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SALON: A Universal Stay Point-Based Location AnalysisPlatform
Yue Hu1,4, Sijie Ruan1,4∗, Yuting Ni4, Huajun He2,4, Jie Bao4∗, Ruiyuan Li3,4, Yu Zheng1,2,41Xidian University, Shaanxi, China 2Southwest Jiaotong University, Chengdu, China
3Chongqing University, Chongqing, China 4JD Intelligent Cities Research, Beijing, [email protected];[email protected];[email protected];[email protected]
[email protected];[email protected];[email protected]
ABSTRACTThe prevalence of positioning technologies has fostered massivetrajectory data. Stay points from trajectories indicate the visitingof moving objects to locations, which provide an opportunity tounderstand the locations comprehensively. Many existing worksrely on stay points to analyze locations. However, they are ad-hocsolutions to tackle specific problems, and it is time-consuming andtedious to develop each application. In this paper, we propose a uni-versal StAy point-based LOcation aNalysis platform, i.e., SALON,with the characteristics of universality, efficiency and flexibility.It can retrieve stay points using flexible conditions, associate staypoints with locations, extract comprehensive location profiles andvisualize the analysis results to users. Based on the combination ofthese functions, we demonstrate three different location analysisscenarios, i.e., illegal location discovery, popular location ranking,location temporal analysis to show its characteristics.
CCS CONCEPTS• Information systems→ Spatial-temporal systems.
KEYWORDSSpatio-temporal Data Mining; Location Analysis; Interactive Explo-rationACM Reference Format:Yu Hu, Sijie Ruan, Yuting Ni, Huajun He, Jie Bao, Ruiyuan Li, and Yu Zheng.2021. SALON: A Universal Stay Point-Based Location Analysis Platform. In29th International Conference on Advances in Geographic Information Systems(SIGSPATIAL ’21), November 2–5, 2021, Beijing, China. ACM, New York, NY,USA, 4 pages. https://doi.org/10.1145/3474717.3483991
1 INTRODUCTIONWith the wide adoption of GPS-enabled devices, massive trajec-tory data has been generated every day, which represents differentmobility behaviors of moving objects. Among them, an individ-ual’s staying behavior provides us an opportunity to understandthe interaction between moving objects and locations. The staying
∗Jie Bao and Sijie Ruan are the corresponding authors.
Permission to make digital or hard copies of part or all of this work for personal orclassroom use is granted without fee provided that copies are not made or distributedfor profit or commercial advantage and that copies bear this notice and the full citationon the first page. Copyrights for third-party components of this work must be honored.For all other uses, contact the owner/author(s).SIGSPATIAL ’21, November 2–5, 2021, Beijing, China© 2021 Copyright held by the owner/author(s).ACM ISBN 978-1-4503-8664-7/21/11.https://doi.org/10.1145/3474717.3483991
Location Analysis Task
Trajectory
Pre-
processing
Spatial Index
Visualization
Location
Profiling
Stay Point
Retrieval
Illegal Location Discovery
Application
Popular Location Ranking
Location Temporal Analysis
Raw GPS
Logs
Geographical
Entities
Stay Point-
Location
AssociationSpatial-Temporal Data
Management
Figure 1: System overview
behavior is usually reflected by the stay point in his/her trajecto-ries, which represents that a moving object stays in a geographicalregion for a while. With those stay points, many location analysis-based applications can be facilitated, e.g., risky region discovery [1],POI recommendation [12], and urban planning [2].
However, to support different applications, one has to designproblem-specific solutions. Apart from that, even for similar tasks,processing steps and parameters should be tweaked, and visualiza-tion techniques should be employed to produce good results dueto different data distributions. Therefore, developing those appli-cations is extremely time-consuming and tedious. In fact, manyprocessing steps of those works are shared in common.
In this paper, we propose a universal StAy point-based LOcationaNalysis platform (SALON) 1, which tends to provide a convenientand efficient approach to obtain insights into location analysis re-sults through interactive exploration for product managers, govern-ment regulators, researchers, etc. We take full consideration of theproperties of stay points and locations, and integrate the commonprocessing steps for location analysis tasks. As shown in Figure 1,the platform takes raw GPS logs and geographical entities suchas POI, road network as input, summarises six processes coveringdata pre-processing, management, interaction and visualization.
