instances of the event type ontology, that is, specific research area; TI are the type individuals that represent the instances of the event type ontology, that is, specific to individual events. to individual events. According to the original language of the five-tuple event ontology, ontology modeling can to the original languagetreatment of the five-tuple event ontology, ontology modeling be be carriedAccording out for urban social variety events, which include conceptual setcan modeling, carried out for urban social variety treatment events, which include conceptual set modeling, conceptual relation modeling, and semantic relationship modeling. conceptual relation modeling, and semantic relationship modeling. This study adopts the open source ontology construction tool Protégé and introduces the idea This study adopts the open source ontology construction tool Protégé and introduces the idea of “incremental development” ofofsoftware Thisstudy study proposes a modular seven-step of “incremental development” softwareengineering. engineering. This proposes a modular seven-step method ontology construction method whichdivides dividesthe the social variety subdomains method ontology construction method(Figure (Figure 5), 5), which social variety intointo subdomains fromfrom top to bottom. Each sub-domain corresponds to a sub-body. The sub-ontology is nested top to bottom. Each sub-domain corresponds to a sub-body. The sub-ontology is nested with with multiple sub-bodies, andand thethe total body fromthe thesub-bodies sub-bodies of multiple modules. multiple sub-bodies, total bodyisispieced pieced together together from of multiple modules. Define the ontology domain of urban social management(USM)

Ontology synthesis

Create an instance Is there known ontology in this domain?

Yes

No

Defines the attribute constraints of a class

List the main concepts in the field of USM

Ontology module division

Defines the properties of the classes

Defines the hierarchical structure of classes and classes Figure 5. ConstructionProcess Process of of Urban Events. Figure 5. Construction UrbanSocial SocialManagement Management Events.

As Figure 5 shows, the ontology contraction process of USME includes seven steps. Firstly, the

As Figure 5 shows, the should ontology contraction includes seven steps. is Firstly, ontology domain of USM be defined. If the process ontologyof in USME this domain existed, an instance the ontology domain of USM should be defined. If the ontology this domain existed, instance created based on the existent ontology for the following ontologyinsynthesis. Otherwise, theanbasic is created based on the existent ontology forbethe followingincluding ontologyclasses, synthesis. Otherwise, the basic elements of the ontology in USM should constructed hierarchical structure among classes, properties of classes constraints of certain etc. In the ontology elements of the ontology in USM shouldand be attribute constructed including classes,classes hierarchical structure among modeling process, ontology module division attempts to consider the decoupling between modules classes, properties of classes and attribute constraints of certain classes etc. In the ontology modeling andontology divides the event and its description multiple modules, which can be combined intoand sets divides of process, module division attemptsinto to consider the decoupling between modules ontology, such as “site ontology” (place name description ontology and coordinate ontology). The the event and its description into multiple modules, which can be combined into sets of ontology, attributes of the classes are mainly divided into two categories: Data attributes and object attributes, such as “site ontology” (place name description ontology and coordinate ontology). The attributes of which mainly include the description attributes of the concept of the social management event and


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the classes are mainly divided into two categories: Data attributes and object attributes, which mainly include the description attributes of the concept of the social management event and the relationship Sustainability 2018, 10, x FOR PEER REVIEW 9 of 24 between concepts and concepts. Attribute constraints are mainly limited to conceptual attributes, such the as the range of values andconcepts date accuracy. The openAttribute source ontology software Protégé [53–57] relationship between and concepts. constraints are mainly limited to with OWLconceptual 2 is presented for the ontology construction for the variety ontology creation of the urban attributes, such as the range of values and date accuracy. The open source ontologysocial software Protégé [53–57] with OWL 2 is presented for the ontology construction for the variety treatment event. ontology creation of the urban social treatment event.

2.2.3. Ontology Semantic Reasoning of USMEs 2.2.3. Ontology Semantic Reasoning of USMEs

The technical framework of ontology semantic reasoning for USMEs is shown as Figure 6. The technical framework of ontology semantic reasoning for USMEs is shown as Figure 6. Input a hot event

Protégé ontology file

Events database

Traverse the database Extract the event description text

Ontology reasoning engine

Jane TDB persistence

OWL reasoning rules

Ontology model

A reasoning engine that supports reasoning Semantic reasoning

Output the result Figure 6. Flowchart of ontology reasoning of USMEs.

Figure 6. Flowchart of ontology reasoning of USMEs.

Figure 6 shows that ontology semantic reasoning is based on the Jane framework and Protégé software, which provides rich conceptual as “functional”, Functional”, Figure 6 shows that ontology semanticrelationships, reasoning issuch based on the Jane “Inverse framework and Protégé “Inverse of”, provides “Transitive, Symmetric”, “Reflexive” and “Irreflexive” [53–57]. The inference are software, which rich conceptual relationships, such as “functional”, “Inverserules Functional”, based on the reasoning function of “Transitive, Symmetric and Irreflexive” to describe the “Inverse of”, “Transitive, Symmetric”, “Reflexive” and “Irreflexive” [53–57]. The inference rules are relationship between social management events. The inference system first reads and parses the based on the reasoning function of “Transitive, Symmetric and Irreflexive” to describe the relationship ontology files of Protégé and analyzes the classes, instances, and various attributes of the ontology between social management events. The inference system first reads and parses the ontology files of model by using Jena TDB [58] to persist the ontology file, combining the ontology relationship of Protégé and analyzes the instances, and various the ontology model by using OWL 2 description, andclasses, constructing an ontology inferenceattributes engine thatof supports semantic reasoning. Jena TDB [58] to persist the ontology file, combining the ontology relationship of OWL 2 description, The extraction of a hot event is taken as an example as follows: A hot event, such as “conflict dispute” and constructing ontology inference engine supports semantic reasoning. The extraction of a hot is entered, theandatabase is traversed, and thethat description text of the event is extracted. The semantic for the ontologyAreasoning extract events from the source data with the eventrelation is takenisaspresented an example as follows: hot event,tosuch as “conflict dispute” is entered, the database support and of the ontology inference Protégé software, thesemantic hot eventsrelation are matched with each is traversed, the description text engine of the and event is extracted. The is presented for the data to obtain all the matching results. The key algorithm of the semantic reasoning of USMEs based ontology reasoning to extract events from the source data with the support of the ontology inference on and the Jena framework is shown in Algorithm 1. matched with each data to obtain all the matching engine Protégé software, the hot events are results. The key algorithm of the semantic reasoning of USMEs based on the Jena framework is shown in Algorithm 1.

