# Using city-bike stopovers to reveal spatial patterns of urban attractiveness

Krystian Baneta Vitalii Naumovb and Rafał Kucharskic CONTACT K. Banet. Email:
<EMAIL_ADDRESS>aDepartment of Transportation Systems, Cracow University of
Technology, Krakow, Poland, ORCiD: 0000-0002-9560-483X;
bDepartment of Transportation Systems, Cracow University of Technology,
Krakow, Poland, ORCiD: 0000-0001-9981-4108;
cDepartment of Transport & Planning, Delft University of Technology, Delft,
the Netherlands, ORCiD: 0000-0002-9767-8883

###### Abstract

We demonstrate how digital traces of city-bike trips may become useful to
identify urban space attractiveness. We exploit their unique feature –
stopovers: short, non traffic-related stops made by cyclists during their
trips. As we demonstrate on the case-study of Kraków (Poland), when applied to
a big dataset, meaningful patterns appear, with hotspots (places with long and
frequent stopovers) identified at both the top tourist and leisure attractions
as well as emerging new places.

We propose a generic method, applicable to any spatiotemporal city-bike
traces, providing results meaningful to understand both the general urban
space attractiveness and its dynamics. With the proposed filtering (to
mitigate a selection bias) and empirical cross-validation (to rule-out false-
positive classifications) results effectively reveal spatial patterns of urban
attractiveness. Valuable for decision-makers and analysts to enhance
understanding of urban space consumption patterns by tourists and residents.

###### keywords:

spatial-data; city-bike; bike-sharing system; tourist hotspots; digital
footprints; urban space; tourist attractiveness

††articletype: RESEARCH ARTICLE

##  1 Introduction

Identifying urban places attractiveness and quantifying it is of high
importance for policymakers, who can better design a city for city users; for
the users, who may know which places are attractive; and for the local
economy, which can find the optimal locations for their businesses. Yet urban
space attractiveness is not at all easy to define, delimit and quantify.
Cities are used by various groups, from daily commuters, through local
visitors, business travellers, to tourists. Each with various activity
patterns, needs and perceptions of urban space attractiveness. Collectively
creating a complex spatial patterns, dynamically changing with emerging trends
and fashions.

In this study we demonstrate how a big spatiotemporal datasets of mobility
traces may be used as a proxy revealing attractiveness of urban spaces. We
contribute to the research stream where large sets of aggregated digital
footprints are analysed to provide novel insights into how people experience
the city (Girardin ., 2009). By utilizing a relatively less exploited dataset
(city bike traces) end exploring its unique feature (so-called stopovers) we
reveal meaningful and valuable spatiotemporal patterns.

###  1.1 Literature review

In this section we first introduce the notions of urban attractiveness for
tourists and local users along with methods to quantify and measure it. Then,
we review a variety of recent methods leveraging on big datasets of digital
footprints and their application to urban attractiveness. Finally we discuss
city-bike systems, and a unique feature of digital-footprints left by city-
bike users - stopovers.

####  1.1.1 Urban space attractiveness

Following the definition of (Biernacka  Kronenberg, 2018), the urban space is
attractive, when one willingly wants to use it and spend her/his time there,
and when this space corresponds with one’s individual needs, expectations and
preferences. Attractiveness of urban space is not at all easy to define,
delimit and quantify (Boivin  Tanguay, 2019), with substantially different
perception of urban space for tourists and locals (Kianicka ., 2006),
notwithstanding both user groups are now better understood thanks to recent
studies. For instance, through the indicators to measure urban quality
proposed by Garau  Pavan (2018), or with ‘City Love Index’, lately introduced
by Kourtit . (2020), which pinpoints attractiveness characteristics based on
perceptions of urban quality by residents and their affinity with city life.
Residents’ urban space consumption is associated mainly with their daily
activities (Gonzalez ., 2008), however its attractiveness for leisure purposes
becomes increasingly significant (Thees ., 2020), better understood (Johnson
Glover, 2013) and quantified (Biernacka ., 2020).

Likewise, the tourists’ behaviour is better understood (for a thorough review
we refer to Cohen . (2014)) through a studies where various segments (Stangl
., 2020), activity-based profiles (Fieger ., 2019), socio-demographic groups
(Md Khairi ., 2019) or groups with specific needs (Lee  King, 2019) are
identified. By means of tourist surveying (Jacobsen ., 2019), stated
preference experiments (González ., 2019), or semi-structured interviews
(Kianicka ., 2006) attractiveness is typically related to a set of site-
specific attributes (Estiri ., 2020) or individual visitors’ perceptions
(Cracolici  Nijkamp, 2009; Lee  King, 2019). Which, in turn, allows for a
refined notion of tourism attractiveness at a national (Mitra, 2020), regional
(Cracolici  Nijkamp, 2009), city (Van der Ark  Richards, 2006), or site
(Jacobsen ., 2019) level. Which however, becomes challenging when within-urban
attractiveness needs to be delimited (Zhu, 2020).

While attractiveness at macro-level can be identified via surveys, observing
tourist movements plays a fundamental role in understanding their behaviour
within the urban space (McKercher  Lau, 2008). To this end, tourists’
spatiotemporal behaviour – encompassing trajectories (movements between
activities) (Zakrisson  Zillinger, 2012) and stops (either at attractions, or
to eat, rest, do shopping, etc.) (Caldeira  Kastenholz, 2020) \- is analysed
with implicit assumption that, in general, consumers of urban space spend more
time in attractive spaces (Gehl, 2011).

In such context, the movement along the multi-attraction itinerary can be
observed (Huang ., 2020), with participation time (Caldeira  Kastenholz, 2020)
or time spent per bloc (Espelt  Benito, 2006) used as attractiveness intensity
indicators. Early attempts to track tourists’ movements using mental maps or
self-completion diaries and surveys were usually time consuming and thus
applied only on small sample sizes (Thimm  Seepold, 2016; Keul  Kühberger,
1997).

####  1.1.2 Digital footprints

Digital footprints are now available in big volumes from numerous sources (J.
Li ., 2018) which, coupled with a new kind of tourist that is avid for online
content and predisposed to share information on social media, allows for a
better understanding of tourist behaviour regarding their spatial distribution
in urban destinations (Encalada ., 2017).

Big volumes of data and its high availability seem to overweight limitations,
mainly inherent selection bias (Salas-Olmedo ., 2018). Consequently, big data
in smart tourism (Y. Li ., 2017) contributes to understanding spatial patterns
around urban tourist destinations and, for instance, to differentiate the
overcrowded places from those with the potential to grow, allowing decision-
makers to revisit planning and managing towards a sustainable ‘smart’ future.

User-generated social media content (photos on Twitter, Instagram, Flickr,
etc. or recommendations and reviews on TripAdvisor or Booking) have been
widely explored in numerous studies (Hasnat  Hasan, 2018; Miah ., 2017; Önder
., 2016; D. Li ., 2018; Giglio ., 2019). Recently Martí . (2021) used
Instagram data to reveal detailed picture of urban areas with most tourism-
related activity – i.e. sightseeing, shopping, eating and nightlife - in
Spanish cities.