The advantages of our platform are summarized as follows: 1)Universality. The platform supports multiple analysis methods totackle various applications. 2) Efficiency. The platform uses JUST [5]as database and builds spatial indexes for spatial data. Users canobtain analysis results in a short time. 3) Flexibility. The platformsupports interactive retrieval and analysis. Users can customizeparameters in these functions and explore the analysis results bydifferent parameters.
In order to show the advantages of our platform, we demonstratethree scenarios: 1) Illegal location discovery, 2) Popular location
1http://just.urban-computing.com/salon.html
SIGSPATIAL ’21, November 2–5, 2021, Beijing, China Yue Hu, et al.O
nli
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Inte
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on
Off
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e P
roce
ssin
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Trajectory Pre-processingNoise Filtering Stay Point Detection Semantic
EnhancementStay
Point Residence
Region
Semantic
Time
Spatial Index BuildingRaw GPS Logs
Raw DataGeographical
Entities
Raw Data
Dynamic Features Static Features
# Stay Points
Temporal
Distribution
Moving Objects
Location ProfilingCategory-based Clustering-based
Stay Point-Location Association
Temporal Condition
Object Condition
Spatial Condition
Attribute Condition
Stay Point Retrieval Visualization
# Entities
POI
Road Network
Figure 2: System framework.
ranking, 3) Location temporal analysis through the combination ofdifferent functions in our platform.
2 SYSTEM OVERVIEWTo understand the locations comprehensively, the platform designsa two-stage approach that follows the characteristics of universality,efficiency and flexibility. Figure 2 shows the framework of SALON,which contains two stages: (1) Offline Processing (Section 3),SALON first prepares raw GPS points and geographic entities, e.g.,points of interest, and road network. For raw GPS points, they willbe transformed to stay points with semantic information throughnoise filtering, stay points detection and semantic enhancement.Considering the efficiency of the interactive exploration of theonline interaction stage, spatial indexes will be built for stay pointsand geographical entities. The goal of this phase is to obtain andstore related data. (2) Online Interaction (Section 4), SALON firstconducts stay point retrieval with a combination of four conditionsto get candidate stay points. After retrieval, it offers two ways, i.e.,category-based method and clustering-based method, to associatestay points with locations. During association, it takes considerationof multiple characteristics describing the location and provides theprofiling of the location. Based on the profiles of locations, onecan perform multiple location analysis. The goal of this phase isto achieve interactive exploration of location analysis by retrieval,association, profiling and visualization.
In summary, the platform is universal, efficient, and flexible tofulfill the complete process for location analysis tasks based on thestay points.
3 OFFLINE PROCESSINGIn this section, we present data pre-processing and management,which builds the foundations of data interaction. This section isdivided into two steps. (1) trajectory pre-processing, which trans-forms trajectory data to stay points with semantic information; (2)spatial index building, which manages stay points and geographicalentities for efficient join and query.
3.1 Trajectory Pre-processingTrajectory pre-processing is important, as the data noise and out-liers in raw data affect the accuracy and performance of later ap-plications. Moreover, we can also enhance the stay points withsemantic meanings extracted from time, location, and the spatio-temporal patterns of the moving objects, which makes the staypoints retrieval more flexible. The platform covers the followingsub-tasks in the trajectory pre-processing [6–8]:Noise Filtering. Due to sensor noise and other factors, such asreceiving poor positioning signals in urban canyons, trajectoriesare never perfectly accurate. We employ a heuristic-based outlierdetection algorithm [10] to remove noise GPS points.Stay Point Detection. This task extracts a subset of consecutiveGPS points as a stay point which represents that the moving objecthas stayed for a while, such as waiting for passengers, loadinggoods. We adopt a clustering-based algorithm [4] to extract staypoints. Respectively, we obtain the centroid (𝑙𝑜𝑛, 𝑙𝑎𝑡 ), the mid-time(𝑡𝑚𝑖𝑑 ) and the difference between the end time and the start time(𝑑𝑢𝑟𝑎𝑡𝑖𝑜𝑛) as a stay point’ attributes from the above subset.Semantic Enhancement: This task enhances stay points withsemantic information which can be indicators to retrieval and anal-ysis. For stay point itself, we consider the district where it is located(𝑟𝑒𝑔𝑖𝑜𝑛), the day of the week (e.g., Sunday) (𝑠𝑒𝑚𝑎𝑛𝑡𝑖𝑐_𝑡 ) when itis generated. For each moving object (𝑜𝑖𝑑), we extract its frequentstay locations in different periods, e.g., night or daytime by cluster-ing its stay points. Then for each frequent stay location, we exactits province, city and location type as the moving object’s labels(𝑜𝑖𝑑_𝑙𝑎𝑏𝑒𝑙 ).