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Algorithm1 1Ontology Ontology Reasoning of USMEs Algorithm Reasoning of USMEs Rule = GetRuleFile(url);

Rule = GetRuleFile(url); Loop Loop rules==Rule.ParseRules( Rule.ParseRules( rules ) ) EndLoop Loop End Definereasoner reasoner Define Loop Loop Create = CreateRuleModel(reasoner,rules) Createrulemodels rulemodels = CreateRuleModel(reasoner,rules) End EndLoop Loop Loop Loop Results Results= =QueryByRule(rulemodels) QueryByRule(rulemodels) End Loop End Loop OutputFormatResult(Results)

OutputFormatResult(Results)

2.3. 2.3. Spatio-Temporal Spatio-Temporal Data Data Mining Mining with with USMEs USMEs A A relationship relationship is is observed observed between between the the spatial spatial distributions distributions of of the the comprehensive comprehensive management management of social events. For example, social security may not be good in areas where disputes are frequent, frequent, of social events. For example, social security may not be good in areas where disputes are and elderly aid events are relatively concentrated in poor or rural areas. This study explores and elderly aid events are relatively concentrated in poor or rural areas. This study explores the the temporal of urban urban social social management management events events from from three three aspects aspects of of “event-place,” “event-place,” temporal and and spatial spatial patterns patterns of “event-event,” “event-event,”spatial spatialrelevance, relevance,and and“event-event” “event-event”time timerelevance. relevance. 2.3.1. “Event-Place” Spatial Correlation 2.3.1. “Event-Place” Spatial Correlation We present in this paper the clustering center method and the direct distance method to explore We present in this paper the clustering center method and the direct distance method to explore the correlation between the events and the designated locations. the correlation between the events and the designated locations. (1) Cluster center method (1) Cluster center method The basic idea of clustering center method is as follows: The basic idea of clustering center method is as follows: Firstly, the point density analysis method obtained a plurality clustering center of the event. Firstly, the point density analysis method obtained a plurality clustering center of the event. The The Kernel density estimation [59,60] is presented for the point density analysis to obtain the clustering Kernel density estimation [59,60] is presented for the point density analysis to obtain the clustering center. The point density analysis of one event is as Figure 7 shows. center. The point density analysis of one event is as Figure 7 shows.

x6 x5 Appointed Position O

x1

x4 x2

x3

Figure 7. 7. “Event-Place” correlation analysis analysis based based on on Cluster Cluster Center Center Method. Method. Figure “Event-Place” correlation

As Figure 7 shows, the event has six cluster centers within the range O. As Figure 7 shows, the event has six cluster centers within the range O. Secondly, calculate the sum of the weighted distance reciprocal between the cluster center and the specified location. In Figure 7, the weighted distance reciprocal of the place O to the clustering center (X1, X2, X3, X4, X5, X6) are added up.


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Secondly, calculate the sum of the weighted distance reciprocal between the cluster center and the specified location. In Figure 7, the weighted distance reciprocal of the place O to the clustering center Sustainability 2018, 10, x FOR PEER REVIEW 11 of 24 (X 1 , X2 , X3 , X4 , X5 , X6 ) are added up. Thirdly, the average weighted distance reciprocal is taken as the clustering center spatial Thirdly, the average weighted distance reciprocal is taken as the clustering center spatial correlation discriminant factor h, as Equation (2): correlation discriminant factor h, as Equation (2): n µ n } = ϕ ∑μ i i ,  = ϕ i=1 Di,

D i =1

(2) (2)

i

where n is the number of cluster centers, Di is the distance of O to the cluster center Xi , µi is the weight where n is the number of cluster centers, Di is the distance of O to the cluster center Xi, μi is the weight of Xi , which is determined by the density factor. ϕ is the correction factor. The discriminant factor of Xi, which is determined by the density factor. φ is the correction factor. The discriminant factor increases, the correlation is stronger. increases, the correlation is stronger. (2) Direct distance clustering method (2) Direct distance clustering method Different from the cluster center method, the direct distance clustering method omits the event Different from the cluster center method, the direct distance clustering method omits the event clustering, which takes the distance from the event to the site as the direct factor. The basic idea is clustering, which takes the distance from the event to the site as the direct factor. The basic idea is as as follows: First, set a suitable location for the center of the screen space, screening at all points in follows: First, set a suitable location for the center of the screen space, screening at all points in space; space; then, calculate the distance to draw distance, as the discriminant factor correlation. As shown in then, calculate the distance to draw distance, as the discriminant factor correlation. As shown in Figure 8. Figure 8.

Position O

Figure 8. “Event-Place” correlation analysis based on Direct Distance Clustering Method. Figure 8. “Event-Place” correlation analysis based on Direct Distance Clustering Method.

The discriminant factor of the direct distance clustering method h is calculated by Equation (3): The discriminant factor of the direct distance clustering method h is calculated by Equation (3): n

ϕ

 = n ϕ, } =i =∑ 1 Di , i =1

Di

(3) (3)

where n is the number of events in the filter space, Di is the distance from the ith event to the location where of events O, andnφisisthe thenumber correction factor.in the filter space, Di is the distance from the ith event to the location O, and ϕ is the correction factor. 2.3.2. “Event-Event” Spatial Correlation 2.3.2. “Event-Event” Spatial Correlation “Event-event” spatial correlation can be used to explore the inducement and accompanying “Event-event” spatial correlation can be used to explore the inducement and accompanying relationships of events from the perspective of spatial distribution. For example, air pollution and relationships of events from the perspective of spatial distribution. For example, air pollution sewage discharge have some kind of association, most of them are caused by factory sewage. By and sewage discharge have some kind of association, most of them are caused by factory sewage. comparing the correlation between floating population events and social security incidents, we can By comparing the correlation between floating population events and social security incidents, we can explore the impact of population mobility on social security. The idea of exploring the spatial explore the impact of population mobility on social security. The idea of exploring the spatial correlation of “event-event” space is shown in Figure 9. correlation of “event-event” space is shown in Figure 9.


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Events Selection: A and B Obtaining multiple cluster centers of A and B based on Point density Approach Computing the weight of cluster center with density Calculating correlation factor of each single point Taking Event A as reference

Calculating Average correlation factor of all cluster centers (Event A) Figure Figure 9. 9. Flowchart Flowchart of of the the spatial spatial correlation correlation exploring exploring between between “event-event”. “event-event”.

Figure 99 shows shows that that the the “event-event” “event-event” spatial spatial correlation correlation analysis analysis algorithm algorithm includes includes the the Figure following steps: steps: following Step 1: 1: Two Two kinds kinds of of events events involved involved in in the the evaluation evaluation are are clustered clustered by by the the density density method method to to Step generate the the clustering clustering centers centers of of two two kinds kinds of of events. events. generate Step 2: 2: Each Each cluster cluster center is assigned assigned aa weight weight that that matches matches the the density density value value according according to to the the Step density factor. The higher the density factor is, the greater the weight is. density factor. The higher the density factor is, the greater the weight is. Step 3: 3: The clustering centers of another event are traversed on the basis of one kind of event. Step The sum sum of of the the mean mean weighted weighted distance distance reciprocal reciprocal in in the the neighborhood neighborhood space space of of the the datum datum cluster cluster The center is is calculated calculated with with the the specific specific size size of ofthe theneighborhood neighborhoodspace. space. center Step 4: 4: The clustering centers centers is is Step The average average weighted weighted distance distance by the reciprocal sum of all clustering accumulated, and the average value is calculated as a correlation measurement factor (Equation (4)). accumulated, and the average value is calculated as a correlation measurement factor (Equation (4)). A B B ) ϕϕ  11 mmi i (µμiAi ++µμ j j } = = ∑   ∑ i,j , , nn i=i =11 mmi ij=j1=1 DD i, j   n n



(4) (4)

where is the the number number of of ith ith clustering clustering center center in in A A where nn is is the the clustering clustering center center number number of of events events A, A, m mii is events, which are in the neighborhood of the clustering center in events B, D is the event clustering ij events, which are in the neighborhood of the clustering center in events B, Dij is the event clustering center and jthjth thethe clustering center of of events B, µB,iA is center distance distancebetween betweenith iththe thecluster clustercenter centerofofevents eventsA A and clustering center events the weight of ith center of events A, andA,µiBand is the weight jth clustering center of center events of B. is the weight of clustering ith clustering center of events is the of weight of jth clustering ϕ is the correction factor. events B. φ is the correction factor. Figure Figure 10 10 gives gives an an example example of of “event-event” “event-event” spatial spatialcorrelation correlationanalysis. analysis.