While such data may reveal spatial patterns, it does not track the tourist
movements, which requires geo-location data from personal devices (mobile
phones) or vehicles (e.g. rental cars, scooters or bikes). GPS traces were
used e.g. by Girardin . (2009) to provide insights into the attractiveness of
urban space in NYC, by Orellana . (2012) to explore visitor movement patterns
in the Dwingelderveld National Park, by Smallwood . (2012) to understand
distance decay in destination choice. Zheng . (2017) used GPS to predict next
destination within a Summer Palace in Beijing and Ferrante . (2018) tracked
cruise passengers at the destinations.

####  1.1.3 City-bike mobility traces

Lately, bicycle sharing has become an increasingly popular around the world,
making usage datasets big enough to study urban dynamics and aggregate human
behaviour (Froehlich ., 2009). City bike systems store records of trips with
their origins, destination, and travel times in publicly available big
databases, which allows for a rich understanding of mobility patterns
(Cantelmo ., 2019). Number of recent studies have used city-bike data e.g. to
identify potential locations for new stations, estimate bicycle flows and
usage, understand social and demographic context or predict usage in real-time
(Caulfield ., 2017; Eren  Uz, 2020; Frade  Ribeiro, 2014; Imani ., 2014; Salon
., 2019; Tran ., 2015; K. Wang  Akar, 2019). Paralller studies investigate how
bike trips are affected by urban space factors such as: the number of retail
stores and business offices near bike stations (Lin ., 2020), demographic
features (J. Wang  Lindsey, 2019) or land-use (Kutela  Teng, 2019).

How city-bikes are used by tourists was also studied. (Vogel ., 2011)
identified that stations dominating between noon and afternoon were located
directly near tourist hotspots, (Brinkmann, 2020) has shown differences in
city bike usage between tourists and frequent users in Rio de Janeiro and
Miami Beach. Buning  Lulla (2020) revealed different usage patterns between
local residents and visitors, showing that visitors primarily use city-bike
for leisure-based urban exploration, while residents’ use bikes mainly to
commute. However, up to our knowledge, the unique feature of city-bike traces
- stopovers was not exploited so far.

###  1.2 Study overview

Core of the proposed method lays in the concept of a stopover, a short stop
made by a city-bike user during her/his trip. Bicycle is not returned at the
station, but stays with the user during a stopover. Stopovers are typically
short, since for longer stops users typically return bicycle to the docking
station due to time-based fare scheme. Stopover is not related to traffic, as
we explicitly filter traffic-related stops e.g. at traffic lights. With such
notion of stopover we may limit it to non-commuting trips, since commuters
rent bikes to quickly reach the destination rather than to have stopovers.

Users may stopover for variety of reasons. The actual interpretation of
stopover depends on the user type. We introduce city-bike users typology on
figure 1. Since the user details are missing we cannot distinguish commuters
(using city-bike to reach their workplace), from local recreational users
(having a weekend tour around green areas of the city), from business visitors
(using city bike to reach the dinner with a client) and tourists (using city-
bike to visit recommended tourist destinations).

Nonetheless, we hypothesise that if city-bike user stops where she/he does not
need to, it is mainly due to the place attractiveness, which may be either
touristic, recreational, commercial or of any other kind. We further
hypothesise that places where stopovers are frequent and long (following Gehl
(2011)), denoted urban hotspots, are attractive.

While we argue that identified hotspots are meaningful and valuable, we
refrain from naively interpreting them as attractive urban hotspots. Like any
other automated classification method, accuracy of our method is not perfect,
as we illustrate with confusion matrix on fig.2a. The wrong classifications
are either when our method failed to identify actually attractive places (e.g.
places not accessible with bike, or outside of city-bike system) or when it
identified places which are not attractive (where stopovers were not due to
attractiveness, but for other reasons). We argue that validation of our
results is straightforward, since each identified place may be examined
against its true attractiveness relying either on the expert knowledge, other
sources (social media or digital footprints from other sources) or a field
visit.

Figure 1: City bike users classification. While local commuters stop mainly
due to traffic, other user groups are likely to stopover during their trip.
Both visitors and local non-commuters may stop at attractive leisure and
touristic places, as well as to supply some their needs (e.g. shopping).
Places with frequent and long stopovers (hotspots), after empirical
validation, may be used as a proxy of urban space attractiveness.

(a) validating results of our method (rows) against actual space
attractiveness (columns)

(b) comparing our classification with classic static rankings

Figure 2: Accuracy of our classification against actual attractiveness (left)
and classic static methods (right).

Figure 3: Urban space attractiveness in Kraków, Poland. Identified hotspots
were not only the classic points of any tourist itinerary, but also emerging
places not mentioned in travel guides, e.g.: 1. the Dolnych Młynów pubs and
clubs 2. Karmelicka str. gardens, 3. the Dąbski Pond.

###  1.3 Research problem, gap and contribution

We contribute to the stream of research aiming to reveal spatial patterns of
urban attractiveness. Identifying tourist hotspots and determining their
dynamically changing attractiveness level is of crucial importance for
decision-makers, who can now design the city better with attractiveness
perception of residents and tourists in mind. Thus, in rapidly changing urban
landscape, we need a dynamic method adapting to the recent trends and fashions
of tourists (Dunne ., 2011) and residents (Kourtit ., 2020). Classic methods
relying on expert knowledge and costly surveying fail to provide the detailed
picture and are inherently static. Recently, number of methods were proposed
where big volumes of digital footprints were applied to reveal behaviour of
residents and tourists in urban areas. Consequently, the delimitation of
attractive urban spaces has become more detailed and underpinned with users’
behaviour (observed via their digital footprints) and perception (understood
thanks to surveying). The objective of this paper is to demonstrate how this
picture can be improved by using a new source of data and its unique features.

In this paper we exploit the potential of stopovers to reveal the spatial
patterns. We hypothesise that the stopovers are related to the space
attractiveness and verify it on a case-study. Nonetheless, observing stopovers
is challenging. Stopovers cannot be read from social media data, even
geotagged, which does not provide a participation time and, since it requires
users action, content is not posted from all the places perceived as
attractive. Detailed spatiotemporal digital traces are needed to reveal
stopovers, and only the active travel modes (walking, scooters, bicycles,
etc.) allow for unrestricted stopovers. Cars are used by urban space consumers
to a limited extend and most of attractive places are not accessible with car.
Cars can be traced only to their parking spot and public transport passengers
up to their bus stop.

Pedestrians exploring urban space are the least restricted to make spontaneous
stopovers. However tracing pedestrians typically raises privacy issues and big
volumes of personal mobile location data are not easily available. The privacy
issues are partially overcome in a station-based systems, which does not
contain sensitive personal data. Consequently, the stopovers may be easily
observed on a large scale only from the shared systems like scooters and city-
bikes, which is their unique feature. In this study we exploit its potential.

Mining stopovers from detailed trajectories is not trivial. To this end we
propose a novel method which allows first to identify stopovers from
spatiotemporal trajectories and then to filter stopovers clearly not related
to attractiveness. While results of the method needs to validated against
external sources and local knowledge (as illustrated on fig.2a) the revealed
pattern accurately reproduced tourist hotspots of Kraków. While proposed
method works with unlabelled data, the results may be refined when extra
labels (sociodemographics or user-type) are available.

