=Paper=
{{Paper
|id=Vol-2940/paper7
|storemode=property
|title=Identifying Anomalous Places, Routes by GPS Feature: A System for Child Monitoring
|pdfUrl=https://ceur-ws.org/Vol-2940/paper7.pdf
|volume=Vol-2940
|authors=Giacomo Abbattista,Donato Impedovo,Giuseppe Pirlo,Lucia Sarcinella,Nicola Stigliano,Alessandro Placido Luise,Gaetano Perrone,Claudio Perrotta,Simon Pietro Romano,Tommaso Zoppi,Enrico Schiavone,Irene Bicchierai,Francesco Brancati,Andrea Bondavalli,Luisa Franchina,Serena Ferracci,Federico Palmaro,Christian Catalano,Paolo Afrune,Mario Angelelli,Giovanni Maglio,Fabrizio Striani,Francesco Tommasi,Marco Zuppelli,Luca Caviglione,Matteo Repetto,Marco Bozzetti,Luca Olivieri,Fausto Spoto,Axel De Nardin,Marino Miculan,Claudio Piciarelli,Gian Luca Foresti,Alessandro Bocci,Stefano Forti,Gian-Luigi Ferrari,Antonio Brogi,Tobia Fiorese,Pietro Montino,Roberto De Prisco,Alfredo De Santis,Rocco Zaccagnino,Daniele Granata,Massimiliano Rak,Giovanni Salzillo,Umberto Barbato,Giuseppe Mario Malandrone,Giovanni Virdis,Giorgio Giacinto,Davide Maiorca,Dario Stabili,Francesco Pollicino,Alessio Rota,Shaharyar Khan,Alberto Volpatto,Geet Kalra,Jonathan Esteban,Tommaso Pescanoce,Sabino Caporusso,Michael Siegel,Alessia Boi,Carmelo Ardito,Tommaso Di Noia,Eugenio Di Sciascio,Domenico Lofù,Andrea Pazienza,Felice Vitulano,Giulio Berra,Gaspare Ferraro,Matteo Fornero,Nicolò Maunero,Paolo Prinetto,Gianluca Roascio,Luigi Coppolino,Salvatore D'Antonio,Giovanni Mazzeo,Luigi Romano,Paolo Campegiani,Vincenzo Dentamaro,Vito Nicola Convertini,Stefano Galantucci,Paolo Giglio,Tonino Palmisano,Giuseppe Pirlo,Massimiliano Masi,Tanja Pavleska,Simone Pezzoli,Massimiliano Calani,Giovanni Denaro,Alberto Leporati,Manuel Cheminod,Luca Durante,Lucia Seno,Adriano Valenzano,Mario Ciampi,Fabrizio Marangio,Giovanni Schmid,Mario Sicuranza,Marco Zuppelli,Giuseppe Manco,Luca Caviglione,Massimo Guarascio,Marzio Di Feo,Simone Raponi,Maurantonio Caprolu,Roberto Di Pietro,Paolo Spagnoletti,Federica Ceci,Andrea Salvi,Vincenzo Carletti,Antonio Greco,Alessia Saggese,Mario Vento,Gabriele Costa,Enrico Russo,Andrea Valenza,Giuseppe Amato,Simone Ciccarone,Pasquale Digregorio,Giuseppe Natalucci,Giovanni Lagorio,Marina Ribaudo,Alessandro Armando,Francesco Benvenuto,Francesco Palmarini,Riccardo Focardi,Flaminia Luccio,Edoardo Di Paolo,Enrico Bassetti,Angelo Spognardi,Anna Pagnacco,Vita Santa Barletta,Paolo Buono,Danilo Caivano,Giovanni Dimauro,Antonio Pontrelli,Chinmay Siwach,Gabriele Costa,Rocco De Nicola,Carmelo Ardito,Yashar Deldjoo,Eugenio Di Sciascio,Fatemeh Nazary,Vishnu Ramesh,Sara Abraham,Vinod P,Isham Mohamed,Corrado A. Visaggio,Sonia Laudanna
|dblpUrl=https://dblp.org/rec/conf/itasec/AbbattistaIPSS21
}}
==Identifying Anomalous Places, Routes by GPS Feature: A System for Child Monitoring==
https://ceur-ws.org/Vol-2940/paper7.pdf
Identifying anomalous places and routes by GPS feature: a
system for child monitoring
Giacomo Abbattista 1, Donato Impedovo 1, Giuseppe Pirlo 1, Lucia Sarcinella 1, and Nicola
Stigliano 1
1
University of Bari, Dep. Of Computer Science, Via Orabona 4, Bari, Italy
Abstract
The phenomenon of bullying and cyberbullying is a constant thorn for today's kids.
