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BIG BICYCLE DATA PROCESSING:
FROM PERSONAL DATA TO URBAN APPLICATIONS
C. J. Pettit, Lieske S. N., Leao S. Z.
City Futures Research Centre, The University of New South Wales:
c.pettit@unsw.edu.au, s.lieske@unsw.edu.au, s.zarpelonleao@unsw.edu.au.
Commission II: Theme Session 17 Smart Cities
KEY WORDS: Big data, little data, data processing, data visualisation
ABSTRACT:
Understanding the flows of people moving through the built environment is a vital source of information for the planners and policy
makers who shape our cities. Smart phone applications enable people to trace themselves through the city and these data can
potentially be then aggregated and visualised to show hot spots and trajectories of macro urban movement. In this paper our aim is to
develop procedures for cleaning, aggregating and visualising human movement data and translating this into policy relevant
information. In conducting this research we explore using bicycle data collected from a smart phone application known as RiderLog.
We focus on the RiderLog application initially in the context of Sydney, Australia and discuss the procedures and challenges in
processing and cleaning this data before any analysis can be made. We then present some preliminary map results using the CartoDB
online mapping platform where data are aggregated and visualised to show hot spots and trajectories of macro urban movement. We
conclude the paper by highlighting some of the key challenges in working with such data and outline some next steps in processing
the data and conducting higher volume and more extensive analysis.
1. INTRODUCTION
1.1 Big Data and Smart Cities
In an age of big data, little data and smart cities (Batty, 2015)
there is an imperative to provide more accessible evidence to
planners and policy makers to better shape our cities. Our
research aim is to develop procedures for cleaning, aggregating
and visualising human movement data and translating this into
policy relevant information. In this paper we focus on
movement data of bicyclists who are using the RiderLog
application to capture data about their individual bicycle
journeys across the City of Sydney, Australia. This little data
(individual record bicycle journeys) can then be aggregated
across a city, which then becomes big data. With big data there
are challenges in processing and cleaning which we outline in
this paper. Next, we present some preliminary findings
visualised using the Carto DB online mapping platform. Finally,
we conclude the paper by highlighting some of the key
challenges in working with such data and outline next steps in
the research.
1.2 From personal to urban applications: the need for Big
Data pre- and post-processing
The growing volumes of data available from sensors, social
media, and other digital interconnected systems are seen as
‘remarkable opportunities for researchers and policy analysts’
(Shneiderman and Plaisant 2015, p. 1). Indeed, the idea of
‘smart cities’ heavily relies on the possibility of integrating and
understanding big (geospatial) data and turning it into
knowledge and intelligence which is used to shape better and
more effective urban environments (Batty, 2013; Li et al.,
2015).
In this study we are focused on data produced by individuals
motivated by personal goals. This type of data is becoming
more prominent with the ubiquity of smart phones with multiple
sensors, and the increasing use of mobile phone applications for
daily routine activities in society (Lane et al. 2010). When
combined in a crowd scale these data may have the capacity to
reveal macro behavioural patterns which are of interest for city
planners and policy makers alike. The transposition of these big
datasets into urban research or urban planning, however, is not
a simple exercise. The different purposes between data
production and application, together with issues associated to
privacy, human inconsistencies, and device inaccuracies, pose
challenges to its practical use.
Contemporary datasets, according to Laney (2001), are
characterised by their volume (data size is large), their velocity
(data is created rapidly and continuously), and variety (data
comprises multiple types and is captured from different
sources), also known as the 3Vs of big data. IBM estimates that
2.5 quintillion bytes of data are generated every day, and that 90
percent of today’s data has been created in the last two years
alone (Zhang et al. 2012). However, more data does not
necessarily mean more useful information, since big data is also
highly heterogeneous, complex, unstructured, incomplete, and
noisy (Ma et al. 2014), and most current information systems or
methods are unable to handle and process big data (Tsai et al.
2015).
Knowledge discovery in databases has always required a
number of operations and processes to turn data from a raw
state into a more appropriate format for analysis and
visualisation (Fayyad et al. 1996), even when they had smaller
size and complexity. Big data brings some additional
challenges. Pettit et al. (2012) introduced a number of
visualisation techniques for representing urban space and place;
however, these are not specifically focused on the application of
big data.
Tsai et al. (2015) presented a comprehensive review on efforts
attempting to produce new methods that are able to handle big
data during the input, analysis and output stages of knowledge
discovery. They identified that most of the recent literature is
focused on innovative methods for data mining and analysis,
with much less attention to the pre- and post-analysis
processing methods.