Applying our method to the case of 35 thousands traces from Kraków, revealed a
surprisingly meaningful and correct spatial pattern (fig.3). Not only the main
tourist attractions were properly identified, but also other insightful
findings appeared. We identified a number of hidden gems, known only to
locals, as well as newly emerging places, recently gaining popularity and
often not yet listed on tourist websites. Such places are unlikely to be
timely identified via static studies, relying on expert knowledge (like in
Faracik . (2008)), or surveys (like Kianicka . (2006)) as we illustrate on
fig.2b, which is a central contribution of the paper. We demonstrate this with
three examples on fig.3.

The study contributes to solving the problem of the tourist hot-spots
identification by using the exact statistical data against the traditional
approaches that infer based on unreliable information from the surveys and
opinions of social media users. Identifies a new feature of well-known digital
footprint source, stopovers, widely available only in city-bike traces.

The paper is organised as follows. In the next section we introduce a generic
method to identify stopovers in mobility traces and apply it to the city bike
datasets. We introduce a set of filters to calibrate the method before we
synthesise the data on the spatial grid. In section 3 we illustrate the method
using the example of Kraków, where stopovers identified in 35,000 bike trips
yielded a grid that was validated against actual tourist hotspots. Finally, in
section 4, we synthesise the results and discuss the potential applications
and limitations of the proposed method.

##  2 Method

We first formalize how stopovers are identified in the raw dataset, followed
with a filtering rules, after which only meaningful stopovers remain in the
dataset. Consequently, we aggregate the stopovers over a spatial grid and
classify cells into four levels of attractiveness, the outcome of the method.
The code to read the data from gpx-files, identify stopovers and compute
attractiveness grid is publicly available 111https://github.com/naumovvs/city-
bikes-analysis.

###  2.1 Stopovers

We analyse trip tracks, i.e., chronologically ordered sets of track-points:

$Track=\\{TP_{i}\\}\quad,\quad i=1,\dots,N_{TP}$ (1)

, where each track point TP is defined as the time t and position:

$TP=\langle t,lon,lat\rangle$ (2)

For convenience, we use a dual definition, where trip becomes a set of trip
segments:

$Track=\\{TS_{j}\\},\quad j=1,\dots,N_{TS}$ (3)

and $TS_{j}$ is the $j$-th trip segment of the journey track defined by the
couple of neighbouring track points $TP_{o}$ and $TP_{d}$:

$TS=\\{TP_{o},TP_{d}\\}$ (4)

From each trip segment we read: $t_{s}$ \- the travel duration for the trip
segment [h]; $d_{s}$ \- the distance, calculated with haversine formula [km]
and $v_{s}$ \- the average travel speed, defined as the distance $d_{s}$
divided by the travel duration $t_{s}$ [km/h]. Consequently, the raw trips
data now becomes:

$Trip=\langle ID,Track,t_{tr},t_{idle},d\rangle,$ (5)

where $ID$ is the unique number identifying a trip in the dataset; Track is
the reference to the object representing the GPS track as a set of track
segments; $t_{tr}$ is the total travel time according to the track points data
(the difference between the time moments when the last and the first track
points in the track were read) [h]; $t_{idle}$ is the total idle time during
the journey [h]; $d$ is the travel distance [km].

The total idle time is defined as the sum of travel durations for those travel
segments for which the location has not been changed:

$t_{idle}=\sum_{TS_{idle}}ts,TS_{idle}=\\{TS_{j}:d_{j}=0\\},\quad
j=1,\dots,N_{TS}$ (6)

, where $TS_{idle}$ is a set of all segments within the trip that have zero
travel distance.

Stopovers, central element of the proposed method, are a set of consequent
travel segments with null distance:

$Stopover=TS_{idle}\\{TS_{k},TS_{k+1}ts\\},\quad 1\leq k\leq N_{TS}.$ (7)

Note that, according to the definition, more than one stopover could be
defined within a trip, if travel segments with zero distance are not
consequent segments. We illustrate three selected rides with various number of
stopovers on fig.4 .

Figure 4: Typical spatiotemporal traces of city bike trips with various number
of stopovers. Commute trips typically are with no stopovers (orange) while
leisure or touristic trips are more relaxed and are often intermitted with
stopovers.

###  2.2 Filtering

To make sure that identified stopovers are meaningful, we applied the
following pre-processing sequence:

  1. 1.

The dataset was first cleansed of corrupt records related to signal failures
in GPS transmitters. Trip data were eliminated if the GPS outage lasted at
least five minutes.

  2. 2.

After the first filtering stage, the sample continued to contain many trips
with an average speed of 0 km/h. Therefore, we decided to eliminate all trips
with a null duration or distance, which were most likely related to situations
where a bike was unlocked, but not taken out of the stand, and then locked
again, e.g. because of a technical problem.

  3. 3.

Subsequently, we removed very short trips, where a technical problem was
discovered soon after the bike was rented and the user decided not to continue
with the trip. Based on (Naumov  Banet, 2020), a 50 m threshold was used.

On such filtered sample we identified stopovers using the method proposed
above. For each recorded trip, stopovers were identified with (eq. 7) with
their location and duration. Yet, a heatmap visualization of obtained
stopovers revealed a need for further filtering. To this end we applied a
second stage of filtering using spatial metadata from OpenStreetMap as
follows:

  1. 1.

Bike rental/return stops.
Most evident was the need to filter stopovers in the proximity of BSS
stations, where what our method identified as stopovers were in fact unlocking
and locking the bike and checking its technical condition. We identified
threshold of 7m around BSS station to efficiently eliminate trip starts and
ends from stopovers. We explored the cumulative number of stops as a function
of distance from origins and found a natural cut-off point at 7 meters, after
which the number of stops stabilized (Naumov  Banet, 2020).

  2. 2.

Stops at traffic lights.
Obviously, most of times when city bike users stop is not for sight-seeing,
but at traffic. This had to be filtered with care to obtain meaningful
results. Importantly, in the vicinity of most of Kraków’s tourist attractions
there are no traffic lights, so we could safely assume that stopovers around
traffic lights are not due to attractiveness. Our analysis revealed that the
number of stops stabilized at a radius of 30 m; adopting a greater radius
would lead to discarding stopovers unrelated to the presence of a pedestrian
crossing or intersection.

  3. 3.

Railway crossings stops.
Despite there is just handful of single-level railway crossings in Kraków,
stopovers in their vicinity (definitely non-attractive places) biased the
emerging picture. Since those were just few points, it was easy to identify
them and manually filter at 30m threshold.

  4. 4.

Short, traffic related stopovers.
While above filters were spatial, we decided to apply also a temporal filter,
which we found efficient in filtering short, traffic related stops. Namely, we
found that vast majority of stopovers below 30s were around unsignalized
pedestrian crossings. So we filtered stopovers in vicinity of pedestrian
crossings shorter than 30s

After above stages of filtering, the meaningful spatial patterns started to
emerge, with heatmaps now clearly resembling tourist attractiveness, rather
than a traffic map. Notably, in the above we did not need to map-match traces,
which makes the methods light and generally applicable.