Some of these phenomena take place on the way from home to school (and back), it can
therefore materialize in anomalies through deviations from the standard route, or through
pauses / interruptions. These anomalies can be detected through the use of a GPS sensor already
available on all smartphones. In this work it is presented a system that through the acquisition
of the GPS parameters of the mobile phone is able to recognize abnormal path compared to
standard ones and to report the event to parents in order to take appropriate precautions. In
addition, parents can visualize paths and events by using a simple web platform.
The system is a preliminary version and has been tested on a sample of 9 users, demonstrating
excellent accuracy of the results and a wide acceptance by the selected users.
Keywords 1
Bullying, Cyberbullyng, GPS, DBScan, FoliumMap, Android
1. Introduction
With more than four billion Internet users across the globe [1], the online world is now part of
everyday life, and it plays a vital role in society. This rapid growth in technology is not coming only
with advantages but has surfaced many problems out of which cyberbullying is one of the primary
concerns. The internet has turned to be a double-edged sword which has brought unmatched ease in our
daily life. On the other hand, the internet has also created grounds for numerous unwanted behaviors,
like cyberbullying, a bullying type articulated via electronic means [2].
The bullying actions include physical assault, verbal assault and by spreading fake news, harsh
words/comments, rumors, gossips, threats, exclusion from social circle etc. The technological
advancement has transformed traditional bullying into cyberbullying [3] which is “the use of
information and communication technologies to support deliberate, repeated, and hostile behavior by
an individual or group that is intended to harm or defame others [4]”, in simple words cyberbullying is
“an electronic form of peer harassment [5]”. Cyberbullying is considered as more dangerous in
comparison to traditional bullying because cyberbullying has the potential to protect the bully due to
anonymity. This is the biggest difference as technology, and the internet gives extra mile protection to
the perpetrator.
A cyberbully can bully from any part of the world, and all s/he needs is a relevant technology or
medium that is readily available in almost all parts of the world. Cyberbullying can be quickly done 24
hours a day and 365 days a year, unlike physical bullying. Cyberbullying can occur at any time of life
irrespective of age group [6] and it increases as a person grow [7], [8].
The work reported in this paper is part of an Italian project aimed at creating an app able to record a
wide series of events can be referred to a bulling or cyberbulling action, and therefore exploiting the
same technologies that created the problem [9]. In particular, in this work we will discuss a functionality
(ITASEC) Italian Conference on Cybersecurity, April 7-9, 2021, Italy
EMAIL: giacomo.abbattista@uniba.it (A. 1); donato.impedovo@uniba.it (A. 2); giuseppe.pirlo@uniba.it (A. 3); lucia.sarcinella@uniba.it (A.
4); n.stigliano@studenti.uniba.it (A. 5)
ORCID: 0000-0003-0850-728X (A. 1); 0000-0002-9285-2555 (A. 2); 0000-0002-7305-2210 (A. 3); 0000-0002-8550-8588 (A. 4)
©️ 2020 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
implemented through a system that acquires by consensus the GPS parameters (latitude and longitude)
of the smartphone on which the application is installed and by analyzing these parameters, it is able to
recognize the places most frequented by the user and the routes usually used to move, but also and
above all, the unusual places and routes taken by the user. The parent then, through a special web
platform can view all this information graphically and visually.
The following paper is organized as follows: in section two the methods and technologies used will
be presented; in section 3 experiments will be presented; in section 4 results are presented. Section 5
concludes the work.
2. Methods
In this section we will examine the different techniques and technologies used within the work.
During the project development many solution have been investigated, however only the main and final
ones will be described below, thus excluding those discarded.
2.1. Second level heading
Fundamental and essential parameters of the work are the GPS parameters related to the users'
smartphones. These parameters are part of those acquired by the app (named ShieldApp) we are
developing. To date, the application is only available for devices with an Android Operating System
having an SDK no older than the 24. Android is the most widespread operating system in the world: it
is certified that 62.94% of mobile devices, including car radios, smartwatches, televisions and IoT
products, use Android as an operating system, or alternatively an operating system based on Android,
each of which has a dedicated graphic interface to make the user experience highly performing.
