PlaceProﬁle: Employing Visual and Cluster Analysis to Proﬁle Regions

based on Points of Interest

Rafael Mariano Christ

ofano, Wilson Est

ecio Marc

ılio J

unior

and Danilo Medeiros Eler

ao Paulo State University (UNESP), Presidente Prudente/S

ao Paulo, Brazil

Keywords:

Area Proﬁling, Smart Cities, Smart Mobility, POIs, Clustering, Visualization, Google Maps.

Abstract:

Understanding how commercial and social activities and points of interest are located in a city is essential to

plan efﬁcient cities in smart mobility. Over the years, the growth of data sources from distinct online social

networks has enabled new perspectives to applications that provide mechanisms to aid in comprehension of

how people displaces between different regions within a city. To support enterprises and governments better

understand and compare distinct regions of a city, this work proposes a web application called PlaceProﬁle to

perform visual proﬁling of city areas based on iconographic visualization and to label areas based on cluster-

ing algorithms. The visualization results are overlayered on Google Maps to enrich the map layout and aid

analyst in understanding region proﬁling at a glance. Besides, PlaceProﬁle coordinates a radar chart with areas

selected by the user to enable detailed inspection of the frequency of categories of points of interest (POIs).

This linked views approach also supports clustering algorithms’ explainability by providing inspections of the

attributes used to compute similarities. We employed the proposed approach in a case study in the S

ao Paulo

city, Brazil.

1 INTRODUCTION

Since the work of (Ravenstein, 1885), researchers

have been focused on understanding displacement

patterns to identify how people need to move in a re-

gion. With the growth of big cities, the increase in

population, society’s evolution, and technology inno-

vation, cities have become more diverse and complex

than ever. The world is increasingly interconnected.

Accordingly, the displacement of people to carry out

their daily activities has become a major challenge.

Therefore, creating solutions to improve mobility so

that people can move from one point to another in an

agile and safe way has been a challenge for local gov-

ernments to manage (D’Andrea et al., 2018). Thus,

city planning is closely related to human mobility in

an urban territory. As a result, this planning directly

inﬂuences the population’s access to services such as

hospitals, schools, parks, and events.

Urban mobility is related to the movement of peo-

ple and goods in a city, with the objective of de-

veloping economic and social activities in the ur-

ban areas, urban agglomerations and metropolitan re-

https://orcid.org/0000-0002-8580-2779

https://orcid.org/0000-0002-9493-145X

gions (Silva, 2014). In the past, to understand mobil-

ity and activities in a city, researchers collected sur-

vey data on small samples and low frequencies. Cur-

rently, information about activities in a region can be

extracted through data from collaborative social net-

works, in which users enter data on routes, trips, pur-

chases, points of interest, as well as the data acqui-

sition from Internet of Things (IoT) devices. With

this information, government ofﬁcials, authorities and

entrepreneurs can understand the organization of re-

gions in a city as well as plan its development. For

example, it is possible to label and compare different

regions of a city according to the activities employed

in an area under analysis.

In the literature, several works present approaches

to analyse the urban mobility (Batty, 2009), (Clara-

munt et al., 2000), (Demissie et al., 2013), (Jiang

et al., 2012), and to label regions (Andrienko et al.,

2013), (Song and Miller, 2012), (D’Andrea et al.,

2018), (Jiang et al., 2012). Usually, those works use

machine learning techniques (i.e., classiﬁer and clus-

tering) to deal with data acquired from geolocated

data of IoT devices, sensors, points of interest, online

posts, trafﬁc information, and other data sources. To

improve the user analysis, information visualization

technique are employed to enhance map layouts and

506

Christófano, R., Marcílio Júnior, W. and Eler, D.

PlaceProﬁle: Employing Visual and Cluster Analysis to Proﬁle Regions based on Points of Interest.

DOI: 10.5220/0010453405060514

In Proceedings of the 23rd International Conference on Enterprise Information Systems (ICEIS 2021) - Volume 1, pages 506-514

ISBN: 978-989-758-509-8

present visualizations on the city maps. While cate-

gorizing city regions based on clustering techniques

can provide information about city patterns, such an

approach might hide important information due to ag-

gregation.