###  2.3 Aggregation

For meaningful and quantifiable visualization, we divided the analysed area
into a number of rectangular fields of a given size $S$. We used a spatial
grid that can be represented as the following matrix:

$Grid=||Field_{ij}||^{S\times S},i=1\dots S,j=1\dots S,$ (8)

where $Field_{ij}$ is the rectangular field representing the part of the
analysed area; $S$ is the grid size (the greater number of cells, the more
detailed results). For each field we get:

$Field=\\{n_{st},t_{st},t_{F}\\},$ (9)

where $n_{s}$ is the total number of stopovers in the field; $t_{st}$ is the
total duration of all the stopovers in the field [h], and $t_{F}$ is the mean
stopover time (calculated only for cells with more than three records):

$t_{F}=\begin{cases}0&n_{st}<3\\\ t_{st}/n_{st}&\text{otherwise}\end{cases}$
(10)

All being potentially useful to reveal the spatial attractiveness. As we
demonstrated for our case-study in the next section, we decided to base
attractiveness on the mean duration of stopovers rather than their number or
total duration. To sharpen the emerging picture, we classified grid cells into
four attractiveness classes, representing quartiles of mean stopover duration
($t_{F}$), where 4-th class represents highest attractiveness and 0-th lowest.
The final outcome of the method is a spatial grid with rank (from 0 to 3) of
urban space attractiveness for each field.

##  3 Results

We illustrate the proposed method with the case of Kraków, Poland, one of
emerging tourism centres in Europe. With its rich history and unique cultural
heritage, the city has attracted a growing number of tourists in the last
decade. For Kraków, tourism is not just an important source of revenue, but
also a major social phenomenon that shapes its urban identity. Tourism in
Kraków has long been concentrated around the historic city centre (Old Town,
Wawel Castle, Kazimierz), i.e. the urban complex inscribed in the UNESCO World
Heritage List. Yet now it extends to neighbouring areas, such as Kleparz,
Krowodrza, Zabłocie, Stare Podgórze, and Nowa Huta, due activities aimed at
decentralizing tourist traffic (Tracz  Semczuk, 2018). Visitors are becoming
more and more heterogenic, spanning from John Paul II related pilgrims, to
city-breakers focused on nightlife, from high-school pupils visiting their
national royal Castle for the first time, to frequent visitors looking beyond
the top-10 sights. These dynamics and diversity yield rapid and complex
patterns which are hard to trace and quantify.

We used the data from the local city bike system, ‘Wavelo’, established in
2008 and gradually expanded until 2019 when its area covered majority of the
city. The records covered one week of the high tourist season of 2017, i.e.,
from 31st of May to 7th of June, when weather was auspicious for bike traffic
and recreation, with mean temperatures between 16 and 21∘C and barely any
rainfalls. The dataset composed of 34,969 tracks of Wavelo city bike users was
sufficient to obtain clear and meaningful patterns. The high-fidelity and pre-
processed mobility traces in the gpx-format were obtained from GPS
transmitters attached to every Wavelo bike .

The first step was to cleanse the dataset provided by the city bike system.
First, we eliminated all data corrupted by GPS transmission failures (a total
of 5,946 trips). At the second stage, 40 trips with a null duration and 635
trips with a null distance were removed. The third stage, which involved
filtering out short trips, identified 421 trips with a distance shorter than
50 m. Once these were eliminated, the final sample consisted of 27,927 routes.

The number of stopovers in the cleansed sample was 54,143, with a mean
stopover time of ca 80s. After the last filtering stage, the number of
stopovers dropped to 5,791, while their mean duration rose to a little over 6
minutes; only 25%, however, were longer than 5 minutes 35 seconds (tab. 1).
The largest drop in the number of stopovers was recorded after the first step,
which involved eliminating those within a radius of 7 m from the trip’s
origin. Further decreases were less steep, but a clear relationship could
still be observed between the increase of the mean duration and the lower
number of stopovers in the sample.

stage | # stops |  |  | time [s] |  |  |  |  | sum
---|---|---|---|---|---|---|---|---|---
|  | mean | std | min | 25% | 50% | 75% | max | [h]
raw | $54\,143$ | $79.17$ | $291.63$ | $1$ | $15$ | $25$ | $65$ | $27\,350$ | $1190.77$
1st | $9639$ | $280.93$ | $559.30$ | $1$ | $85$ | $90$ | $255$ | $8290$ | $752.20$
2nd | $6277$ | $335.83$ | $647.13$ | $1$ | $85$ | $155$ | $275$ | $8290$ | $585.56$
3rd | $6219$ | $337.74$ | $649.78$ | $1$ | $85$ | $160$ | $275$ | $8290$ | $583.45$
4th | $5791$ | $361.80$ | $667.09$ | $31$ | $85$ | $170$ | $335$ | $8290$ | $582.00$

Table 1: Number of stopovers and their statistics in the subsequent filtering
stages.

After each filtering stage, we visualised the dataset for verification of the
obtained results. The unfiltered sample was dominated by punctual stops near
rental stations. The first filtering stage allowed us to identify stops that
were not related to the trip origin or destination. After the first stage, the
map still contained punctual hotspots related to stopovers at junctions and
level crossings which were successfully filtered with subsequent filtering
stages. Finally, most stopovers were identified in the touristic and leisure
places like Vistula Boulevards, Old Town and the Main Market Square.

In order to quantify results, we created a grid where each field was assigned
the corresponding number of stopovers and total stopover time (eq. 9). In the
case at hand, the area delimited by the outer geographical coordinates of the
recorded stopovers was divided into 10,000 fields of the same geographical
longitude and latitude, i.e., 0.0015∘ × 0.0036∘. Figure 5 shows values on the
grid of: number of stops (a), total stopover time (b), mean stopover time (c)
and mean stopover time in four classes (d). Based on the emerging patters, it
was evident that the mean stopover time provides a meaningful proxy to
identify urban hotspots. Other ones yielded both false-positive as well as
true-negative errors, where identified hotspots were not attractive and
attractive hotspots were not identified, respectively.

Figure 5: Values on the 100x100 grid of a) number of stopovers, b) total
stopover time, c) mean stopover time and d) mean stopover time in four
classes. Clearly the last one showing most evident patterns resembling the
actual structure of Kraków’s tourist and recreational attractiveness.

Figure 6: Four classes of urban attractiveness in Kraków based on the mean
stopover times of city bike users.

Most fields in the attractiveness identification grid, i.e., 94.74%, have a
rating of 0, but the most attractive areas of the city, such as the Old Town,
the Vistula Boulevards, the Benedictine Abbey in Tyniec, the Kolna kayaking
centre, the Polish Aviators’ Park, Bagry Lake or Nowa Huta Lake, achieved a
high attractiveness score. Table 2 shows the number of fields in each
attractiveness category and their percentage share in the total number of
fields in the attractiveness identification grid.

class | # fields | share
---|---|---
0 (lowest) | $9474$ |  $94.74$%
1 | $183$ |  $1.83$%
2 | $183$ |  $1.83$%
3 (highest) | $160$ |  $1.60$%
Table 2: Attractiveness classes of attractiveness identification grid fields.

###  3.1 Validation

Since the proposed method is explorative and aims for more complete
identification of previously unrevealed attractive urban hotspots, its’
validation is not straightforward. Nonetheless, to demonstrate its’
capabilities we validated our results against typical sources of tourist
attractiveness. Faracik . (2008) evaluated urban space in terms of its tourism
attractiveness, which was later adopted as an official and the latest tourism
policy by the Mayor of Kraków. In the comprehensive study, they relied on
their expert knowledge to select the natural, cultural, accommodation and
services factors in the assessment process and, similarly to our study,
classified city space into four classes, as shown in fig. 7.