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Sustainability Sustainability2018, 2018,10, 10,xxFOR FORPEER PEERREVIEW REVIEW

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Figure correlation between “event-event”. Figure10. 10.Example Exampleof ofexploring exploringthe thespatial spatialcorrelation correlationbetween between“event-event”. “event-event”. Figure 10. Example of exploring the spatial

Figure Figure10 10shows showsthat thatevent eventAAisisused usedas asthe thebasis. basis.Event EventBBisisthe theobject objectto tobe betraversed. traversed.At Atfirst, first, Figure 10 shows that event A is used as the basis. Event B is the object to be traversed. At first, A1 A1isistaken takenas asthe theclustering clusteringcenter; center;thus, thus,all allevent eventB’s B’sin inthe theneighborhood neighborhoodspace spaceof ofA1 A1are aretraversed traversed A1 is taken as the clustering center; thus, all event B’s in the neighborhood space of A1 are traversed to to tocalculate calculatethe thecorrelation correlationfactor factoras asthe thepresented presentedalgorithm. algorithm.The Thecalculation calculationfor forA2, A2,A3, A3,A4, A4,A5, A5,and and calculate the correlation factor as the presented algorithm. The calculation for A2, A3, A4, A5, and all all allA’s A’sare aretransversed, transversed,until untilall allevent eventA’s A’sare aretraversed. traversed. A’s are transversed, until all event A’s are traversed. 2.3.3. Time Correlation Analysis 2.3.3.“Event-Event” “Event-Event”Time TimeCorrelation CorrelationAnalysis Analysis 2.3.3. “Event-Event” The time correlation between events isisto to explore the succession, concomitant, and incentive Thetime timecorrelation correlationbetween betweenevents eventsis toexplore explorethe thesuccession, succession,concomitant, concomitant,and andincentive incentive The relationship of events from the perspective of time distribution. The time correlation between events relationshipof ofevents eventsfrom fromthe theperspective perspectiveof oftime timedistribution. distribution.The Thetime timecorrelation correlationbetween betweenevents events relationship depends on spatial correlation. Only if the events have spatial correlation, is the time correlation of depends on spatial correlation. Only if the events have spatial correlation, is the time correlation of depends on spatial correlation. Only if the events have spatial correlation, is the time correlation of events meaningful. For example, it is assumed that there is a periodic correlation between the sewage events meaningful. For example, it is assumed that there is a periodic correlation between the sewage events meaningful. For example, it is assumed that there is a periodic correlation between the sewage discharge events in Wuhan and the environmental sanitation events in dischargeevents eventsin inWuhan Wuhan and the environmental sanitation events inQingdao, Qingdao, butthis thisrelevance relevance discharge and the environmental sanitation events in Qingdao, butbut this relevance has has no significance, because Wuhan and Qingdao are too far apart in space, and the sewage discharge has no significance, because Wuhan and Qingdao are too far apart in space, and the sewage discharge no significance, because Wuhan and Qingdao are too far apart in space, and the sewage discharge in in little impact environmental sanitation of inWuhan Wuhan has little impact onthe the environmental sanitation ofQingdao. Qingdao. Wuhan hashas little impact on on the environmental sanitation of Qingdao. A flowchart to explore the time correlation between events isisshown shown in Figure 11. A flowchart to explore the time correlation between events shownin inFigure Figure11. 11. A flowchart to explore the time correlation between events is

Divide Divideevents eventsby bytime time

Count Countthe thenumber numberofofevents eventsinineach each period of time period of time Calculate Calculatethe therate rateof ofchange changebetween between time timeperiods periods Displays Displaysresults resultswith withaaline linechart chart Figure Figure11. 11.Flowchart Flowchartof of“Event-Event” “Event-Event”Time TimeCorrelation CorrelationAnalysis. Analysis.

Figure 11 shows the following. certain month or Figure11 11shows showsthe thefollowing. following.First, First,according accordingto toaaacertain certaintime timeinterval, interval,such suchas asthe themonth monthor or Figure First, according to time interval, such as the week, the number of events is counted within each time period. Second, the increase in the number week,the thenumber numberof ofevents eventsisiscounted countedwithin withineach eachtime timeperiod. period.Second, Second,the theincrease increasein inthe thenumber number week, of events is used as a measure of standards to calculate the event gain in the adjacent period. Finally, of events is used as a measure of standards to calculate the event gain in the adjacent period. Finally, of events is used as a measure of standards to calculate the event gain in the adjacent period. Finally, the change between the two events and visualization is compared. The time correlation between the the change between the two events and visualization is compared. The time correlation between the the change between the two events and visualization is compared. The time correlation between the events can be measured. eventscan canbe bemeasured. measured. events In the “Event-Event” time exploring analysis, the calculation scheme of time interval In the “Event-Event” timecorrelation correlationexploring exploringanalysis, analysis,the thecalculation calculationscheme schemeof oftime timeinterval interval In the “Event-Event” time correlation and increase is an important factor that affects new exploration. The event interval must be chosen andincrease increaseisisan animportant importantfactor factorthat thataffects affectsnew newexploration. exploration.The Theevent eventinterval intervalmust mustbe bechosen chosen and according accordingto tothe theperiodic periodicnature natureof ofthe theevent eventitself. itself.An Anincrease increasein inevents eventsover overtime timecan canbe becalculated calculated by by the the quantity quantity of of the the current current unit unit of of time time and and the the number number of of units units of of the the previous previous time, time, as as the the standard of gain. You can also choose the sum of the quantities of the previous two or three time standard of gain. You can also choose the sum of the quantities of the previous two or three time


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according to the periodic nature of the event itself. An increase in events over time can be calculated by Sustainability 2018, 10, x FOR PEER REVIEW 14 of 24 the quantity of the current unit of time and the number of units of the previous time, as the standard of gain. can also indicator. choose theConsidering sum of the quantities of the previous two or units the units asYou the uptrend the possibility that the number of three eventstime is zero forassome uptrend indicator. Considering the possibility that the number of events is zero for some time period, time period, it is appropriate to select the sum of the events in the first two or three time periods as itdivisors. is appropriate to select the sum of the events in the first two or three time periods as divisors. 3. Results 3. Results 3.1. Dataset 3.1. Dataset The experimental data is the USMEs of Jiaonan city and Huangdao District in Qingdao City, The experimental data is the USMEs of Jiaonan city and Huangdao District in Qingdao City, Shandong Province, China. The time spans are from 22 August 2014 to 8 February 2017. There are Shandong Province, China. The time spans are from 22 August 2014 to 8 February 2017. There are 2,162,302 events data in total (Figure 12). These USMEs are from Qingdao City Management System, 2,162,302 events data in total (Figure 12). These USMEs are from Qingdao City Management System, an actual running system in the government of Jiaonan City and Huangdao Distrcit. an actual running system in the government of Jiaonan City and Huangdao Distrcit.

Figure 12. Dataset of USMEs for the experiment. Figure 12. Dataset of USMEs for the experiment.