ShieldApp as soon as the installation is completed shows the user a security policy that asks for
consent to acquire his personal data relating to GPS movements and to use them for scientific research
purposes, ensuring not outside, all according to the protection regulations of European data (GDPR).
The security policies shown to the user, in particular, state that in accordance with the GDPR, the
acquired data are used for research purposes and solely and exclusively for the detection of bullying
and cyberbullying. Furthermore, all the results obtained from the processing will be visible only to
parents.
As soon as the security policy is accepted, whenever the user has turned on the GPS, the application
acquires this value and stores it on a mySQL database. In particular, in addition to the GPS parameters
(latitude and longitude), for each recorded value, the corresponding ID of the device, the type of data
(in this case of the "Sensor" type), the timestamp and the acquisition time (in the format YYYY-MM-
DD HH: MM: SS) are also stored. In fig. 1 an example of the acquired data.
Figure 1: Example of stored data
2.2. Data Clustering
Data are transferred from the mobile device to a server periodically. A series of processing steps are
performed on the server, in this case the user routing behavior is inspected by adopting an unsupervised
clustering algorithm: elements of a cluster will be the usual places and paths, while all the outliers will
be the anomaly ones. In particular, the choice of the unsupervised is mandatory since during the test
phase the user was not asked to report anything or to explicitly interact with the app, therefore labels
are not available. This also occurs in a real scenario in which the user normally will not voluntarily tag
his/her movements [10], [11], [12], [13].
Cluster analysis groups data objects based only on information found in the data that describes the
objects and their relationships. The goal is that objects within one group are similar (or related) to each
other and different (or unrelated) from objects in other groups. The greater the similarity (or
homogeneity) within a group and the greater the difference between the groups, the better or more
distinct the grouping. In this preliminary experiment, DBSCAN has been considered [14] based on the
consideration that it has been already adopted in several works dealing with geospatial data for position
prediction [15] [16]. The DBSCAN algorithm uses two parameters:
• minPts: the minimum number of points (a threshold) grouped together for a region to be
considered dense.
• eps (ε): a distance measure that will be used to locate points in the vicinity of any point.
These parameters can be understood if exploring Density Reachability and Density Connectivity.
Reachability in terms of density establishes a point reachable by another if it is within a particular
distance (eps) from it. Connectivity, on the other hand, involves a transitivity-based chaining approach
to determine whether points are in a particular cluster.
DBSCAN does not require to specify the number of clusters a priori, unlike many other widely used
algorithms such as k-means. This is of vital importance since each cluster is equivalent to a place heavily
frequented by the user, such as his home, school, workplace, etc., but there is no fixed number of these
places that applies to all users, nor is there a fixed number of these places for the same user over time.
Also, DBSCAN can find clusters of arbitrary shape. It can even find a cluster surrounded (but not
connected) by a different cluster. Due to the MinPts parameter, the so-called single-link effect (several
clusters connected by a thin line of dots) is reduced. In addition, it requires only two parameters and is
mostly insensitive to the ordering of points in the database.
Unfortunately, there are not only advantages, but DBSCAN also has disadvantages. In this case the
main disadvantage is that the quality of DBSCAN depends on the distance measure used in the
regionQuery function (P, ε). The most common distance metric used is the Euclidean distance.
Especially for high-dimensional data, this metric can be rendered almost useless due to the so-called
"Curse of Dimensionality", making it difficult to find an appropriate value for ε. This effect, however,
is also present in any other Euclidean distance algorithm.
As for the implementation of dbscan, it is clear that the greatest difficulty lies in deciding the values
that eps and minPts will have to assume. An additional difficulty lies in the fact that depending on the
device, configurations and possible data distributions will change from time to time. Since it is not
possible to predict what kind of data we will have available, it was decided to test a range of values
each time. This range for minPts goes from 5 to 400, while for eps, which will be tested on the basis of
the best minpt, a value between 0.01 and 0.09 will be chosen if we have less than 51 for minPts,
otherwise a value between 0 , 1 and 2. These choices are supported by the silhouette coefficient [17]:
an important metric that is calculated using the mean intra-cluster distance (a) and the nearest mean
distance (b) for each sample. The silhouette coefficient for a sample is (b - a) / max (a, b), where b is
nothing more than the distance between a sample and the nearest cluster of which the sample is not part.