Most of the hotspots identified by our method overlap with those mentioned in
the literature. Figures 8 to 11 zoom in the attractiveness grid in selected
areas of Kraków and discuss them. By comparing figure 7 with figures 8 to 11
one can see greater details of hotspots locations and complex, yet clear
patterns resulting from our method.

In table 3 we compare most important official tourist attractiveness (highly
ranked in Faracik . (2008)) with our method results. Our method managed to
correctly identify all the tourist attractions from the official sources of
attractiveness (compare our findings on fig 6 with official attractiveness on
fig 7), with two exceptions, both poorly accessible by bike.

First one is pilgrims centre in Łagiewniki (south), which is typically visited
by elder tourists who rarely use city bikes and is poorly accessed by bicycle
(as it is located on the hill). The second was the Wolski Forest, with the Zoo
and the Piłsudski Mound. While considered as one of the most attractive spots
in the city, for topographical reasons, this place is popular among mountain
bikers rather than Wavelo users.

On the other hand, some places classified as attractive with our method, were
clearly not touristic shopppiung malls. Shopping can be perceived attractive
by tourists and locals, several of shopping malls in Kraków are the attractive
ones. They are located near the Old Town (Galeria Krakowska) and Kazimierz
(Galeria Kazimierz). However the two examples that we use: the Krokus mall,
and shopping centre in Norymberska Street are clearly not attractive and used
by locals to supply their basic needs. Our method failed to filter them out,
yet manual post-processing with a basic background field-knowledge allowed to
effectively interpret such false-positive cases.

Figure 7: Four classes of tourist attractiveness in the official policy of
Mayor of Kraków Faracik . (2008).

Place | rank: |  |  |
---|---|---|---|---
| official | our |  accuracy∗ | comment
|  Central (fig. 8)
the Main Market Square | 3 | 3 | TP |
Wawel Castle, and the Vistula Boulevards | 3 | 3 | TP |
Kazimierz quarter | 3 | 3 | TP |
the city beach in the Courland Boulevard | - | 3 | TP | recently opened
Błonia and the Rudawa Valley | 2 | 3 | TP |
the Jordan’s Park | 2 | 3 | TP |
|  South-west (fig. 9, 10)
Cricoteka | 1 | 3 | TP |
Schindler’s Factory and the MOCAK | 2 | 3 | TP |
the Krakus Mound | 2 | 3 | TP |
Zoo and Piłsudski Mound | 2 | 0 | TN | poorly accessible by bike
Bagry Lake | - | 3 | TP+ | primarily for locals
the Vistula Boulevards in Stare Dębniki, | 2 | 3 | TP |
Pilgrim center Łagiewniki | 3 | 0 | TN | poorly accessible by bike
the Vistula Boulevards in Ludwinów | 2 | 3 | TP |
Zakrzówek | 1 | 3 | TP+ | primarily for locals
the kayaking trail in Tyniec | 2 | 3 | TP |
the Benedictine Abbey in Tyniec | 3 | 3 | TP | distant yet bike-accessible
Shopping mall at Norymberska street | - | 3 | FP | hardly attractive
|  East (fig. 11)
Centralny Square | 2 | 3 | TP |
the Polish Aviators’ Park | 1 | 3 | TP+ |
Nowa Huta Lake | 2 | 3 | TP |
Nowa Huta Meadows | - | 3 | TP+ | picnic spot for bike trips
Krokus shopping mall | - | 3 | FP | hardly not attractive

Table 3: Most attractive tourist places according to official listing Faracik
. (2008) compared with our classifications. Italics denote places either not
identified or wrongly classified as attractive our method. ∗TP - true
positive, TP+ - added-value to the official sources, TN - true negative, FP -
false positive, compare with fig. 2

Figure 8: Selected highest-rated spots in Central Kraków: 1. Błonia with the
Rudawa Valley and the Jordan Park, 2. the Old Town with the Main Market
Square, 3. Wawel Hill with the Vistula Boulevards, 4. Kazimierz with the
Vistula Boulevards, 5. the city beach area, highly popular among locals is the
new riverside and not listed in official guides. The old town (2) is now more
detailed, making the most attractive spots visible.

Figure 9: Selected highest-rated spots in South Kraków: 1. the kayak rental
station, 2. Zakrzówek lake, 3. the Vistula Boulevards in Stare Dębniki,
including the Dębnicki Park, 4. the Vistula Boulevards in Ludwinów, 5. the
area of Cricoteka and the Podgórski Market Square, 6. the area of Schindler’s
Factory and the MOCAK, 7. Bagry Lake. Pilgrims centre Łagiewniki (to the
south), highly ranked in official guides, not identified in our method due to
low bike accessibility. The shopping mall at Norymberska street (down from
hotspot 2) was identified as attractive, which is clearly a false-positive
case that has to be filtered manually.

Figure 10: Selected highest-rated spots in West Kraków: 1. the Benedictine
Abbey in Tyniec, 2. the kayaking trail in Tyniec. Both attractive yet distant,
which leaves a trace of short breaks along the highly popular bike path
stretching by the river between old town and Tyniec.

Figure 11: Selected highest-rated spaces in East Kraków: 1. the Polish
Aviators’ Park, 2. Nowa Huta Meadows, 3. Centralny Square with Aleja Róż, 4.
Nowa Huta Lake. The old Nowa Huta area marked as one equally attractive spot
in official guidelines (fig. 7 ) is now depicted with more detail, making it
evident that attractive places concentrate around Plac Centralny and the Lake.

##  4 Conclusions and discussion

We proposed a generic method applicable for any spatiotemporal data from city-
bikes, which, since city bikes are nowadays present in most of metropolises
worldwide, makes it applicable to explore spatial patterns of stopovers in
broad range of cities. The light and replicable method, relying on standard
spatiotemporal trip tracks allows to identify hotspots - places with frequent
and long stopovers. Which, as we demonstrate are a good proxy of the space
attractiveness. By assuming that most of stopovers identified with the method
are related to tourism and/or leisure, we identified complex and meaningful
spatial patterns, clearly pointing towards city’s most attractive urban
hotspots. The results shown that the most frequent stopover locations of
Wavelo bike users were, in fact, concentrated in the proximity of the most
attractive cultural and natural assets. With the proposed automated manual
filtering, not relying on local field knowledge, one can reveal number of
valuable findings, both in terms of identifying unknown places as well as
quantifying well-known ones. While the local knowledge may refine the results
and make it reliable indicator of urban attractiveness.

Results of our case-study proved that the method effectively identified most
of established Kraków tourist attractions. The recent dynamism in behaviour of
tourists, shifting from well-known paths to exploring newly emerging sights,
was evident from the emerging patterns. Our method managed to cover it and
quantify those changes.

Despite relying on personal and potentially sensitive data, we find the method
transparent and ethical. Even though the precise spatial path is recorded, it
always starts and ends at the city bike station, rather than at personally
sensitive home or workplace. While the users’ ID is not stored in the dataset,
his sociodemographic attributes may be disclosed which would enable more
detailed analysis differentiating locals from visitors, young from older
users, etc. The proposed method has potential in real-time monitoring and can
be potentially automated to report the attractiveness and its relative changes
over time. Making it an efficient tool for policy makers to monitor shifts in
tourist behaviour.