Figure 12 shows that the events are aggregated and scattered in the spatial distribution. Figure reflected 12 shows the isevents aggregated and scattered in the isspatial distribution. Dispersion in that the data full of are the entire research space. Aggregation reflected in the data Dispersion reflected in the data is full of the entire research space. Aggregation is reflected in the data within the scope in different scales of space gathered. withinFigure the scope different scales of space gathered. 13 isinthe satellite remote sensing images (from IKNOS with resolution of 1 m) in the Figure 13 is the satellite remote sensing images (from IKNOS with resolution of 1 m) in the experiment region. experiment region.


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Figure 13. Satellite remote sensing images in the experiment region. Figure 13. Satellite remote sensing images in the experiment region.

Figure 13 shows the events of large-scale aggregation of the area for the city and the small-scale Figure 13 shows the events large-scale aggregation the area for and the city and the District small-scale gathering place for rural areas. of The experimental area of of Jiaonan City Huangdao is a gathering for The ruralterritory areas. The experimental of Jiaonan City and Huangdao District is aa hilly-basedplace region. of large and small area mountains of human activities are divided into hilly-based region. The territory of large and small mountains of human activities divided into a large number of scattered large and small areas. The whole study area has severalare highly populated large number of scattered large and small areas. The whole study area has several highly populated urban areas and a large number of scattered rural areas. This regional division led to a pluralistic urban a large number of scattered ruralrural areas.elements. This regional to a pluralistic study study areas area and of society with both urban and The division types ofled social comprehensive area of society with both urban and rural elements. The types of social comprehensive management management events are relatively rich and representative. events are relatively rich and representative. 3.2. Hot Events Extraction 3.2. Hot Events Extraction This study includes urban and rural study areas. The elements of social management are more This study includes and ruralThe study areas. ontology-based The elements ofsemantic social management are more highly complicated than urban modern cities. proposed reasoning method is highly complicated than modern cities. The proposed ontology-based semantic reasoning method is used to extract disputes of hot events and spatial statistics in the district’s grid, and statistical results used to extract disputes of hot events andPlot) spatial statistics in the district’s statistical are shown using a boxplot (Box Whisker and scatter diagram (Scattergrid, Plot)and (Figure 14). results are shown using a boxplot (Box Whisker Plot) and scatter diagram (Scatter Plot) (Figure 14). Figure 14 shows that, in most of the district, the disputes events are not frequent and are below Figure 14 shows that, in the district, East the disputes events are not frequent and are below the average. However, in most the ofChangjiang Road Management Zone, Wanggezhuang the average. However, in the Changjiang East Road Management Zone, Wanggezhuang Management Zone, and Paifang Street Management Zone, the activities frequencyManagement of the three Zone, and Paifang Street Management Zone, the activities frequency of the three management zones management zones are excessively dense, which are dozens of times of the average and significantly are excessively whichThe aredata dozens of times of the average and significantly higher thanZone the higher than the dense, other areas. shows that the number of Wanggezhuang Management other areas. The data shows that the number of Wanggezhuang Management Zone is 27 times is 27 times of the average. The number of the Changjiang East Road Management Zone is 22 times of of the average. The number of the Changjiang East Road Management Zone is 22 times of the average. the average. The number of Paifang Street Management Zone is 18 times of the average. Therefore, The number of Paifang Street Management Zone 18 times of the average. Therefore, disputedispute events dispute events have clustering characteristics in isspatial distribution. In most of the district, have clustering characteristics in spatial distribution. In most of the district, dispute events are events are not active. In individual areas, disputes are slightly hot events that frequently occur innot the active. In individual areas, disputes are slightly hot events that frequently occur in the Changjiang Changjiang East Road Management Zone, Wanggezhuang Management Zone, and Paifang Street East Road Management Zone, Management Zone, and Paifang Street Management Management Zone, which haveWanggezhuang extremely frequent dispute events. Zone, which have extremely frequent dispute events.


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Wanggezhuang Management Zone Wanggezhuang Management Zone Changjiang East Road Management Changjiang East Zone Road Management Zone Paifang Street Management Zone Paifang Street Management Zone

Wanggezhuang Management Zone Wanggezhuang Management Zone Changjiang East Road Management Changjiang East Zone Road Paifang Street Management Management Zone Zone Paifang Street Management Zone

(a) Box Whishker Plot

(b) Scatter Plot

(a) Box Whishker Plot

(b) Scatter Plot

Figure 14. Spatial distribution of “Dispute” Hot Events. Figure 14. Spatial distribution of “Dispute” Hot Events. Figure 14. Spatial distribution of “Dispute” Hot Events.

Moreover, the number of monthly conflicts and disputes are counted by month, and the result Moreover, the number of monthly conflicts and disputes are counted by month, and the result is is shown in Figure Moreover, the 15. number of monthly conflicts and disputes are counted by month, and the result shown in Figure 15. is shown in Figure 15.

700

Variance of Dispute Events by Time Variance of Dispute Events by Time

700 600 600 500 500 400 400 300 300 200 200 100

0

2014/8 2014/8 2014/9 2014/9 2014/10 2014/10 2014/11 2014/11 2014/12 2014/12 2015/1 2015/1 2015/2 2015/2 2015/3 2015/3 2015/4 2015/4 2015/5 2015/5 2015/6 2015/6 2015/7 2015/7 2015/8 2015/8 2015/9 2015/9 2015/10 2015/10 2015/11 2015/11 2015/12 2015/12 2016/1 2016/1 2016/2 2016/2 2016/3 2016/3 2016/4 2016/4 2016/5 2016/5 2016/6 2016/6 2016/7 2016/7 2016/8 2016/8 2016/9 2016/9 2016/10 2016/10 2016/11 2016/11 2016/12 2016/12 2017/1 2017/1 2017/2 2017/2

1000

Figure 15. Time series characteristics of “dispute” events. Figure 15. Time series characteristics of “dispute” events.

Figure 15. Timethe series characteristics “dispute” events. Figure 15 shows that, although “dispute” eventsofdid not show cyclical characteristics, the “dispute” in thethat, distribution time are not uniform, and theshow difference is large. April 2015the to Figureevents 15 shows althoughofthe “dispute” events did not cyclical characteristics, Figure 15 shows that, although the “dispute” events did not show cyclical characteristics, August 2016 is a frequent time period for contradictions and disputes. “dispute” events in the distribution of time are not uniform, and the difference is large. April 2015 to the “dispute” events in the distribution of time are not uniform, and the difference is large.ofApril 2015 Figure gives the thermodynamic diagram of the spatial focusing characteristic “dispute” August 201616 is a frequent time period for contradictions and disputes. to August 2016 is a frequent time period for contradictions and disputes. events, and 16 Figure is the spatial distribution change “dispute” eventscharacteristic in different years. Figure gives17the thermodynamic diagram of theofspatial focusing of “dispute” Figure 16 gives the thermodynamic diagram of the spatial focusing characteristic of “dispute” events, and Figure 17 is the spatial distribution change of “dispute” events in different years. events, and Figure 17 is the spatial distribution change of “dispute” events in different years.


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Cold Cold

Huangdao Huangdao City City

Jiaonan Jiaonan City City Hot Hot

Figure 16. Thermodynamic diagram of the spatial focusing characteristic of “dispute” events.