The best value is 1 and the worst value is -1. Values close to 0 indicate overlapping clusters. Negative
values generally indicate that a sample was assigned to the wrong cluster, as a different cluster is more
similar. In the end, therefore, dbscan will be set with the best values based on the silhouette coefficient,
using the Euclidean metric, most used metric, such as in [18] where is used as fitness function to control
the process of parameters determination by optimization, or in [19] used to classify the type of text
contained in the Al-Quran, or as confirmed by [20] and [21]. Obviously, all these operations were
performed on normalized data.
Based on some tests carried out, it was decided to work week by week on the data. This choice
derives from the fact that the tests carried out have shown that less than 5 days produce inaccurate
results, probably due to the scarcity of available data. The best results are obtained when we have more
and more data available, but unfortunately the time required for execution would increase significantly.
For this reason, the best result-time compromise was reached with 7 days (Silhouette Coefficient> 0.90).
2.3. Data Visualization
Once the classification of the available GPS data is done, it is necessary to visually visualize these
results. Powerful visualization tools and libraries are available nowadays. In this work the Folium
library has been adopted. It is a powerful data visualization library in Python created primarily to help
people visualize geospatial data. Maps are interactive so that zoom in and out are available. Folium will
be used to create an interactive map that shows Cluster and Outlier in the most understandable way for
the user (in this case the parent). More precisely, four different maps will be created:
1. Interactive map containing all the clusters of the week: in this map it will be possible to view all
the clusters in the form of a heatmap, with the addition of a marker on the positions where there are
more concentrations of data. This marker will be clickable and will give the user the possibility to see
the city, the postcode, the street and possibly also the name of the place where it is located. We also
tried to give continuity to the user's movement: the various points of the map were connected based on
the time that elapses between them. It was decided to combine the points, based on the recording of
which they took place with a time frame of less than 10 minutes (obviously other times were also tested
before arriving at this choice). This choice is due to the fact that it is possible for a person to stop at a
traffic light, encounter a traffic accident or simply make a stop, without changing your final destination.
2. Interactive map containing all the clusters of the week, sorted by time: Within the map the points
are always displayed in the form of a heat map. It also contains a slider where the user (the parent) can
move according to the time and view the precise instant in time with the precise point where the position
was recorded. The parent will also be given the opportunity to decide whether to view a specific day on
which to view the data.
3. Interactive map containing all outliers for the week: identical to the first map, but with outliers
instead of clusters.
4 Interactive maps containing all outliers for the week, sorted by time: identical to the second map,
but with outliers instead of clusters.
As for the pop-up in which the data relating to the point produced by the coordinates are displayed,
a reverse geocoding technique has been implemented. For this technique, the 10 most recurring points
were taken (counting the occurrences present in the Data frame) and finally they were fed to Nominatim,
a function present in the geopy.geocoders library that returns all the details of the position.
This process was repeated for all four maps. Another action carried out was to create a dictionary,
sorted by key (date and time) sent to the "heatmapwithtime" folium to create maps 2 and 4 (Heatmap
with time by cluster and outlier). As for this last feature, the user will be given the opportunity to view
a certain day. This was possible simply by creating an ad hoc Data Frame, selecting only the values
that contain the date entered by the user.
It is important to underline that the user is also given the possibility to disable the heat maps, in case
they prevent them from being correctly displayed.
An example of a user display is shown in fig. 2 and 3, where in figure 2, the 2 main clusters identified
by a user and the relative usual paths performed by him can be observed, while in figure 3, a path carried
out by the user can be observed, identified as anomalous, in particular it is a deviation commute from
home to work.
Figure 2: Graphic representation of clusters
Figure 3: Graphic representation of an anomalous path identified
3. Experiments
Two methodologies were considered to ascertain the accuracy of the system: the first relies on
clustering evaluation metrics, while the second is based on questionnaires administered to users. Two
methodologies were used because with simple clustering assessment metrics, it cannot be said for sure
whether the locations and paths identified as frequent or unusual by a user have been correctly
classified. An answer that only the user can provide.
Regarding clustering evaluation metrics, three metrics were chosen [17]:
• Davies-Bouldin score: The score is defined as the average similarity measure of each cluster
with its most similar cluster, where similarity is the ratio of within-cluster distances to
between-cluster distances. Thus, clusters which are farther apart and less dispersed will
result in a better score. The minimum score is zero, with lower values indicating better
clustering.
• Calinski and Harabasz score: It is also known as the Variance Ratio Criterion. The score is
defined as ratio between the within-cluster dispersion and the between-cluster dispersion.