Importantly, in the post-COVID context our method offers an efficient and
inexpensive monitoring framework allowing to understand how the pandemic
changes impacted the consumption of urban space by tourists, locals and
visitors. Allowing to quickly identify most visited, possibly crowded, places
where intervention may be needed to stop virus-spreading.

Obviously, our method has some limitations. It relies on sequence of filters,
where thresholds need to be manually parameterized (e.g., cut-off distance
from traffic lights, or from station). While it effectively filters commuting
trips, as long as user type remains unknown, the leisure remains
indistinguishable from tourism, and local residents from visitors. This shall
be further enhanced with labelled data. While the city bike systems are often
limited in space and rental stations are not evenly distributed everywhere in
the city. This was not the case for Kraków, yet in cities where coverage is
not complete, this may obscure the overall image and fail to yield a complete
spatial pattern.

Finally, to verify the results before drawing conclusions a basic field
knowledge is needed. In the case of Kraków, some definitely non-attractive
shopping malls were misclassified with our method. Yet in any case, virtual or
physical site visit may always verify its actual attractiveness. For instance,
for us, the locals of Kraków, the data revealed the place that we were not
aware of (hidden garden at Karmelicka street).

We believe that with this method we fill some of the gaps in research on the
urban space attractiveness for residents and tourists.

##### Acknowledgement(s)

We thank the City of Kraków, the operator of Wavelo city bike for the data for
analysis.