This paper presents a web-based application

called PlaceProﬁle to aid in proﬁling and character-

izing city areas based on visual analysis of points of

interest (POIs) of regions in a city. Given a set of POIs

and their categories, the proposed approach uses an

iconographic visualization to proﬁle areas based on

the main categories of POIs, and employs clustering

techniques to label areas of a region based on the fre-

quency of the POIs present in each area. The icono-

graphic approach extends clustering analysis power

by showing how POIs in different city areas are re-

lated and showing the information details that could

be used to different clustering results. PlaceProﬁle

also presents a linked views strategy to coordinate the

map clustered areas with a radar chart visualization.

To augment the interpretability power of clustering

results, this coordination mechanism also provides a

way to explain the clustering results to aid analysts in

a detailed analysis by showing predominant POI ac-

tivities for each area. We provide a use case to demon-

strate how PlaceProﬁle can be employed to analyze

areas of S

ao Paulo, Brazil.

Summarily, the main contribuitions of PlacePro-

ﬁle are:

• A iconographic visualization approach to show at

a glance the main activities of areas in a city;

• A coordination approach between the areas of in-

terest and a radar chart to aid in explainability of

clustering and proﬁling results.

The remaining of this manuscript is organized as fol-

lows: in Section 2, we review related works on ur-

ban mobility focusing on data mining visualization

approaches; in Section 3, we present our tool by de-

tailing all of the pipeline involving in data preparation

and visualization; a case study is presented in Sec-

tion 4; we conclude our work in Section 5.

2 RELATED WORKS

The ﬁrst applications of visualization systems for the

analysis of urban mobility were based on the re-

sources of Geographic Information Systems (GIS)

and traditional visualization methods (bar and line

charts) with limited interaction capabilities (Clara-

munt et al., 2000). The advent of new technologies

for research and development of visual representa-

tions ( such as D3.js

and Google Maps API

) ac-

celerated the number of applications for knowledge

discovery considering data from Smart Cities (Sobral

et al., 2019).

Usually, human displacement is analysed from

various perspectives, such as vehicle trafﬁc, people

movement dynamics, incidents, activities in regions

of a city, and daily patterns of human activities. In

the domain of applications for vehicle trafﬁc analy-

sis, (Andrienko et al., 2013) proposed an application

of street diagrams with time-space to analyse urban

trafﬁc congestion in the city of Helsinki, Finland. Us-

ing a ﬂower visual metaphor, the authors used a rose

chart in which the segments of the circle represent

the hours of the day, and such segments represent the

number of trafﬁc jams and the segment size represents

the time duration of the trafﬁc jams.

Heat maps are usually employed to analyse vehi-

cle trafﬁc, which are superimposed on a geographic

map of the region to be analysed, (Song and Miller,

2012), (Liu et al., 2013), (Pu et al., 2013). The use of

such a technique presents the user with the real per-

ception of the place with the highest incidence of ve-

hicle congestion.

In the domain of applications that analyse the dy-

namics of displacement of people, that is, what makes

a person or group of individuals move from point

A to point B, has concentrated on mandatory urban

points. For example, in the work of (Sagl et al., 2012),

researchers sought to better understand the typical

space-time patterns of collective human mobility on

the operational scale of a city and its periphery, so

that the work was able to reveal similarities and dif-

ferences in functional conﬁguration of cities in terms

of mobility. In the work of (Demissie et al., 2013),

the researchers analysed data obtained by mobile tele-

phony, speciﬁcally, data known as call details record

(CDRs), to understand the process of downloading a

mobile call on the move, that is, when an active con-

nection is switched from one transmission tower to

another. The objective was to emphasize the neces-

sary points in the coverage of the tower signal, to de-

tect the points of cellular congestion and human mo-

bility patterns.

For applications that analyse trafﬁc incidents, (Al-

bino et al., 2015) and (Pack et al., 2009) performed

analyses on incident data acquired by departments re-

sponsible for public administration. In (Pack et al.,

2009), the researchers developed an application that

offers an analysis of the data sets of transport inci-

dents. The tool offers the user an intuitive set of fea-

tures that includes data ﬁltering, geospatial visualiza-

https://d3js.org/

https://cloud.google.com/maps-platform/

PlaceProﬁle: Employing Visual and Cluster Analysis to Proﬁle Regions based on Points of Interest

507

tions, statistical classiﬁcation functions and multidi-

mensional data exploration features.