• Silhouette Coefficient: it is calculated using the mean intra-cluster distance (a) and the mean
nearest-cluster distance (b) for each sample. The Silhouette Coefficient for a sample is (b -
a) / max (a, b). To clarify, b is the distance between a sample and the nearest cluster that the
sample is not a part of. Note that Silhouette Coefficient is only defined if number of labels
is 2 <= 𝑛_𝑙𝑎𝑏𝑒𝑙𝑠 <= 𝑛_𝑠𝑎𝑚𝑝𝑙𝑒𝑠. The best value is 1 and the worst value is -1. Values
near 0 indicate overlapping clusters. Negative values generally indicate that a sample has
been assigned to the wrong cluster, as a different cluster is more similar.
Nine users were involved in the testing phase. The monitoring period ranges between 10 and 14
days. At the end of this period, a 9-question questionnaire was administered to the users. A sub-set of
questions related to the veracity of the displayed data of the paths and places identified as normal and
anomaly. A 5-value Likert Scale (from Strongly Disagree to Strongly Agree) was adopted, each answer
was then associated with a value (0, 25, 50, 75, 100). The average was adopted to estimate the degree
of accuracy [22].
4. Results
The silhouette score was calculated for each user obtaining an average value of 91%, with a
minimum value of 73% and a maximum value of 98%. (table 1).
Table 1
Silhouette results
User Silhouette %
1 98%
2 88%
3 93%
4 88%
5 88%
6 95%
7 96%
8 73%
9 98%
Tot. 91%
The accuracy related to the evaluation of anomalies reported by the system and evaluated by users
reached a value of 87.5%. This result is similar to the silhouette score and therefore confirms the
previous classification data.
5. Conclusion and future development
In order to monitor the movements of children from infancy to adolescence, an android app and a
web platform have been developed. The Andorid app was used with the aim of acquiring the gps
parameters related to the children's smartphones, while the web platform was used to visually show the
user the data acquired by the app and the results of the analyzes relating to the identification of any
anomaly places or routes. The difference between this solution and those already on the market lies
precisely in the fact that current solutions usually allow simple real-time monitoring or movement
history, while our solution automatically identifies any anomalous routes, supporting parental control.
For this purpose, DBScan was used as a clustering algorithm, and FoliumMap and Flask for the creation
of the web platform. The overall system was tested on 9 users, demonstrating an accuracy of 87.5%,
confirming its possible use in real contexts.
Of course, in the next studies it is of primary importance to considerably extend the test sample to
validate the results currently obtained and, to extend the web platform implemented by integrating with
works that use other reference data other than GPS parameters, such as other sensors such as the
accelerometer. In fact, alone, the results obtained from the GPS parameters are not always sufficient to
affirm a phenomenon of bullying or cyberbullying, for this reason, please note that the following work
is only part of a larger project in progress, where with the use of multiple sensors, apps and other
technologies, it will be possible to identify these phenomena.
6. Acknowledgements
This work is supported by the Italian Ministry of Education, University and Research within the
PRIN2017 - BullyBuster project - A framework for bullying and cyberbullying action detection by
computer vision and artificial intelligence methods and algorithms. CUP: H94I19000230006.
7. References
[1] M. Group, «Internet Top 20 Countries-Internet Users 2020.,» 30 June 2019. [Online].
Available: https://www.internetworldstats.com/top20.htm.
[2] Q.Li., «Cyberbullying in schools: A research of gender differences,» School Psychol, vol.
27, n. 2, pp. 157-170, 2006.
[3] M. Dadvar e F. De Jong, «Cyberbullying detection: A step toward a,» 21st Int. Conf.
Companion World Wide Web ( WWB ), pp. 121-125, 2012.
[4] L. Robinson, « Bullying and Cyberbullying,» 5 March 2020. [Online]. Available:
https://www.helpguide.org/articles/abuse/bullying-and-cyberbullying.htm.
[5] P. S. Storm e R. D. Storm, «Cyberbullying by adolescents:A preliminary assestment,»
Educ. Forum, vol. 70, n. 1, pp. 21-36, 2006.
[6] L. Betts, T. Baguley e S. Gardner, «Examining adults' participant roles in cyberbullying,»
J. Social Pers. Relationships, vol. 36, n. 11-12, pp. 3362-3370, 2019.
[7] R. Ortega, P. Elipe, J. Mora-Merchán, J. Calmaestra e E. Vega, «The emotional impact on
victims of traditional bullying and cyberbullying:A study of Spanish adolescents,» Zeitschrift
Psychologie/J.Psychol., vol. 217, n. 4, pp. 197-204, 2009.