Overall, a stay point is characterized as follows:𝑆𝑃 = ⟨𝑜𝑖𝑑, {𝑜𝑖𝑑_𝑙𝑎𝑏𝑒𝑙}, 𝑙𝑜𝑛, 𝑙𝑎𝑡, 𝑡𝑚𝑖𝑑 , 𝑑𝑢𝑟𝑎𝑡𝑖𝑜𝑛, 𝑟𝑒𝑔𝑖𝑜𝑛, {𝑠𝑒𝑚𝑎𝑛𝑡𝑖𝑐_𝑡}⟩
3.2 Spatial Index BuildingSALON contains two types of data, respectively, the processed staypoints and geographic entities. A geographic entity is a geographicfeature, like a restaurant or a road segment. It occupies a positionin space and its attributes and geographic location are recorded inthe related database. In SALON, we use POIs and road networksas geographic entities to retrieve stay points and analyze location
SALON: A Universal Stay Point-Based Location Analysis Platform SIGSPATIAL ’21, November 2–5, 2021, Beijing, China
auxiliarily. In order to achieve interactive analysis and make theplatform efficient, these massive data will be indexed and stored ina spatial database [5–7] for retrieval and data summaries.
4 ONLINE INTERACTIONIn this section, we present the online interaction of the locationanalysis task based on stay points. This section is divided into threesteps. 1) Stay Point retrieval, which is used to obtain candidate staypoints; 2) Stay Point-Location Association, which focuses on theway to associate stay points with locations. 3) Location Profiling,which summarises the properties of each location and generatesstatistics and analysis results.
4.1 Stay Point RetrievalIn order to obtain desired stay points and apply them to differentapplications, we take full account of the properties of stay pointsand design four main selection dimensions as follows:Spatial condition, which pays attention to each stay point’ spatialfeatures, such as the region, POIs and road network, e.g., findingstay points that are close (e.g., within 50m) to any restaurant;Temporal condition, which focuses on a temporal feature of thestay points, e.g., finding stay points on Sundays;Object condition, which is used to obtain stay points of movingobjects with the same label, e.g., finding stay points generated bymoving objects whose resident place is a university;Attribute condition, which focuses on the duration of each staypoint, e.g., finding stay points whose duration is more than 30min.
We support a free combination of these types of conditions toflexibly obtain desired stay points.
4.2 Stay Point-Location AssociationIn some applications, the underlying location is known, such asall gas stations in a city, while in others, the location is unknown,such as an illegal warehouse. Therefore, we devise the followingtwo association methods.Clustering-based Association. We adopt density-based spatialclustering of applications with noise (DBSCAN) [3] to generate fre-quently visited locations. The platform supports users to customlyinput this algorithm’s parameters (𝑚𝑖𝑛𝑝𝑡𝑠 and 𝑒𝑝𝑠) according todifferent application scenarios.Catagory-basedAssociation.The platform contains location datawith categories from OpenStreetMap2 and allows users to selectone particular category to analyze. We focus on stay points withinthese locations regardless of quantity.
4.3 Location ProfilingLocation profiling is an essential process of our platform, whichhelps us understand location better through concrete value and dis-tributions. To meet the universality, we extract static and dynamicfeatures for each location, details as follows:Static features, which indicate where the location is and what isaround the location. There are many kinds of static features. Forexample, calculating the distribution of road network around a loca-tion infers its traffic status in one city. In our platform, we identify
2http://download.geofabrik.de/asia.html
the center point of location (longitude and latitude), distribution ofPOIs within 100m and distribution of road network within 200m asstatic features, which can well infer the location’s significance andfunctions.Dynamic features,which indicate access of moving objects to thelocation. As we focus on stay points within the location, informa-tion about moving objects they visit are natural dynamic features.For example, if a location contains hundreds of taxi stay points, thelocation can be regarded as a candidate drop-off and pick-up loca-tion with high popularity. In our platform, we propose six dynamicfeatures (the number of stay points, the number of moving objects,the frequency of visits, distribution of visit periods, distribution ofvisit weeks/months/years, distribution of visit duration) to describethe moving objects’ visiting pattern of the location.