In the domain of applications with the objective of

labelling the activities in regions of a city, (D’Andrea

et al., 2018) used data collected from various sources

to extract signiﬁcant characteristics for identifying ac-

tivities in areas of a city. Moreover, other works have

tried to understand patterns from data to answer some

questions, such as ’What is the activity that the user is

performing based on her geographical position? and

’What is the purpose of her movement?’ (Xiong et al.,

2014), (Hung and Peng, 2011), (Paul et al., 2013). Al-

though these researchers raise a few important ques-

tions, these surveys are limited to identifying activ-

ities in speciﬁc locations and the data produced in

these works were not made available so that other sur-

veys could include data on mobility in order to under-

stand why people move.

And ﬁnally, applications that seek to identify

daily patterns of human activities in order to improve

transport logistics for school, work, leisure, among

other. (Jiang et al., 2012), for example, developed

a survey on the routes of residents in the Chicago

metropolitan area, to analyse the data collected, the

researchers used the Principal Component Analysis

(PCA) method and the clustering algorithm K-means,

in this work the researchers concluded that the tech-

niques were effective in analysing the correlation be-

tween the variables and in identifying similar groups

respectively. In their most recent work (Jiang et al.,

2017), the researchers used data produced by mobile

device, known as Call Detail Records (CDR) to exam-

ine the mobility patterns of anonymous individuals in

the metropolitan region of Singapore.

3 PLACEPROFILE:

UNDERSTANDING PATTERNS

BASED ON POINTS OF

INTEREST

Many complex data containing information about

places of interest in a city, cost of living, trafﬁc, and

preferences are produced daily. Most data sources on

the web, in general, provide information about any

city or geographic region, while other data sources are

speciﬁc to a particular city (D’Andrea et al., 2018).

Understanding how this enormous amount of data can

be used and the analytical process can help decision-

makers.

This paper proposes a web application tool called

PlaceProﬁle, which uses information on points of in-

terest to proﬁle and label areas of a city. PlacePro-

ﬁle enables visual metaphors and uses clustering algo-

rithms to support users to identify the main activities

of different regions of a city or metropolitan region.

Figure 1 shows the PlaceProﬁle’s architecture, which

consists of four functional components: Preparation,

Data Collect, Data Mining Analysis, and Visualiza-

tion.

The Preparation components (see Figure 1 (a))

consists of deﬁning parameters that will be used for

the Data Collect step. Firstly, the user deﬁnes a re-

gion for analysis using the zooming feature – a fea-

ture inherited from Google Maps API. Then, cells of

the same size (in meters) are used to impose micro-

regions on top of the user-deﬁned region. Notice that

such a process will result in a grid on top of the region

being analysed. Thus, cells (areas) with a smaller size

can be used if one wants a more ﬁne-grained analysis.

The data about the user-deﬁned region is collected

in the Data Collect step (see Figure 1 (b)) by using

Google Place API library. Table 1 shows an example

of the collected data for a region, where Type corre-

sponds to a classiﬁcation assigned to a point of in-

terest with 130 possible values, and User Rating is

the evaluation assigned by users – the user rating goes

from 1 (bad) to 5 (good). Usually, the data is extracted

in batches, stored in a database, and later processed.

Table 1: Raw data collected from Google Places.

Description Sample value

ID ChIJfUjHo(...)

Name Museu Paulista

Geo. coordinates (-23.5855993, -46.6097431)

Type museum, establishment

User Rating 4.6

During the Data Collect process, the collected points

of interest (POIs) are visualized on the grid (as shown

in Figure 2) using red circles to encode the density

of POIs in each cell. The red points representing the

POIs are positioned in their respective geographical

location.

In the Data Mining Analysis step (see Figure 1

(c)), all of the 130 types of categories retrieved from

Google Places are grouped into eleven macro cate-

gories (D’Andrea et al., 2018), as show in Table 2,

enabling easier selection or deletion by users for lat-

ter analysis. For each cell, the number of macro

categories is retrieved to compute features (see Ta-

ble 3) for clustering algorithms and iconographic vi-

sualization. PlaceProﬁle allows clustering using k-

Means (MacQueen et al., 1967), c-Means (Dunn,

1973), and Agglomerative Clustering (Frigui and Kr-

ishnapuram, 1997).