[8] F. Shaikh, . M. Rehman e A. Amin, «Cyberbullying: A Systematic Literature Review to
Identify the Factors Impelling University Students Towards Cyberbullying,» 21 Aug. 2020.
[Online].
[9] N. Covertini, N. Logrillo, F. Manca e T. Palmisano, «Recommendation System using
Hybrid Fuzzy Association Rules for Human Smart Cities,» 2018 AEIT International Annual
Conference, Bari, Italy, 2018.
[10] D. Impedovo, F. Balducci, V. Dentamaro e G. Pirlo, «Vehicular Traffic Congestion
Classification by Visual Features and Deep Learning Approaches: A Comparison,» Sensors,
vol. 19, n. 5213, 2019.
[11] D. Impedovo, V. Dentamaro, G. Pirlo e L. Sarcinella, «TrafficWave: Generative Deep
Learning Architecture for Vehicular Traffic Flow Prediction,» Sensors, vol. 9, n. 5504, 2019.
[12] N. Convertini, N. Dentamaro, D. Impedovo, G. Pirlo e L. Sarcinella, «A Controlled
Benchmark of Video Violence Detection Techniques,» MDPI information, vol. 11, n. 321,
2020.
[13] V. Dentamaro, D. Impedovo e G. Pirlo, «Gait Analysis for Early Neurodegenerative
Diseases Classification Through the Kinematic Theory of Rapid Human Movements,» IEEE
Access, vol. vol. 8, pp. 193966-193980, 2020.
[14] K. S. do Prado, «How DBSCAN works and why should we use it?,» 2 Apr 2017. [Online].
Available: https://towardsdatascience.com/how-dbscan-works-and-why-should-i-use-it-
443b4a191c80.
[15] M. S. Suchithra e M. L. Pai, «Data Mining based Geospatial Clustering for Suitable
Recommendation system,» 2020 International Conference on Inventive Computation
Technologies (ICICT), Coimbatore, India, pp. pp. 132-139, 2020.
[16] M. Perumal e B. Velumani, «Design and development of a Spatial DBSCAN Clustering
framework for location prediction- An optimization approach,» 2018 3rd International
Conference on Communication and Electronics Systems (ICCES), Coimbatore, India, pp. pp.
942-947, 2018.
[17] Mohantysandip, «A Step by Step approach to Solve DBSCAN Algorithms by tuning its
hyper parameters,» 12 May 2020. [Online]. Available:
https://medium.com/@mohantysandip/a-step-by-step-approach-to-solve-dbscan-algorithms-
by-tuning-its-hyper-parameters-93e693a91289.
[18] M. Li, X. Bi, L. Wang e X. Han, «A method of two-stage clustering learning based on
improved DBSCAN and density peak algorithm,» Computer Communications, vol. 167, pp.
pp. 75-84, 2021.
[19] M. A. Ahmed, H. Baharin e P. N. E. Nohuddin, «Analysis of K-means, DBSCAN and
OPTICS Cluster algorithms on Al-Quran verses,» International Journal of Advanced
Computer Science and Applications, vol. vol. 11, n. n. 8, pp. pp. 248-254, 2020.
[20] G. Huang, W. B. Qu e H. Y. Xu, «Traffic Accident Location Clustering Based on Improved
DBSCAN Algorithm,» Jiaotong Yunshu Xitong Gongcheng Yu Xinxi/Journal of
Transportation Systems Engineering and Information Technology, vol. vol. 20, n. n. 5, pp. pp.
169-176, 2020.
[21] U. Pandya, V. Mistry, A. Rathwa, H. Kachroo e A. Jivani, «2DBSCAN with Local Outlier
Detection,» International Conference on Recent Advancement in Computer, Communication
and Computational Sciences, RACCCS 2019, Ajmer, India, vol. vol. 1097, pp. pp. 255-263,
17 August 2019.
[22] E. R. S. H. Saputra, E. Utami e A. Nasiri, «Implementation of Location Based Service on
Monitoring System of Visually Impaired Position with A-GPS Method,» 2018 3rd
International Conference on Information Technology, Information System and Electrical
Engineering (ICITISEE), pp. pp. 271-275, 14 November 2018.