5 DEMONSTRATIONAs shown in Figure 4, the main page contains four sections. TheData Interaction panel supports four types of stay point retrieval andtwo types of location analysis for users to obtain desired stay pointsand apply them to different scenarios. TheData visualization is usedto display stay points and locations on the map. The button in thelower-left corner is designed for map switching. The Data statisticspanel displays the overall dataset summaries. TheData Distributionspanel is used to visualize the spatial-temporal distributions of staypoints, including temporal distributions, POI distributions, roadnetwork distributions, etc.
Data Interaction Data StatisticsData Visualization
Data DistributionsA stay
point
Figure 3: System Viewing Interface.Our platform SALON will be demonstrated using three types of
trajectory data (pedestrians, taxis and hazardous material vehicles)and displaying three application scenarios as following:
5.1 Illegal Location DiscoveryWe can select the HCT dataset, which contains almost 1 monthof GPS records for 2,854 hazardous chemical material trucks andsome of them may load and unload hazardous chemical materialsat unregistered locations illegally. Obviously, trucks may stay in acertain location for a while when they load and unload materials,thus, we can discover those illegal locations based on stay points ofHCT trucks. We first extract stay points with 15min, 100m. Then,as shown in Figure 4(a), we set two conditions to seek suspiciousstay points: 1) the duration of the stay point is longer than 30min(an attribute condition) and 2) there does not exist POIs within 50mof the stay point (a spatial condition). In the association step, weadopt the clustering method to cluster these suspicious stay points
SIGSPATIAL ’21, November 2–5, 2021, Beijing, China Yue Hu, et al.
for obtaining candidate illegal locations, as shown in Figure 4(b).Next, we filter these candidate locations to find locations whosenumber of visits is more than 10 and the number of objects is lessthan 3, as shown in Figure 4(c). Finally, as shown in Figure 4(d),we show a suspicious illegal location with the highest number ofvisits. Moreover, this location is indeed an illegal warehouse forchemicals based on the on-field investigation.
(a) Stay Point Retrieval. (b) Location Association.
(c) Location Selection. (d) An Illegal Location.
Attribute Condition
Spatial Condition
Clustering-based:
DBSCAN(10,100m)
# of visits > 10
# of moving objects < 3
Figure 4: Processes of obtaining an illegal location.
5.2 Popular Location RankingWe switch the dataset to T-drive[9], which contains 7 days of GPSrecords for 10,357 taxis. In this example, we explore where taxisoften stay in the evening nearby restaurants. Like the first scenario,We first obtain stay points described above, which satisfy the fol-lowing criteria: 1) the duration of the stay point is less than 30minutes, 2) the time of the stay point is within 17:00-21:00 and 3)there exist POIs with the type of restaurant within 50m. Then weuse clustering analysis mode. As shown in Figure 5, we display theranking of popular locations, according to the number of visits andthen combine the map to show the top two locations specifically.
Ranking by the number of visits
81
75
Top 1 Location： # of visits = 81 Top 2 Location： # of visits = 75
Figure 5: Ranking of popular locations for taxis.
Popular Location Ranking can be widely used in taxi manage-ment, in which the driver can have full knowledge of popular loca-tions for receiving passengers to avoid wasting time and resources.
5.3 Location Temporal AnalysisWe use Geolife [11] data, which contains 5 years of GPS recordsfor 183 moving objects. Due to the long period of the data, we canobserve location temporal variation, such as year, month, day. Asshown in Figure 6, we give the example of Beijing Happy Valley.This location is from the category-based association and the param-eter is theme_park. We find June is the peak month for the locationand more visits on Fridays and Saturdays by tracking the movingobjects’ visiting time.
Location Temporal Analysis helps us understand a location’scyclical characteristics and development. It is widely adopted inurban planning.
Figure 6: Example of location temporal distribution.
ACKNOWLEDGMENTSThe work was supported by the National Key R&D Program ofChina (2019YFB2101801), NSFC (61976168, 62076191).
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