Data detection, selection, cleaning, filtering, correction,
completion, and transformation are some of the pre-processing
methods applied to prepare databases with the objective to
obtain more accurate, complete and compatible information sets
(Fayyad et al. 1996). In the context of big data, new approaches
are combining those traditional methods with strategies to
reduce its size and complexity. These can include the extraction
of relevant records, event types, or key events; folding data to
make cyclic patterns such as days or weeks clear; and pattern
simplification strategies to simplify complex sequence of events
(Shneiderman and Plaisant 2015). The question that remains is
to what extent reduction can be made before losing important
meanings. Pre-processing of big data can be so demanding that
Kandel et al. (2011) refers to it as ‘data wrangling’.
Similarly, big data also raises new challenges for the post-
processing analysis stage of knowledge discovery in databases,
which in most cases are associated to the visualisation of
patterns and processes. ProfitBricks (2015) presented a brief
review of 39 data visualisation tools for big data currently
available, including open-source, free, and commercial
platforms. Some examples with geographic mapping
capabilities include GoogleMaps1, CartoDB2, ProcessingGIS3,
and Leaflet4. With varied levels of sophistication, these
developments demonstrate that this is a field in expansion.
Interestingly, Kendal et al. (2011) argues that ‘analysts might
more effectively wrangle data through new interactive systems
that integrate data verification, transformation, and
visualization’ (p. 1); therefore, bringing pre- and post-
processing close together. This is the underlying approach for
the research undertaken in our focus on bicycling data.
1.3 Big Cycling Data from participatory sensing via mobile
phones
There is a growing trend to use mobile devices and applications
to collect data relating to fitness activities (Clarke and Steele,
2014). Some mobile phone applications currently available for
bicycling include MapMyRide5, iBike6, Cycle Meter7, Strava8,
and RiderLog9. They vary in their format, purpose and
functionalities; some save routes and monitor progress of
ordinary riders, some are designed for professional riders, while
others are more focused on bicycling for transport. What they
all have in common is that they produce large amounts of
complex data that document riding journeys. Individually, each
application comprises data records with locations, time and
intervals, and other attributes, which are organised into the
specific application’s format and purpose. Daily, new records
are captured from registered users, new users join the system,
and some previous users may disconnect from the system.
1 https://developers.google.com/maps/
2 https://cartodb.com/
3 http://processingjs.org/
4 http://leafletjs.com/
5 http://www.mapmyride.com/
6 https://itunes.apple.com/au/app/ibike/id369550718?mt=8
7http://itunes.apple.com/us/app/cyclemeter-gps-
bikecomputer/id330595774?mt=8
8 www.strava.com
9 https://www.bicyclenetwork.com.au/general/programs/1006/
At the same time, there is an increasing interest from city
planners and policy makers in evidenced based research in
active transportation. This is due to contemporary issues
associated to health (chronical disease associated with physical
inactivity) (Pratt et al. 2014), and the environment (transition to
less carbon intensive cities) (Haines and Wilkinson 2014).
Recent research has been focused on the development and
evaluation of mobile applications associated to bicycling, such
as BikeNet (Eisenman et al. 2009), Biketastic (Reddy et al.
2010), and SocialCycle (Navarro et al. 2013). However, most of
this research describes the mobile applications within the
context of individual use, making only brief mention to its
potential implications for aggregation both spatially and
temporally to assist with city planning and policy making across
urban geographies. In fact we could not find any reported study
directly concerned with the transposition of data produced by
individuals with a personal goal, into a database useful for the
wider purpose of urban planning analysis.
Many factors can cause noise, inconsistencies, errors,
inaccuracies, and incompleteness in the personal tracking data
collected by people using their mobile phones via a specific
application for bicycling. Inaccuracies can come from weak
signal (i.e. in urban canyons in the CBD or in underground
areas); incomplete data can occur if the signal is completely
lost, if the batteries of the phone go flat, or if an incoming call
interrupts the app (depending on the app). Incompleteness is
also related to the fact that some people do not record all of
their rides, or riders use different mobile applications, or do not
change options when undertaking different types of journeys.
The rider may have multiple purposes in a trip, simplifying its
answer to the system with a single purpose. Some riders may
also do their cycling as part of a multi-modal journey, placing
the bike, for example, temporarily inside a train. All these
sources or noise in the data are not a concern for individual
users of the mobile application for the purpose of monitoring
their fitness progress. However, these varied sources of
inconsistencies, accumulated to millions or billions of records,
can have a great adverse impact when this data is aimed to be
used for city planning or policy making.