Figure 16. Thermodynamic diagram of the spatial focusing characteristic of “dispute” events. Figure 16. Thermodynamic diagram of the spatial focusing characteristic of “dispute” events.

Cold

Cold

Cold

Cold

Hot

Hot

Hot

Hot

(a) Spatial distribution of“dispute”events (2015)

(b) Spatial distribution of“dispute”events (2016)

(b) Spatial distribution of“dispute”events (2016) (a) Spatial distribution of“dispute”events Figure 17. Changes (2015) of “dispute” events in different years. Figure 17. Changes of “dispute” events in different years.

Figures 16 and 17 show17. oneChanges large gathering area and oneinsmall gathering Figure of “dispute” events different years.area in the study area of “dispute” events, which are the main urban areas of Jiaonan City and Huangdao City. Jiaonan City Figures 16 and 17 show one large gathering area and one small gathering area in the study area has one large and three small gathering areas, which are in the neighborhood of Wanggezhuang of “dispute” events, which arelarge the main urban areas of Jiaonan City and Huangdao City. City Figures 16 and 17 show gathering one small gathering area in Jiaonan the study Management Zone. The one “dispute” events havearea the and distribution characteristics of gathering in thearea of has one large and three small gathering areas, which are in the neighborhood of Wanggezhuang “dispute” which areactivity the main urban areas of other Jiaonan City and Huangdao City. Jiaonan mainevents, urban areas. The is not high although rural areas have some “dispute” events. City Management Zone. The “dispute” events have the distribution characteristics of gathering in the has one large and three small gathering areas, which are in the neighborhood of Wanggezhuang main urban areas. The activity is not high although other rural areas have some “dispute” events.

Management Zone. The “dispute” events have the distribution characteristics of gathering in the main urban areas. The activity is not high although other rural areas have some “dispute” events. Thus, the main manpower of the city’s administration should be arranged in the main city, especially the main city of Jiaonan City and the neighborhood of Wanggezhuang Management Zone.


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Thus, the main manpower of the city’s administration should be arranged in the main city, especially the main city of Jiaonan City and the neighborhood of Wanggezhuang Management Zone.

3.3. Spatial Distribution Correlations Analysis Result of “Events-Places” 3.3. Spatial Distribution Correlations Analysis Result of “Events-Places”

“Petition” events are used as examples for the spatial distribution correlations analysis of “Petition” events are used as examples for the spatial distribution correlations analysis of “events-places.” Figure 18 gives the result of the spatial distribution correlations analysis with the “events-places.” Figure 18 gives the result of the spatial distribution correlations analysis with the proposed cluster center method. proposed cluster center method.

Figure 18. Spatial distribution correlations analysis of “Petition” events from the aspect of “events-

Figure 18. Spatial distribution correlations analysis of “Petition” events from the aspect of “events-places”. places”.

Figure 18 shows the spatial correlations of Langya Terrace, Langyatown, town,big bigtown, town,and andsome Figure 18 shows that that the spatial correlations of Langya Terrace, Langya some other places with events of visiting are preliminarily high. Table 1 gives correlation factors of other places with events of visiting are preliminarily high. Table 1 gives correlation factors of “Petition” “Petition” events in all places. events in all places. Table 1. Correlation factors of “Petition” events in all places.

Table 1. Correlation factors of “Petition” events in all places. Correlation Place Factor Correlation Langya Town 102.135 Liuwang Place Place Town Factor Langya Scenic District 98.217 Zhangjialou Town Langya Town 102.13551.284 Liuwang Town Poli Town Zangnan Town Langya Scenic District 98.21710.341 Zhangjialou Dachang Town DazhushanTown Town Poli Town 51.284 Zangnan Town Dazhushan Scenic Haiqing Town Dachang Town 10.341 0.956 Dazhushan Town District Place

Haiqing Town Dacun Town Dacun Town Liwuguan Town Town Liwuguan

0.956 Dazhushan Scenic District Jiaonan City 44.27744.277 Jiaonan City 1.757 1.757 Yinzhu Town Yinzhu Town

Correlation Factor Correlation 0.073 Factor 0.215 0.073 0.496 0.215 0.408

0.496 0.408 0.384 5.006 5.006 24.472 24.472 0.384

Correlation Factor Correlation Baoshan Town 0.124 Place Factor Wangtai Town 0.211 Baoshan Huangdao CityTown 8.612 0.124 Wangtai Town 0.211 Lingshan Town 2.4 Place

Huangdao City 3.173 Lingshan Town Hongshiya Town Jinshatan Scenic Jinshatan Scenic District4.626 District

Hongshiya Town

8.612 2.4 3.173 4.626

Table 1 and Figure 18 show that “Petition” events in two places have super spatial distribute

Table 1 and Figure 18 show that “Petition” events in two places have super spatial distribute correlations: Langya Town and the Scenic District. Boli Town and Dacun Town have strong relevance correlations: Langya Town and the Scenic District. Boli Town and Dacun Town have strong relevance of spatial distribution, while the relevance of other places is weak. After field investigation, the of spatial distribution, while the relevance other such places weak. After field investigation, theand reasons reasons of “Petition” are similar in theseofplaces, as is house demolition, industrial disputes, of “Petition” are similar in these places, such as house demolition, industrial disputes, and life infrastructures destroyed for a long time. Therefore, the suggestion to the city’s administration is life infrastructures destroyed for a long time. Therefore, suggestion city’s administration is to to solve these kinds of problems early in those places the to reduce events to of the “Petition.” solve these kinds of problems early in those places to reduce events of “Petition.” 3.4. Spatial Distribution Correlations Analysis Result of “Events-Events” “Dispute” events and “Fight” events are used as examples of the spatial distribution correlations analysis of “events-places” from the aspects of spatial and temporal distribution correlations.


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3.4. Spatial Distribution Correlations Analysis Result of “Events-Events” Sustainability 2018, 10, 2084

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“Dispute” events and “Fight” events are used as examples of the spatial distribution correlations analysis of “events-places” from the aspects of spatial and temporal distribution correlations.

(1) (1) Spatial Spatial distribute distribute correlations correlations Spatial Spatial clustering clustering is is presented presented separately separately for for “Dispute” “Dispute” and and “Fight” “Fight” events. events. The The strength strength of of the the clustering center is taken as the weight. High weight has high point density (Figure 19). clustering center is taken as the weight. High weight has high point density (Figure 19).

Fight Events

Dispute Events

Figure 19. Distribution of data of dispute contradiction and events of fight by spatial clustering. Figure 19. Distribution of data of dispute contradiction and events of fight by spatial clustering.