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Figure 1: PlaceProﬁle consists of a web application visual data mining of points of interest. The main components of

PlaceProﬁle are Preparation, Data Collect, Data Mining Analysis, and Visualization.

Figure 2: PlaceProﬁle: The collected data (red dots) are

plotted on the grid in their respective cell overlaid on the

map.

Importantly, PlaceProﬁle allows the inclusion of

other features for later analysis, such as geographic

coordinates, a cell identiﬁer, or cell location in the

grid. Besides that, users can discard cells with only a

few POIs to perform analysis using only informative

cells. Finally, the only required parameter is the num-

ber of clusters used to analyse the division imposed

by the clustering algorithm in the cells, that is, how

many k distinct areas on the data will be labeled to

create the proﬁle for the user-deﬁned region.

The data resulting from the mining process is

stored in a database to facilitate the deﬁnition of

different visualization strategies. For example, one

may use the processed data to create a visualization

approach that emphasizes the reasons that citizens

change location in a city of a metropolitan region.

In the Visualization step (see Figure 1 (d)), three

different strategies are used to represent the results of

the previous steps: (i) Iconograph Analysis, (ii) Clus-

(a)

(b)

Figure 3: Visualizing the most common macro categories.

Given a region divided into cells, we divide the cell space

using a grid (a) and color code the grid based on the propor-

tion of macro categories (b). Ordered from top to bottom,

the last color (brown) represents the remaining macro cate-

gories aggregated.

ter Analysis, and (iii) Radar Chart Analysis.

Figure 3 shows six cells (areas) in the central re-

gion of S

ao Paulo before any analysis. To design the

Iconograph representation, each cell is divided into a

hundred equal parts (see Figure 3), which creates an

internal grid in the cell (a). Then, for each cell, the

three main macro categories are highlighted by re-

ceiving proportional visual space according to their

PlaceProﬁle: Employing Visual and Cluster Analysis to Proﬁle Regions based on Points of Interest

509

Table 2: Clustering Google Maps categories into macro cat-

egories (Macro Cat.).

Macro

Category Category from Google Maps

Food

bakery, bar, cafe, food, liquor store,

meal delivery, meal takeaway,

restaurant

Finance accounting, atm, bank, ﬁnance

Admin.

city hall, courthouse, embassy, police,

ﬁre station, local government ofﬁce

Transport.

airport, bus station, subway station,

taxi stand, train station,

transit station, light rail station

Cultural

art gallery, library, school,

university, movie theater, museum

Entert.

night club, amusement park,

bowling lley, campground, zoo,

aquarium, stadium, casino

Health

pharmacy, physiotherapist,

beauty salon, dentist, doctor,

gym, hair care, hospital,

veterinary care, health, spa

Services

travel agency, funeral home,

park, post ofﬁce, parking,

rooﬁng contractor, locksmith,

general contractor, lodging,

moving company, car repair,

electrician, car rental, laundry,

gas station, plumber, painter,

real estate agency, lawyer,

recreational vehicle park,

insurance agency, car wash

Religious

mosque, cemetery, church,

hindu temple, synagogue,

place of worship

Stores

shoe store, shopping mall,

pet store, bicycle store,

book store, car dealer, , movie rental

clothing store, jewelry store,

ﬂorist, store, furniture store,

convenience store, department store,

electronics store, hardware store,

home goods store, storage,

grocery or supermarket

Misc.

point of interest, establishment,

country, ﬂoor, intersection,

locality, natural feature, geocode,

colloquial, area, room, post box,

neighborhood, postal code,

postal town, political,

postal code preﬁx and sufﬁx,

premise, route, street address,

subpremise, street number

occurrence in the cell (b). The most prominent POI

macro category appears at the top of the cell, then the

second highlighted macro category, the third macro

category highlighted comes next, and the fourth repre-

sents all the remaining macro categories aggregated.

The result of dividing cells according to their most

common macro category is shown in Figure 4 for the

six cells presented in Figure 3. Notice that each color

represents a macro category and the color occupation

in each cell represents the proportion of activities re-

lated to that macro category. Finally, the sum of all

the remaining macro categories is shown in propor-

tion and represented by the brown color. In this exam-

ple, the purple color represents the services macro cat-

egory, red is related to stores, yellow represents food,

green represents health, blue represents cultural, and

brown represents all other macro categories present in

the cell. The analyst can notice the categories that are

not predominant in each cell by looking at the legend

that describes each macro category employed in the

visualization.