2. RESEARCH DESIGN AND METHODS
Bicycle Network have developed the RiderLog application10
which is a free smart phone application and captures the
location of a bicyclist every forty seconds. For this research
project we have bicycling route data covering all of Australia
from 2010 to 2014. This includes 148,769 bicycle journeys
undertaken by 9,727 cyclists. In this study we focus on 26,242
routes completed by 1,923 unique cyclists from the year 2010 to
year 2014 in New South Wales (NSW). In this paper we focus
specifically on its application for Sydney, the capital city for the
State of New South Wales.
Data processing steps and flow are summarized in Figure 1.
Original data are a 421 MB text file. In order to structure and
clean the data as well as begin the process of fixing errors we
brought the data into Microsoft Excel (text Import Wizard,
limited with character, “|”). We then separated the data into
smaller files based on Australian state or territory: New South
Wales, Queensland, Northern Territory, South Australia,
Tasmania, Victoria and Western Australia. These files were
10 https://www.bicyclenetwork.com.au/general/programs/1006/
saved individually as, “rider_STATE.xlsx”. The New South
Wales (NSW) data rider_NSW.xlsx file at this stage was 58.1
MB.
Figure 1. Data Processing Steps and Flow
Extracting geographic data from these tables was a challenge
due to data size and formatting. In the Excel files geographic
data are contained in a text formatted column, ‘Route’. For
NSW, individual route records contain between 62 to 32,753
characters with latitude longitude pairs separated by commas
and irregularly interspersed with miscellaneous words and other
characters. Cleaning the data required removing words and
characters using Excel’s find and replace capabilities. For NSW
68,153 unneeded strings of varying length were removed from
the Route column.
Separating the often long strings of latitude and longitude
information to Lat/Long pairs and saving them within
individual data columns were accomplished with a series of two
Excel functions, LEFT and MID. These functions enable
extraction of a subset of a string from the left-most character or
from the middle of a string using an indicated position,
respectively. LEFT and MID were used in two separate
formulas where the LEFT equation extracts a Lat/Long pair
from the long string and the MID equation copies the original
string less the Lat/Long pair in the first formula. Together, these
formulas allow all Lat/Long pairs to be extracted to single
columns from the original string in a recursive fashion. The
32,753 characters in the longest NSW cycling routes result in
915 Lat/Long pairs. If developed as a single data table these 915
pairs multiplied by the 26,243 data records along with the 27
columns already in the spreadsheet would result in a
spreadsheet containing over 1,856 columns and 48,707,000
cells. This large data volume required what would be an easy
series of iterations within a data table for a small dataset to
proceed in a stepwise fashion. The first step in processing this
large volume of data was to determine the length of the
geographic data (route) strings in each data record then sort
from smallest to largest. For the first 125 Lat/Long pairs we
were only able to process 25 pairs at a time. For each grouping
of 25 Lat/Long pairs we used the LEFT and MID formulas
above to extract the geographic data then used a VBA script to
replace formulas with data values in the worksheet. We then
deleted ‘no data’ values (where the long string had been full
parsed). Next, the intermediate data generated by the MID
equations were deleted which substantially reduced the file size.
In the case of the second iteration (the first 50 Lat/Long pairs)
this stepwise process of replacing formulas with values and
deleting intermediate data reduced the data set from 186 MB to
85.1 MB. After having completed five of these iterations, for
the first 125 columns, the vast majority of the data (22,000 of
26,243 routes) including nearly all route records of 4,000
characters or less had been processed (Figure 2). The remaining
routes, although the strings were longer, were able to be
processed considerably more quickly and were therefore
processed in groups of 100 Lat/Long pairs.
Figure 2. Length of Geographic Data Strings
Another part of data structuring and cleaning involved working
with time stamp data. The original data for ride start and finish
time were amalgamated with date and year in the format:
“10/1/2011 6:40:16 AM”. From these data we generated several
time variables through a process of extracting a subset of a
string from a string in a process similar to that described above.
We generated a Year variable, a Month variable as an integer
indicating one through 12, a Date variable as a combination
date and month where, for example, 10/31 indicates 31 October,
and day of the week. We also separated start and finish time
from the original data resulting in HH:MM:SS format, e.g.
“06:40:16” that excluded the day, month and year included in
the original data. The duration variable included with the
original data was incorrect so we re-calculated trip duration by
subtracting start time from finish time.
The next step in cleaning the data was to delete several columns
that became redundant or are otherwise not necessary:
DateStarted, DateFinished, TimePaused, Route, and year of
birth. TimePaused contained no data. Year of birth data are
captured by rider age and date of ride which are both retained in
the data set.