Figure 19 shows that the “Dispute” and “Fight” events in Liuwang have strong spatial Figure 19 shows that the of “Dispute” and in Liuwang have strong spatialindistribution distribution relevance. Most the fights are“Fight” causedevents by common contradictory disputes this region relevance. Most of the fights are caused by common contradictory disputes in this region after field investigation. Thus, the “Dispute” and “Fight” events have strong relevance. after field investigation. Thus, the “Dispute” and “Fight” events have strong relevance. (2) Temporal distribution correlations (2) Temporal distribution correlations Two months is the time interval for statistics. The number and variance of “dispute” and “fight” Two months is the are timeshown interval for statistics. The number and variance of “dispute” and “fight” events every 2 months in Table 2. events every 2 months are shown in Table 2. Table 2. Number and variance of “dispute” events and “fight” events every two months. Table 2. Number and variance of “dispute” events and “fight” events every two months. Time 2014/8 Time 2014/10 2014/12 2014/8 2015/2 2014/10 2015/4 2014/12 2015/6 2015/2 2015/8 2015/4 2015/10 2015/6 2015/12 2015/8 2016/2 2015/10 2016/4 2015/12 2016/6 2016/2 2016/8 2016/4 2016/10 2016/6 2016/12

2016/8 2016/10 2016/12

Dispute Events

Number Variance Ratio Dispute Events 3680 0.096 Number Variance Ratio 4010 −0.793 830 1.880 3680 0.096 2390 4.272 4010 −0.793 12,600 1.219 830 1.880 27,960 −0.353 2390 4.272 18,080 0.386 12,600 1.219 25,060 1.159 27,960 −0.353 54,100 −0.168 18,080 0.386 44,990 −0.022 25,060 1.159 43,990 −0.046 54,100 −0.168 41,980 −0.020 44,990 −0.022 41,160 −0.122 43,990 −0.046 36,140 −0.290 41,980 −0.020 25,660

41,160 36,140 25,660

−0.122 −0.290

Fight Events

Number Variance Ratio Fight Events 140 0.149 Number Variance Ratio 160 −0.625 140 60 0.149 −0.330 40 160 −0.6254.000 200 2.250 60 −0.330 650 −0.462 40 4.000 350 0.200 200 2.250 420 0.786 650 −0.462 750 −0.080 350 0.200 690 −0.174 420 0.786 570 −0.246 750 −0.080−0.372 430 690 270 −0.1740.000 570 270 −0.246−0.519 430 130 −0.372

270 270 130

0.000 −0.519


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Figure20 20further furthergives gives visualization result of variance the variance ratio of “dispute” and “fight” Figure thethe visualization result of the ratio of “dispute” and “fight” events events fortwo every two months from August to December for every months from August 2014 to 2014 December 2016. 2016.

Dispute Events

Fight Events

Figure 20. 20. Variance Variance ratio ratio of of events events of of disputes-fights. disputes-fights. Figure

Figure 20 shows that the line chart of these two types of events are almost coincident. Thus, these Figure 20 shows that the line chart of these two types of events are almost coincident. Thus, these two types of events are considered strongly correlated to temporal distribution. Considering that two types of events are considered strongly correlated to temporal distribution. Considering that these these two events are relevant in spatial distribution, we conclude that strong correlations exist two events are relevant in spatial distribution, we conclude that strong correlations exist between between contradictions and brawl events in temporal and spatial distributions. Based on the strong contradictions and brawl events in temporal and spatial distributions. Based on the strong temporal temporal and spatial correlations, we conclude that the contradictions and disputes are the important and spatial correlations, we conclude that the contradictions and disputes are the important causes of causes of fights and brawls, and they easily evolve into fights if these events are not dealt with well. fights and brawls, and they easily evolve into fights if these events are not dealt with well. Therefore, Therefore, the investigation and mediation of the contradictions and disputes are particularly the investigation and mediation of the contradictions and disputes are particularly important in the important in the maintenance of social order. maintenance of social order. 4. Discussion Discussion 4. The concept concept system system of of social social comprehensive comprehensive governance governance is is huge huge and and complex, complex, and and there there are are The various kinds paper focuses on the of interesting hot events and theand spatiovarious kinds of ofevents. events.This This paper focuses on extraction the extraction of interesting hot events the temporal information mining, which is only one of the many entry points in this field. It has a broad spatio-temporal information mining, which is only one of the many entry points in this field. It has a research spacespace in theininformation mining of the comprehensive management events based on broad research the information mining ofsocial the social comprehensive management events based space-time management, whether in content or or method. on space-time management, whether in content method. First, this study only uses urban social management and comprehensive comprehensive control control events events without without First, this study only uses urban social management and the other indirect data source. The indirect data from social media can be introduced into later the other indirect data source. The indirect data from social media can be introduced into later research, research, can dig information out more information with the comprehensive datasuch together, asand the which canwhich dig out more with the comprehensive data together, as the such Weibo Weibo and WeChat check-in data. WeChat check-in data. Second, Ontology Ontology is is aa system system for for the the knowledge knowledge representation representation of of all all kinds kinds of of spatio-temporal spatio-temporal Second, phenomena [61]. Our study takes a case study on spatio-temporal data mining with urban social social phenomena [61]. Our study takes a case study on spatio-temporal data mining with urban management events to describe the law of urban management. In addition, the system construction management events to describe the law of urban management. In addition, the system construction of ontology ontology in in the the urban urban social social management management domain domain requires requires the the participation participation of of experts experts in in the the field; field; of the ontology of comprehensive management events constructed in this paper cannot be the real the ontology of comprehensive management events constructed in this paper cannot be the real universal ontology because of the limitations of the author. Thus, the ontology constructed in this universal ontology because of the limitations of the author. Thus, the ontology constructed in this research has has plenty plentyof ofroom roomfor forimprovement. improvement. research Moreover, the calculation formulas of correlation factors are given in the proposed method of “event-event” and “event-place” correlation exploring in this study. However, the correlations between events cannot be found out intuitively with the correlation factors because of the effect of


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Moreover, the calculation formulas of correlation factors are given in the proposed method of “event-event” and “event-place” correlation exploring in this study. However, the correlations between events cannot be found out intuitively with the correlation factors because of the effect of distance measure units to the calculated values of correlation factors. Therefore, a correction factor is introduced in this study, and its value is related to the distance measure units. The correlation factor requires a great number of tests to verify the relationship between the correction factor and the distance measure units repeatedly to make sure that the relevance between events can be represented intuitively with the correlation factor. 5. Conclusions The purpose of urban management and comprehensive administration is to maintain a good environment for social development. During the process of urban management, there are a large number of work record data. Thus, how to make use of these work records well to excavate useful information hidden in these historical data is very important for the decision-making of further urban social governance. The content of city management is huge with a complicated structure for urban governance. This study puts forward a concept system of urban social management events. An ontology model is proposed for the massive spatio-temporal data mining of social management and comprehensive control events. It designs the process of the construction of the ontology, builds the ontology using the existing tools, and realizes the extraction of the hot events in city management based on the semantic reasoning of ontology with Java-based frameworks, whose comprehensiveness and accuracy are higher than that of the old ones. This paper also introduces the spatio-temporal information mining for discrete USMEs from three perspectives: geographical statics, spatial aggregation and correlation relationship. A spatial-temporal correlation data mining between events and locations or between events and events is proposed to mine the spatial-temporal information from the discrete and massive city’s comprehensive management events. Thermodynamic charts scatter plots, and the line charts are used to realize the visualization of the urban social management event model to provide decision support for urban comprehensive management. The USMEs of Qingdao city in August 2016 are taken as an experimental dataset with the proposed approach. The proposed method can effectively mine the management of social hot events and their spatial distribution patterns, which can guide city governance and enhance the city’s comprehensive management level. The social media data should be introduced to integrate with the USMEs for the future spatio-temporal data mining of urban management and comprehensive administration. Author Contributions: X.L. and S.W. designed the experiments and wrote the paper. Q.H. conceived the study; H.W. performed the experiments and analyzed the data. Funding: This research received no external funding. Acknowledgments: This research is supported by National Natural Science Foundation of China (Grant No. 41271452) and Key Technologies R&D Program of China (Grant No. 2015BAK03B04). Conflicts of Interest: The authors declare no conflict of interest.