Figure 4: Using colors to encode the most common macro

category in a cell. This region is particularly represented by

services and stores macro categories.

In the cluster analysis view, each cell is labeled with

a different color for each data group partitioned by

the clustering algorithm. Figure 5 shows the result of

cluster analysis on the same six cells highlighted in

Figure 3. Notice that ﬁve out of the six cells belong to

the same cluster (cells in red) and one cell has differ-

ent features (cell in green). The user can understand

the pattern adopted by the clustering algorithm to sep-

arate groups of data. We must highlight that the colors

in the cluster analysis do not relate to the colors used

for encoding the macro categories.

To complement the analysis of macro categories

and assist in the interpretation of clustering results,

PlaceProﬁle also uses a Radar Chart visualisation to

help users to perceive predominant activities for each

cell. Interactivity is present in this view, allowing the

user to select cells of interest to analyse and make

comparisons. Figure 6 shows the differences between

the two groups, the red cells stand out for macro cat-

egories of services and store activities while in the

green cell the highlight is for stores, services, and

health.

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Table 3: The table illustrates a sample of the summary of the macro categories per cell: the count of the total of the 11

macro categories in each cell creates the attributes that will be passed as a parameter for the grouping algorithm and for the

iconographic analysis.

Macro categories Count

Cell Id GPS coordinates (lat, lng) Food Finance Administrative Transport Cultural

1 -23.532288, -46.671019 5 10 3 3 5

2 -23.532288, -46.668570 14 1 0 2 2

3 -23.532288, -46.666120 16 3 1 1 6

4 -23.532288, -46.663671 6 9 4 2 0

Figure 5: Clustering analysis. Based on the features re-

trieved during Data Collect step, cells are clustered to help

analysis based on similarity.

Figure 6: Radar Chart showing the proportion of macro cat-

egories for the two clusters presented in Figure 5. Although

very similar in services, stores, and food, these two cluster

differ in cultural, health, and ﬁnance macro categories.

Implementation. The PlaceProﬁle front-end was

developed using HTML5, the website was styled us-

ing the CSS3 language and Javascript was used to

perform the interactivity actions of the website. Be-

sides, Bootstrap (https://getbootstrap.com/) libraries

were used to optimise the styling process, JQuery

library (https://jquery.com/) for event handling and

interactivity with External APIs. On the PlacePro-

ﬁle backend, the language used was Python (https://

www.python.org/), along with Pandas (https://pandas.

pydata.org/) library for the cleaning process and raw

data mining and scikit-learn (https://scikit-learn.org/)

for the application of clustering algorithms. We used

Google Maps API for the construction of the visual-

izations, graph rendering, and maps.

4 RESULTS

To validate PlaceProﬁle, this section presents an anal-

ysis of POIs collected from the central region of S

Paulo city, Brazil. For this analysis, we deﬁned cells

with a size of 250 × 250 meters, generating a grid

with 527 cells overlapping the region of interest. Af-

ter data collection, 27454 POIs were identiﬁed. We

removed the miscellaneous macro category since it is

not related to any other macro category, resulting in

ten macro-categories for analysis.

Figure 7 shows the most common macro cate-

gories for each cell in the region of interest. We can

see a lot of patterns in this region. Firstly, the eastern

region is represented by the most POIs categorized

by health (due to the amount of green in the cells).

Second, the northeast of the region shows macro cat-

egories related to stores (reddish cells). Finally, POIs

related to the food (in yellow) and services (in purple)

can be seen throughout the whole region, although

more concentrated in the center. Cells with POIs be-

low a minimum threshold (in this case, one) receive

black color.

Next, we proceed to analyze the similarity among

the POIs collected for this region with clustering

analysis. Using the same data and the same pre-

processing steps discussed above, we performed the

k-means clustering algorithm. Figure 8 (top) shows

the result of such clustering, where each color corre-

sponds to a different cluster. Notice that the clustering

result shows disconnected components in the visual

PlaceProﬁle: Employing Visual and Cluster Analysis to Proﬁle Regions based on Points of Interest

511

Figure 7: Using PlaceProﬁle to get an overview of a region of a metropolitan region. We can see mainly a division between

POIs related to stores and POIs related to health. Black cells correspond to regions with number of POIs below a minimum

threshold.