After data structuring, cleaning and the preliminary processing
indicated above data we transposed data from records based on
routes to geographic data where records are based on Lat/Long
coordinates suitable for CartoDB and GIS import. We
restructured the data from routes to geographic coordinates with
a pivot table type data summarization using visual basic for
applications (VBA) within Microsoft Excel. Data were first
saved in a new worksheet as values rather than formulas. The
VBA script then transposed the data from records based on
route to a more standard spatial data format where data indicate
point locations of a bicycling route. Along with the pivot of the
Lat/Long data the VBA script associated attributes originally
linked with individual cycling routes to each Lat/Long pair.
These attributes include rider ID, top speed, ride purpose, the
aforementioned time variables, start local government area
(LGA), end LGA, age and gender.
The challenges, solutions and processing volumes in working
with the NSW RiderLog data are summarized in Table 1. The
solutions used to address the problems of extra text in the
Lat/Long strings, correcting the start and finish time, fixing the
incorrect rider duration and splitting the Lat/Long data from one
to two columns, although sometimes time consuming to
execute, were easily accomplished with standard MS Excel
commands that could be applied simultaneously to data
columns or entire worksheets.
Other errors required more sophisticated techniques including
occasions where rides took place across two days resulting in
duration errors, the lack of a time stamp on Lat/Long pairs and
the overall volume of data. Fixing duration errors associated
with multi-day rides required replacing the simple ride duration
formula (finish time minus start time) with a separate formula
that is able to calculate duration while accounting for multiple
days. There were 68 of these errors in the NSW data.
The solution to the lack of time stamps associated with
Lat/Long observations required estimating the time of each
location observation. Calculation of a new data field,
“TimeModel” was also embedded within the pivot script.
Timemodel was calculated by dividing trip duration by number
of segments, summing this value for the segment number in
question and adding this value to start time in order to get an
approximate time stamp for each point in a rider’s route.
The overall challenge of dealing with the large volume of data,
especially reducing the complexity of route data formatted as
long text strings, required numerous iterations performed in
such a way as to minimize file sizes at each given stage. The
volume of data also required the use of multiple files (4 .xlsx
files) to accommodate the entire New South Wales RiderLog
2010 – 2014 dataset. We estimate the RiderLog 2010 – 2014
dataset the covers the entirety of Australia would require 21
.xlsx files. At both the New South Wales and national scales
this volume of data exceeds the definition of big data presented
in Batty (2013); any dataset which cannot fit into an Excel
spreadsheet.
Challenges
Solution
Volume
Extra Text
Find/Replace
68,153 strings
Start/Finish Time
Incorrect
Switch labels 2 columns
Ride Across two Days
Change
formula
68
Ride Duration Incorrect
Re-calculate
26,243 records
Amalgamated Lat/Long
Separate
1 column
No Time Stamp on
Lat/Long data
TimeModel All records
Data Volume
Iteration;
multiple data
files, 4 .xlsx
files for NSW
>48,700,000
possible cells
Table 1. Big Data Challenges, Solutions and Volume for the
NSW Rider data
CartoDB offers multiple ways to view cycling route data, as
points which may be displayed in time sequence and as routes
(lines) which display well in still images. The final step in
preparing our data tables for upload into CartoDB was to split
the amalgamated Lat/Long pairs into two columns. Bringing
point data into CartoDB required connecting, or uploading, the
data and georeferencing, a simple matter of specifying the
latitude and longitude columns in the data. Once correctly
georeferenced data could be displayed and manipulated within
CartoDB. Uploading route data as lines into CartoDB was
facilitated by bringing the point data into GIS and converting
points to lines based on RouteID.
In CartoDB, both temporally dynamic torque maps based on
point data and polyline based cycling route maps may be
displayed by category. The CartoDB Map Layer Wizard may be
used to select among several category options, select the column
that contains the data one wishes to view, and adjust the legend
to symbolize categories by colour.
3. RESULT AND ANALYSIS
In this section of the paper we illustrate how the processed
RiderLog data can be visualised and then undertake some
preliminary analysis of the results with a focus on the City of
Sydney. User profile data which includes gender and year of
birth can be used to create specific map visualisations of the
flow of bicyclists across the city. Also, the user can specify the
purpose of each individual trip and again this can provide an
interesting breakdown of who is bicycling where for what
purpose. Other views of the data can be made on ride duration
and origin of the trip. Using CartoDB we have created a frame
where these parameters may be toggled on or off to visually
analyse bicycle behaviour across the city.