## References

  * Biernacka  Kronenberg (2018) biernacka2018classificationBiernacka, M. Kronenberg, J.  2018\.  Classification of institutional barriers affecting the availability, accessibility and attractiveness of urban green spaces Classification of institutional barriers affecting the availability, accessibility and attractiveness of urban green spaces. Urban Forestry & Urban Greening3622–33.
  * Biernacka . (2020) biernacka2020aBiernacka, M., Kronenberg, J. Łaszkiewicz, E.  2020\.  An integrated system of monitoring the availability, accessibility and attractiveness of urban parks and green squares An integrated system of monitoring the availability, accessibility and attractiveness of urban parks and green squares. Applied Geography116102152.
  * Boivin  Tanguay (2019) boivin2019aBoivin, M. Tanguay, G.  2019\.  Analysis of the determinants of urban tourism attractiveness: The case of Québec City and Bordeaux Analysis of the determinants of urban tourism attractiveness: The case of québec city and bordeaux. Journal of destination marketing & management1167–79.
  * Brinkmann (2020) brinkmann2020aBrinkmann, J.  2020\.  Active Balancing of Bike Sharing Systems. Active balancing of bike sharing systems.
  * Buning  Lulla (2020) buning2020aBuning, R. Lulla, V.  2020\.  Visitor bikeshare usage: tracking visitor spatiotemporal behavior using big data Visitor bikeshare usage: tracking visitor spatiotemporal behavior using big data. Journal of Sustainable Tourism.  10.1080/09669582.2020.1825456
  * Caldeira  Kastenholz (2020) caldeira2020aCaldeira, A. Kastenholz, E.  2020\.  Spatiotemporal tourist behaviour in urban destinations: a framework of analysis Spatiotemporal tourist behaviour in urban destinations: a framework of analysis. Tourism Geographies22122–50.  10.1080/14616688.2019.1611909
  * Cantelmo . (2019) cantelmo2019aCantelmo, G., Kucharski, R. Antoniou, C.  2019\.  A low dimensional model for bike sharing demand forecasting A low dimensional model for bike sharing demand forecasting. MT-ITS 2019 - 6th International Conference on Models and Technologies for Intelligent Transportation Systems. Mt-its 2019 - 6th international conference on models and technologies for intelligent transportation systems.  10.1109/MTITS.2019.8883283
  * Caulfield . (2017) caulfield2017aCaulfield, B., O’Mahony, M., Brazil, W. Weldon, P.  2017\.  Examining usage patterns of a bike-sharing scheme in a medium sized city Examining usage patterns of a bike-sharing scheme in a medium sized city. Transportation Research Part A: Policy and Practice100152–161.  10.1016/j.tra.2017.04.023
  * Cohen . (2014) cohen2014consumerCohen, SA., Prayag, G. Moital, M.  2014\.  Consumer behaviour in tourism: Concepts, influences and opportunities Consumer behaviour in tourism: Concepts, influences and opportunities. Current issues in Tourism1710872–909.
  * Cracolici  Nijkamp (2009) cracolici2009attractivenessCracolici, MF. Nijkamp, P.  2009\.  The attractiveness and competitiveness of tourist destinations: A study of Southern Italian regions The attractiveness and competitiveness of tourist destinations: A study of southern italian regions. Tourism management303336–344.
  * Dunne . (2011) dunne2011aDunne, G., Flanagan, S. Buckley, J.  2011\.  Towards a decision making model for city break travel Towards a decision making model for city break travel. International Journal of Culture, Tourism and Hospitality Research52158–172.  10.1108/17506181111139573
  * Encalada . (2017) encalada2017aEncalada, L., Boavida-Portugal, I., Ferreira, C. Rocha, J.  2017\.  Identifying tourist places of interest based on digital imprints: Towards a sustainable smart City Identifying tourist places of interest based on digital imprints: Towards a sustainable smart city. Sustainability (Switzerland912.  https://doi.org/10.3390/su9122317 10.3390/su9122317
  * Eren  Uz (2020) eren2020aEren, E. Uz, V.  2020\.  A review on bike-sharing: The factors affecting bike-sharing demand A review on bike-sharing: The factors affecting bike-sharing demand. Sustainable Cities and Society Sustainable cities and society ( 54,  101882).  Elsevier Ltd.  10.1016/j.scs.2019.101882
  * Espelt  Benito (2006) espelt2006aEspelt, N. Benito, J.  2006\.  Visitors’ Behavior in Heritage Cities: The Case of Girona Visitors’ behavior in heritage cities: The case of girona. Journal of Travel Research444442–448.  10.1177/0047287505282956
  * Estiri . (2020) estiri2020aEstiri, M., Heidary Dahooie, J., Hosseini, F. Khajeheian, D.  2020\.  Proposing a new model for shopping centre attractiveness assessment by a Combination of Structural Equation Modelling (SEM Proposing a new model for shopping centre attractiveness assessment by a combination of structural equation modelling (sem. and Additive Ratio ASsessment (ARAS). Current Issues in Tourism and additive ratio assessment (aras). current issues in tourism ( 1–19).
  * Faracik . (2008) faracik2008aFaracik, R., Kurek, W., Mika, M., Pawlusiński, R., Pitrus, E., Ptaszycka-Jackowska, D.Zawilińska, B.  2008\.  Waloryzacja przestrzeni miejskiej Krakowa dla potrzeb turystyki - raport końcowy Waloryzacja przestrzeni miejskiej krakowa dla potrzeb turystyki - raport końcowy.
  * Ferrante . (2018) ferrante2018generalFerrante, M., De Cantis, S. Shoval, N.  2018\.  A general framework for collecting and analysing the tracking data of cruise passengers at the destination A general framework for collecting and analysing the tracking data of cruise passengers at the destination. Current Issues in Tourism21121426–1451.
  * Fieger . (2019) fieger2019pullFieger, P., Prayag, G. Bruwer, J.  2019\.  ‘Pull’motivation: an activity-based typology of international visitors to New Zealand ‘pull’motivation: an activity-based typology of international visitors to new zealand. Current Issues in Tourism222173–196.
  * Frade  Ribeiro (2014) frade2014aFrade, I. Ribeiro, A.  2014\.  Bicycle Sharing Systems Demand Bicycle sharing systems demand. Procedia - Social and Behavioral Sciences111February 2014518–527.  10.1016/j.sbspro.2014.01.085
  * Froehlich . (2009) froehlich2009aFroehlich, J., Neumann, J. Oliver, N.  2009\.  Sensing and predicting the pulse of the city through shared bicycling Sensing and predicting the pulse of the city through shared bicycling. IJCAI International Joint Conference on Artificial Intelligence Ijcai international joint conference on artificial intelligence ( 3,  1420–1426).  https://doi.org/10.1.1.150.4370
  * Garau  Pavan (2018) su10030575Garau, C. Pavan, VM.  2018\.  Evaluating Urban Quality: Indicators and Assessment Tools for Smart Sustainable Cities Evaluating urban quality: Indicators and assessment tools for smart sustainable cities. Sustainability103.  https://www.mdpi.com/2071-1050/10/3/575 10.3390/su10030575
  * Gehl (2011) gehl2011aGehl, J.  2011\.  Life between buildings Using public spaces Life between buildings using public spaces.  Island Press.
  * Giglio . (2019) giglio2019aGiglio, S., Bertacchini, F., Bilotta, E. Pantano, P.  2019\.  Using social media to identify tourism attractiveness in six Italian cities Using social media to identify tourism attractiveness in six italian cities. Tourism management72306–312.
  * Girardin . (2009) girardin2009aGirardin, F., Vaccari, A., Gerber, A., Biderman, A. Ratti, C.  2009\.  Quantifying urban attractiveness from the distribution and density of digital footprints Quantifying urban attractiveness from the distribution and density of digital footprints. International Journal4175–200.
  * Gonzalez . (2008) gonzalez2008understandingGonzalez, MC., Hidalgo, CA. Barabasi, AL.  2008\.  Understanding individual human mobility patterns Understanding individual human mobility patterns. nature4537196779–782.
  * González . (2019) gonz2019aGonzález, R., Román, C. Ortúzar, JD.  2019\.  Preferences for sustainable mobility in natural areas: The case of Teide National Park Preferences for sustainable mobility in natural areas: The case of teide national park. Journal of Transport Geography7642–51.  10.1016/j.jtrangeo.2019.03.002
  * Hasnat  Hasan (2018) hasnat2018aHasnat, M. Hasan, S.  2018\.  Identifying tourists and analyzing spatial patterns of their destinations from location-based social media data Identifying tourists and analyzing spatial patterns of their destinations from location-based social media data. Transportation Research Part C: Emerging Technologies9638–54.  10.1016/j.trc.2018.09.006
  * Huang . (2020) huang2020touristsHuang, X., Li, M., Zhang, J., Zhang, L., Zhang, H. Yan, S.  2020\.  Tourists’ spatial-temporal behavior patterns in theme parks: A case study of Ocean Park Hong Kong Tourists’ spatial-temporal behavior patterns in theme parks: A case study of ocean park hong kong. Journal of Destination Marketing & Management15100411.
  * Imani . (2014) imani2014aImani, A., Eluru, N., El-Geneidy, A., Rabbat, M. Haq, U.  2014\.  How does land-use and urban form impact bicycle flows: Evidence from the bicycle-1 sharing system (BIXI) in Montreal How does land-use and urban form impact bicycle flows: Evidence from the bicycle-1 sharing system (bixi) in montreal. Proceedings of the 93rd Annual Meeting of the Transportation Research Board. Proceedings of the 93rd annual meeting of the transportation research board.  Washington DC.  https://doi.org/10.1002/jgrd.50214 10.1002/jgrd.50214
  * Jacobsen . (2019) jacobsen2019hotspotJacobsen, JKS., Iversen, NM. Hem, LE.  2019\.  Hotspot crowding and over-tourism: Antecedents of destination attractiveness Hotspot crowding and over-tourism: Antecedents of destination attractiveness. Annals of Tourism Research7653–66.