Figure 8: Similarity analysis based on clustering results. Selected cells shows that blue cluster represents POIs related to store

macro category while red cluster represents POIs related to health and services macro categories.

Figure 9: Selecting different clusters to understand patterns of POIs. Cluster green seems to have POIs related to store and

services macro categories, while cluster yellow corresponds to POIs related to the macro category health.

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representation since the k-means was performed in

a high dimensional space computed with the macro-

categories but without the GPS coordinates.

One of the problems of analysing the result of a

clustering algorithm is that information about which

features led to the clustering patterns is usually lost.To

explain how the information in the regions led to clus-

ters formation, a Radar Chart showing the propor-

tion of POIs in each region can be used through a

coordination mechanism. Figure 8 (bottom) shows

the Radar Chart encoding the proportion of POIs for

the red and blue cells selected and highlighted in the

grid visualization with thicker borders. Thus, we

can know how those selected cells belonging to the

blue cluster present a much higher proportion of POIs

related to the macro category store than any other,

whereas the cells selected from the red cluster present

POIs related to the services and health macro cate-

gories. Notice that this analysis is consistent with the

iconograph representation seen in Figure 7. That is,

while the blue cluster is related to the northeast region

of Figure 7 – cells with POIs highly related to macro

category store, the red cluster is consistent with the

cells showing POIs related to health and services in

the same ﬁgure.

Figure 9 shows the same clustering result but with

different selected cells. Using coordination between

the map and the Radar Chart to help on the cluster ex-

plainability. Using the Radar Chart, the cells from the

green cluster represent POIs related to the macro cat-

egories store and services. Notice that, these cells are

the ones located on the boundaries of the blue cluster

and the neighborhood of the area concentrated with

POIs of store macro category in Figure 7. Lastly, we

see from the Radar Chart that yellow cluster corre-

sponds to POIs related to health macro category. This

information is also perceived using the iconograph

representation of Figure 7.

In this case study, we showed how the Radar

Chart’s explainability mechanism could help users

understand the cluster formation based on the analy-

sis of the proportion of POIs in cells of clusters of in-

terest. Nevertheless, the iconographic approach also

provides analysis improvements since it encodes pat-

terns of cells presenting a similar proportion of POIs

with the same category. The iconographic approach

can provide an overview of data organization and de-

tails about POIs at the same time. Thus, the proposed

approach presents new mechanisms to improve the

cluster-based analysis.

5 CONCLUSION

In this paper, we present PlaceProﬁle, a web-based vi-

sualization tool to identify the proﬁle of areas in cities

or metropolitan regions. PlaceProﬁle allows the def-

inition of regions, granularity of analysis, and other

parameters to assist in analyzing patterns based on

points of interest.

The main advantage of our tool consists of its abil-

ity to provide understanding about the areas being an-

alyzed. First, we augment clustering results by coor-

dinating a map with a Radar Chart that shows the pro-

portion of points of interest in selected cells. Thus,

users can get to know how these cells differentiate

or relate to contributing to cluster formation. Sec-

ond, our iconographic approach extends cluster analy-

sis by showing an overview and detailed information

simultaneously. On a higher level, users understand

the result and possible cluster formation by inspect-

ing the iconographic design’s color patterns. In a de-

tailed analysis, users inspect the proportion of points

of interest inside cluster cells. We show the analysis

power of these two approaches by inspecting a region

in S

ao Paulo, Brazil.

The analysis and interaction mechanisms were

validated through a case study in a region of S

Paulo, Brazil. The approach helped us understand

how the points of interest are organized so that near

areas present similar categories. Moreover, the case

study also demonstrated how the two analysis strate-

gies (clustering and iconographic) are consistent.

We plan to accommodate mobility data to analyze

displacement patterns and recommend points of inter-

est according to the cluster and iconographic proﬁles

in future works.

ACKNOWLEDGMENTS

This work was supported by FAPESP (S

ao Paulo Re-

search Foundation), grant number #2018/17881-3 and

#2018/25755-8.

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