References 1. 2. 3. 4.

Bencardino, M.; Nesticò, A. Demographic Changes and Real Estate Values. A Quantitative Model for Analyzing the Urban-Rural Linkages. Sustainability 2017, 9, 536. [CrossRef] Van den Berg, L.; Drewett, R.; Klaasen, L.H. Urban Europe: A Study of Growth and Decline; Elsevier Ltd.: London, UK, 1982. Nestico, A.; Sica, F. The sustainability of urban renewal projects: A model for economic multi-criteria analysis. J. Prop. Invest. Financ. 2017, 35, 397–409. [CrossRef] Wang, J.; Xie, M. Geographical national condition and complex system. Acta Geod. Cartogr. Sin. 2016, 45, 1–8.


5.

6. 7.

8. 9. 10. 11. 12. 13. 14. 15. 16.

17. 18. 19.

20. 21.

22. 23. 24.

25.

26. 27.

22 of 24

Merem, E.C.; Yerramilli, S.; Twumasi, Y.A.; Wesley, J.M.; Robinson, B.; Richardson, C. The Applications of GIS in the Analysis of the Impacts of Human Activities on South Texas Watersheds. Int. J. Environ. Res. Public Health 2011, 8, 2418–2446. [CrossRef] [PubMed] Gao, S.; Janowicz, K.; Couclelis, H. Extracting urban functional regions from points of interest and human activities on location-based social networks. Trans. GIS 2017, 21, 446–467. [CrossRef] Dai, L.; Xue, T.; Wu, B.; Rong, X.; Xu, B. Spatiotemporal Structure Features of Network Check-in Activities of Urban Residents and Their Impacting Factors: A Case Study in Six Urban Districts of Beijing. J. Asian Arch. Build. Eng. 2017, 16, 131–138. [CrossRef] Gore, A. The digital earth: Understanding our planet in the 21st century. Aust. Surv. 1998, 43, 89–91. [CrossRef] Su, K.; Li, J.; Fu, H. Smart city and the applications. In Proceedings of the 2011 International Conference on Electronics, Communications and Control, Ningbo, China, 9–11 September 2011; pp. 1028–1031. Girard, L.F. Toward a Smart Sustainable Development of Port Cities/Areas: The Role of the “Historic Urban Landscape” Approach. Sustainability 2013, 5, 4329–4348. [CrossRef] Li, D. Digital city + Internet of things + cloud computing = smart city. China New Telecommun. 2011, 20, 46. Paganelli, F.; Turchi, S.; Giuli, D. A Web of Things Framework for RESTful Applications and Its Experimentation in a Smart City. IEEE Syst. J. 2017, 10, 1412–1423. [CrossRef] Zhang, K.; Ni, J.; Yang, K.; Liang, X.; Ren, J.; Shen, X.S. Security and Privacy in Smart City Applications: Challenges and Solutions. IEEE Commun. Mag. 2017, 55, 122–129. [CrossRef] Rathore, M.M.; Paul, A.; Ahmad, A.; Jeon, G. IoT-Based Data: From Smart City towards Next Generation Super City Planning. Int. J. Semant. Web Inf. Syst. 2017, 13, 28–47. [CrossRef] Khan, M.S.; Woo, M.; Nam, K.; Chathoth, P.K. Smart City and Smart Tourism: A Case of Dubai. Sustainability 2017, 9, 2279. [CrossRef] Wong, M.S.; Wang, T.; Ho, H.C.; Kwok, C.Y.T.; Lu, K.; Abbas, S. Towards a Smart City: Development and Application of an Improved Integrated Environmental Monitoring System. Sustainability 2018, 10, 623. [CrossRef] Wang, Y.; Wang, T.; Ye, X.; Zhu, J.; Lee, J. Using Social Media for Emergency Response and Urban Sustainability: A Case Study of the 2012 Beijing Rainstorm. Sustainability 2016, 8, 25. [CrossRef] Kuroishi, I. Urban Survey and Planning in Twentieth-Century Japan: Wajiro Kons Modernology and Its Descendants. J. Urban Hist. 2016, 42, 557–581. [CrossRef] Xu, Z.; Zhang, J.; Li, C.; Li, Z.; Rao, Y.; Lu, T. A Road to Sustainable Development of Chinese Cities: A Perception of Improving Urban Management Efficiency Based on Two-Level Production Factors. Sustainability 2017, 9, 2212. [CrossRef] Zhou, M.; Yue, Y.; Li, Q.; Wang, D. Portraying Temporal Dynamics of Urban Spatial Divisions with Mobile Phone Positioning Data: A Complex Network Approach. ISPRS Int. J. Geo Inf. 2016, 5, 240. [CrossRef] Fu, J.Y.; Jing, C.F.; Du, M.Y.; Fu, Y.L.; Dai, P.P. Study on Adaptive Parameter Determination of Cluster Analysis in Urban Management Cases. ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2017, XLII-2/W7, 1143–1150. Mennis, J.; Guo, D. Spatial data mining and geographic knowledge discovery—An introduction. Comput. Environ. Urban Syst. 2009, 33, 403–408. [CrossRef] Chen, C.C.; Chiang, M.F.; Peng, W.C. Mining and clustering mobility evolution patterns from social media for urban informatics. Knowl. Inf. Syst. 2016, 47, 381–403. [CrossRef] Noulas, A.; Scellato, S.; Lathia, N.; Mascolo, C. Mining User Mobility Features for Next Place Prediction in Location-Based Services. In Proceedings of the 2012 IEEE 12th International Conference on Data Mining, Brussels, Belgium, 10–13 December 2012; pp. 1038–1043. Noulas, A.; Scellato, S.; Lathia, N.; Mascolo, C. A Random Walk around the City: New Venue Recommendation in Location-Based Social Networks. In Proceedings of the 2012 International Conference on Privacy, Security, Risk and Trust, Amsterdam, The Netherlands, 3–5 September 2012; pp. 144–153. Ji, B.; Lee, Y.; Yu, K.; Kwon, P. Detecting Themed Streets Using a Location Based Service Application. ISPRS Int. J. Geo Inf. 2016, 5, 111. [CrossRef] Laylavi, F.; Rajabifard, A.; Kalantari, M. A Multi-Element Approach to Location Inference of Twitter: A Case for Emergency Response. ISPRS Int. J. Geo Inf. 2016, 5, 56. [CrossRef]


28. 29.

30. 31.

32. 33.

34. 35. 36. 37. 38.

39. 40. 41. 42. 43.

44.

45. 46.

47. 48. 49.