  * Johnson  Glover (2013) amanda2013Johnson, AJ. Glover, TD.  2013\.  Understanding Urban Public Space in a Leisure Context Understanding urban public space in a leisure context. Leisure Sciences352190-197.  https://doi.org/10.1080/01490400.2013.761922 10.1080/01490400.2013.761922
  * Keul  Kühberger (1997) keul1997aKeul, A. Kühberger, A.  1997\.  Tracking the Salzburg tourist Tracking the salzburg tourist. Annals of Tourism Research2441008–1012.  10.1016/s0160-7383(97)00038-8
  * Kianicka . (2006) kianicka2006localsKianicka, S., Buchecker, M., Hunziker, M. Müller-Böker, U.  2006\.  Locals’ and tourists’ sense of place Locals’ and tourists’ sense of place. Mountain Research and Development26155–63.
  * Kourtit . (2020) kourtit2020aKourtit, K., Nijkamp, P. Wahlström, M.  2020\.  How to make cities the home of people–a ‘soul and body’analysis of urban attractiveness How to make cities the home of people–a ‘soul and body’analysis of urban attractiveness. Land Use Policy104734.
  * Kutela  Teng (2019) kutela2019aKutela, B. Teng, H.  2019\.  The influence of campus characteristics, temporal factors, and weather events on campuses-related daily bike-share trips The influence of campus characteristics, temporal factors, and weather events on campuses-related daily bike-share trips. Journal of Transport Geography78160–169.  10.1016/j.jtrangeo.2019.06.002
  * Lee  King (2019) lee2019aLee, CF. King, B.  2019\.  Determinants of attractiveness for a seniors-friendly destination: a hierarchical approach Determinants of attractiveness for a seniors-friendly destination: a hierarchical approach. Current Issues in Tourism22171–90,.  10.1080/13683500.2016.1250725
  * D. Li . (2018) li2018aLi, D., Zhou, X. Wang, M.  2018\.  Analyzing and visualizing the spatial interactions between tourists and locals: A Flickr study in ten US cities Analyzing and visualizing the spatial interactions between tourists and locals: A flickr study in ten us cities. Cities74249–258.  10.1016/j.cities.2017.12.012
  * J. Li . (2018) li_bigdataLi, J., Xu, L., Tang, L., Wang, S. Li, L.  2018\.  Big data in tourism research: A literature review Big data in tourism research: A literature review. Tourism Management68301–323.  10.1016/j.tourman.2018.03.009
  * Y. Li . (2017) li2017aLi, Y., Hu, C., Huang, C. Duan, L.  2017\.  The concept of smart tourism in the context of tourism information services The concept of smart tourism in the context of tourism information services. Tourism Management58293–300.  10.1016/j.tourman.2016.03.014
  * Lin . (2020) lin2020aLin, P., Weng, J., Hu, S., Alivanistos, D., Li, X. Yin, B.  2020\.  Revealing Spatio-Temporal Patterns and Influencing Factors of Dockless Bike Sharing Demand Revealing spatio-temporal patterns and influencing factors of dockless bike sharing demand. IEEE Access866139–66149.  10.1109/access.2020.2985329
  * Martí . (2021) marti2021takingMartí, P., García-Mayor, C. Serrano-Estrada, L.  2021\.  Taking the urban tourist activity pulse through digital footprints Taking the urban tourist activity pulse through digital footprints. Current Issues in Tourism242157–176.
  * McKercher  Lau (2008) mckercher2008aMcKercher, B. Lau, G.  2008\.  Movement patterns of tourists within a destination Movement patterns of tourists within a destination. Tourism Geographies103355–374.  10.1080/14616680802236352
  * Md Khairi . (2019) md2019aMd Khairi, N., Ismail, H. Syed Jaafar, S.  2019\.  Tourist behaviour through consumption in Melaka World Heritage Site Tourist behaviour through consumption in melaka world heritage site. Current Issues in Tourism225582–600.  10.1080/13683500.2018.1491534
  * Miah . (2017) miah2017aMiah, S., Vu, H., Gammack, J. McGrath, M.  2017\.  A Big Data Analytics Method for Tourist Behaviour Analysis A big data analytics method for tourist behaviour analysis. Information and Management546771–785.  10.1016/j.im.2016.11.011
  * Mitra (2020) mitra2020aMitra, S.  2020\.  A different perspective to measure tourism attractiveness A different perspective to measure tourism attractiveness. Current Issues in Tourism23141718–1722.
  * Naumov  Banet (2020) naumov2020aNaumov, V. Banet, K.  2020\.  Estimating parameters of demand for trips by public bicycle system using GPS data Estimating parameters of demand for trips by public bicycle system using gps data. Advances in Intelligent Systems and Computing Advances in intelligent systems and computing ( 1091).  https://doi.org/10.1007/978-3-030-35543-2_17 AISC.  10.1007/978-3-030-35543-2_17
  * Orellana . (2012) ORELLANA2012672Orellana, D., Bregt, AK., Ligtenberg, A. Wachowicz, M.  2012\.  Exploring visitor movement patterns in natural recreational areas Exploring visitor movement patterns in natural recreational areas. Tourism Management333672-682.  https://www.sciencedirect.com/science/article/pii/S026151771100152X https://doi.org/10.1016/j.tourman.2011.07.010
  * Salas-Olmedo . (2018) SALASOLMEDO201813Salas-Olmedo, MH., Moya-Gómez, B., García-Palomares, JC. Gutiérrez, J.  2018\.  Tourists’ digital footprint in cities: Comparing Big Data sources Tourists’ digital footprint in cities: Comparing big data sources. Tourism Management6613–25.
  * Salon . (2019) salon2019aSalon, D., Conway, M., Wang, K. Roth, N.  2019\.  Heterogeneity in the relationship between biking and the built environment Heterogeneity in the relationship between biking and the built environment. Journal of Transport and Land Use12199–126.  10.5198/jtlu.2019.1350
  * Smallwood . (2012) smallwood2012analysisSmallwood, CB., Beckley, LE. Moore, SA.  2012\.  An analysis of visitor movement patterns using travel networks in a large marine park, north-western Australia An analysis of visitor movement patterns using travel networks in a large marine park, north-western australia. Tourism Management333517–528.
  * Stangl . (2020) stangl2020segmentingStangl, B., Prayag, G. Polster, L.  2020\.  Segmenting visitors’ motivation, price perceptions, willingness to pay and price sensitivity in a collaborative destination marketing effort Segmenting visitors’ motivation, price perceptions, willingness to pay and price sensitivity in a collaborative destination marketing effort. Current Issues in Tourism23212666–2682.
  * Thees . (2020) THEES2020171Thees, H., Zacher, D. Eckert, C.  2020\.  Work, life and leisure in an urban ecosystem - co-creating Munich as an Entrepreneurial Destination Work, life and leisure in an urban ecosystem - co-creating munich as an entrepreneurial destination. Journal of Hospitality and Tourism Management44171-183.  https://www.sciencedirect.com/science/article/pii/S1447677020301637 https://doi.org/10.1016/j.jhtm.2020.06.010
  * Thimm  Seepold (2016) thimm2016aThimm, T. Seepold, R.  2016\.  Past, present and future of tourist tracking Past, present and future of tourist tracking. Journal of Tourism Futures2143–55.  10.1108/JTF-10-2015-0045
  * Tracz  Semczuk (2018) tracz2018aTracz, M. Semczuk, M.  2018\.  Wpływ turystyki na zmianę funkcji przestrzeni miejskiej na przykładzie Krakowa Wpływ turystyki na zmianę funkcji przestrzeni miejskiej na przykładzie krakowa. Biuletyn Komitetu Przestrzennego Zagospodarowania Kraju PAN272272–284.
  * Tran . (2015) tran2015aTran, T., Ovtracht, N. D’Arcier, B.  2015\.  Modeling bike sharing system using built environment factors Modeling bike sharing system using built environment factors. Procedia CIRP30293–298.  10.1016/j.procir.2015.02.156
  * Van der Ark  Richards (2006) van2006attractivenessVan der Ark, LA. Richards, G.  2006\.  Attractiveness of cultural activities in European cities: A latent class approach Attractiveness of cultural activities in european cities: A latent class approach. Tourism Management2761408–1413.
  * Vogel . (2011) vogel2011aVogel, P., Greiser, T. Mattfeld, D.  2011\.  Understanding bike-sharing systems using Data Mining: Exploring activity patterns Understanding bike-sharing systems using data mining: Exploring activity patterns. Procedia - Social and Behavioral Sciences20514–523.  10.1016/j.sbspro.2011.08.058
  * J. Wang  Lindsey (2019) wang_socioWang, J. Lindsey, G.  2019\.  Neighborhood socio-demographic characteristics and bike share member patterns of use Neighborhood socio-demographic characteristics and bike share member patterns of use. Journal of Transport Geography79102475.  10.1016/j.jtrangeo.2019.102475
  * K. Wang  Akar (2019) wang_genderWang, K. Akar, G.  2019\.  Gender gap generators for bike share ridership: Evidence from Citi Bike system in New York City Gender gap generators for bike share ridership: Evidence from citi bike system in new york city. Journal of Transport Geography761–9.  10.1016/j.jtrangeo.2019.02.003
  * Zakrisson  Zillinger (2012) zakrisson2012aZakrisson, I. Zillinger, M.  2012\.  Emotions in motion: Tourist experiences in time and space Emotions in motion: Tourist experiences in time and space. Current Issues in Tourism156505–523.  10.1080/13683500.2011.615391
  * Zheng . (2017) zheng2017understandingZheng, W., Huang, X. Li, Y.  2017\.  Understanding the tourist mobility using GPS: Where is the next place? Understanding the tourist mobility using gps: Where is the next place? Tourism Management59267–280.
  * Zhu (2020) zhu2020aZhu, H.  2020\.  Zone division and extraction of historic area based on big data Zone division and extraction of historic area based on big data. Current Issues in Tourism001–22.  10.1080/13683500.2020.1814223
  * Önder . (2016) oender2016aÖnder, I., Koerbitz, W. Hubmann-Haidvogel, A.  2016\.  Tracing Tourists by Their Digital Footprints Tracing tourists by their digital footprints. Journal of Travel Research555566–573.  10.1177/0047287514563985