23 of 24

Hu, Q.; Wang, M.; Li, Q. Urban hotspot and commercial area exploration with check-in Data. Acta Geod. Cartogr. Sin. 2014, 43, 314321. Wang, M.; Hu, Q.; Li, Q.; Qin, L. School of Remote Sensing and Information Engineering, Wuhan University; Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University; State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University. Extracting hierarchical landmark from check-in data. Chin. J. Comput. 2016, 39, 405–413. Zhang, D.; Zhou, B.; Li, H.; Wang, X.; Si, M. Prediction of Urban Heat Island Expansion based on Markov Chain Theory. China Popul. Resour. Environ. 2013, 23, 321–325. Kazak, J.K. The Use of a Decision Support System for Sustainable Urbanization and Thermal Comfort in Adaptation to Climate Change Actions—The Case of the Wrocław Larger Urban Zone (Poland). Sustainability 2018, 10, 1083. [CrossRef] Ai, H.; Shi, Y. Study on prediction of haze based on BP neural network. Comput. Simul. 2015, 32, 402–405. Zhao, X.J.; Zhao, P.S.; Xu, J.; Meng, W.; Pu, W.W.; Dong, F.; He, D.; Shi, Q.F. Analysis of a winter regional haze event and its formation mechanism in the North China Plain. Atmos. Chem. Phys. 2013, 13, 5685–5696. [CrossRef] Liu, Z.; Jia, Z.; Li, X. Traffic volume forecast based on gray markov chain model. J. East China Jiaotong Univ. 2012, 29, 30–34. Das, R.D.; Winter, S. Detecting Urban Transport Modes Using a Hybrid Knowledge Driven Framework from GPS Trajectory. ISPRS Int. J. Geo Inf. 2016, 5, 207. [CrossRef] Deng, Z. Transport Service Oriented Muti-Source Mobile Trajectory Data Mining and Muti-Level Knowledge Discovery of Human Activities. Ph.D. Thesis, East China Normal University, Shanghai, China, 2012. Bergman, C.; Oksanen, J. Conflation of OpenStreetMap and Mobile Sports Tracking Data for Automatic Bicycle Routing. Trans. GIS 2016, 20, 848–868. [CrossRef] Zhang, J.; Wu, F.; Zhang, H.; Information Engineering University; Xi0 an Surveying and Mapping Information & Technology Station. Urban residents travel characteristics mining utilizing taxi trajectory data. Geogr. Geo Inf. Sci. 2015, 31, 104–108. Ishikawa, T.; Fujinami, K. Smartphone-Based Pedestrian’s Avoidance Behavior Recognition towards Opportunistic Road Anomaly Detection. ISPRS Int. J. Geo Inf. 2016, 5, 182. [CrossRef] Xu, Z.; Liu, Y.; Yen, N.; Mei, L.; Luo, X.; Wei, X.; Hu, C. Crowdsourcing based Description of Urban Emergency Events using Social Media Data. IEEE Trans. Cloud Comput. 2016, 99, 1. [CrossRef] Huang, Q.; Wong, D.W.S. Activity patterns, socioeconomic status and urban spatial structure: What can social media data tell us? Int. J. Geogr. Inf. Syst. 2016, 30, 1873–1898. [CrossRef] Shen, Y.; Karimi, K. Urban function connectivity: Characterisation of functional urban streets with social media check-in data. Cities 2016, 55, 9–21. [CrossRef] Romolini, M.; Grove, J.M.; Locke, D.H. Assessing and comparing relationships between urban environmental stewardship networks and land cover in Baltimore and Seattle. Landsc. Urban Plan. 2013, 120, 190–207. [CrossRef] Du, X.; Emebo, O.; Varde, A.; Tandon, N.; Chowdhury, S.N.; Weikum, G. Air quality assessment from social media and structured data: Pollutants and health impacts in urban planning. In Proceedings of the 2016 IEEE, International Conference on Data Engineering Workshops, Helsinki, Finland, 16–20 May 2016; pp. 54–59. Li, J.; Qin, Q.; Han, J.; Tang, L.-A.; Lei, K.H. Mining Trajectory Data and Geotagged Data in Social Media for Road Map Inference. Trans. GIS 2015, 19, 1–18. [CrossRef] Petrova, M.; Nenko, A.; Sukharev, K. Urban acupuncture 2. In 0: Urban management tool inspired by social media. In Proceedings of the International Conference on Electronic Governance and Open Society: Challenges in Eurasia, St. Petersburg, Russia, 22–23 November 2016; pp. 248–257. Gabrielli, L.; Rinzivillo, S.; Ronzano, F.; Villatoro, D. From Tweets to Semantic Trajectories: Mining Anomalous Urban Mobility Patterns. Lect. Notes Comput. Sci. 2014, 8313, 26–35. Widhalm, P.; Yang, Y.; Ulm, M.; Athavale, S.; González, M.C. Discovering urban activity patterns in cell phone data. Transportation 2015, 42, 597–623. [CrossRef] Lee, W.H.; Tseng, S.S.; Shieh, J.L.; Chen, H.H. Discovering Traffic Bottlenecks in an Urban Network by Spatiotemporal Data Mining on Location-Based Services. IEEE Trans. Intell. Transp. Syst. 2011, 12, 1047–1056. [CrossRef]


50. 51. 52. 53.

54. 55.

56. 57. 58. 59. 60. 61.

24 of 24

Zhu, B.; Xu, X. Urban Principal Traffic Flow Analysis Based on Taxi Trajectories Mining. In Advances in Swarm and Computational Intelligence; Springer International Publishing: New York, NY, USA, 2015; pp. 172–181. Jin, J.; Gubbi, J.; Marusic, S.; Palaniswami, M. An Information Framework for Creating a Smart City Through Internet of Things. IEEE Int. Things J. 2014, 1, 112–121. [CrossRef] Kim, T.H.; Ramos, C.; Mohammed, S. Smart City and IoT. Future Gener. Comput. Syst. 2017, 76, 159–162. [CrossRef] Noy, N.F.; Crubézy, M.; Fergerson, R.W.; Knublauch, H.; Tu, S.W.; Vendetti, J.; Musen, M.A. Protégé-2000: An open-source ontology-development and knowledge-acquisition environment. AMIA Annu. Symp. Proc. AMIA Symp. 2003, 2003, 953. Musen, M.A.; Team, T.P. Protégé Ontology Editor. In Encyclopedia of Systems Biology; Springer: New York, NY, USA, 2013; pp. 1763–1765. Ameen, A.; Khan, K.U.R.; Rani, B.P. Extracting knowledge from ontology using Jena for semantic web. In Proceedings of the International Conference for Convergence for Technology-2014, Pune, India, 6–8 April 2014; pp. 1–5. Diosteanu, A.; Cotfas, L.A. Agent Based Knowledge Management Solution using Ontology, Semantic Web Services and GIS. Inform. Econ. J. 2009, 13, 90–98. Giri, K.; Gokhale, P. Developing a banking service ontology using Protégé, an open source software. Annu. Libr. Inf. Stud. 2015, 62, 281–285. Owens, A.; Seaborne, A.; Gibbins, N.; Schraefel, M.C. Clustered TDB: A Clustered Triple Store for Jena. In Proceedings of the 2009 WWW, Madrid, Spain, 20–24 April 2009. Sheather, S.J.; Jones, M.C. A Reliable Data-Based Bandwidth Selection Method for Kernel Density Estimation. J. R. Stat. Soc. 1991, 53, 683–690. Hinneburg, A.; Gabriel, H.H. DENCLUE 2.0: Fast Clustering Based on Kernel Density Estimation. In Advances in Intelligent Data Analysis VII; Springer: Berlin, German, 2007; pp. 70–80. Iwaniak, A.; Łukowicz, J.; Strzelecki, M.; Kaczmarek, I. Ontology Driven Analysis of Spatio-temporal Phenomena, Aimed at Spatial Planning and Environmental Forecasting. In Proceedings of the ISPRS 2013-SSG Conference, Antalya, Turkey, 11–17 November 2013; pp. 119–124. © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).