It is important to understand the purpose of a ride when city
planners and policy makers are considering new bicycle
infrastructure. Different Smart Phone applications are used for
different purposes. The RiderLog application is predominantly
used by those commuting and travelling for transport, rather
than recreational purposes. Hence, the results in Figure 3 show
a predominance in bicycle trips made for transport. We can see
the Central Business District (CBD) of Sydney is a hot spot of
activity as one would expect for bicycle movements for travel. It
also highlights the CBD provides limited recreational
opportunities for cyclists. However, examining Figure 3 we can
see that the roadway within Centennial Park, which is located 1
kilometre out of the CBD, is a major attractor for recreational
bicyclists. Using such data we can begin to understand which
part of the city’s bicycle network is being used for what purpose
and this can in turn provide valuable information on future
infrastructure planning provisions across the city.
Figure 3. Sydney Area Rider Routes by Ride Purpose
One of the user profile questions in RiderLog is related to the
age of the bicyclist. We can see in Figure 4 that a significant
number of bicyclists using the RiderLog application are around
40 years of age. It is difficult for such an application to record
journeys made by children travelling to school independently as
most will not have their own smart phone application to record
their journey. There is also no mechanism in the Application for
a bicyclist to log if there might have been a child on board a
bike being dropped to school. So the use of such applications is
difficult in trying to find out information about bicycling
behaviour of children. However, preliminary analysis of this
aged based data would suggest policy makers might like to
target bicycle promotion programs to those aged below 40 if the
goal was to increase bicyclist numbers across the City.
Figure 4. Sydney Area Routes by Rider Age
Understanding gender patterns in bicycling is very important in
order to develop and maintain bicycle infrastructure which is
used by both male and female riders. In Figure 5 we can see the
females (in red) follow distinctive and a more limited set of
routes when bicycling as compared to males. If we visually
ground truth this information we see that female bicyclist
activity is more restricted to those routes where there is
dedicated and indicated bicycling lanes. Whereby male bicyclist
seems to be more comfortable bicycling in areas where bicycle
lanes may not be available. This preliminary analysis suggests
that by providing more dedicated bicycling infrastructure this
could likely result in an increase of female bicyclists across the
city.
Figure 5. Sydney Area Rider Routes by Gender
Understanding how far people are willing to bicycle and from
which origin to destination is also an important piece of
evidence when planning for travel behaviour. Figure 6
illustrates the travel time taken by bicyclists to arrive at their
destination. It indicates that most trips range from 0-15km.
Such information is important in understanding bicycle
catchment areas which is useful in the formulation of city wide
transport and planning strategies.
To further understand bicycle movements across the city origin
data can be most useful (Figure 7). This data can highlight
suburbs and precincts where bicyclists reside. This can further
assist planners in developing municipal specific bicycle
infrastructure strategies. For example those areas where
bicyclists live and are in close proximity to a train station might
then be a candidate for a bicycle parkiteer (a secure parking
station) which supports multi-modal travel behaviour as
illustrated in Figure 8.
Figure 6. Sydney Area Rider Routes by Rider Duration
Figure 7. Sydney Area Rider Routes by Ride Origin
Figure 8. Bicycle parkiteer situated at a train station. Photo
attributed to Bicycle Network
4. CONCLUSIONS
In this paper we have discussed the processing, visualisation
and potential application of bicycling data acquired from
individuals using the RiderLog Smart Phone Application. We
have discussed methods for processing and cleaning the data,
which have initially been undertaken using Excel. However,
given the need to use multiple files and iterative processing to
accommodate one data set we conclude Excel is not sufficient
for handling such data efficiently. When we move to the next
steps of the research we will be looking at upscaling this
approach to include Riderlog data from across all of Australia.
Next steps will look at migrating the database into a platform
which handles big datasets and better support processing and
cleaning, possibly R Project through the pbdR initiative
(Ostrouchov et al. 2012).
Through our cleaning processes we have identified a number of
challenges in aggregating this data from individual rider
journeys to a city wide analysis. In addition to efficiently
handling the large volume of data, the most notable of these
challenges was separating long strings of text containing
geographic data on bicycling routes. While solutions to some
data problems were easily implemented and others were more
challenging, it is important to be cognisant of how data
manipulations at any given stage would impact the data at future
stages of development as well as in the final analysis. When
processing big data it was also important to be cognisant of
one’s end goal and/or desired product outputs as a means of
evaluating the potential impact of data manipulations at any
given step.
Once we have the resultant data processed and cleaned, the
CartoDB online mapping platform was used to visualise the
results across the City of Sydney. In future work with real-time
data feeds, potentially at the scale of analysis of the entire
country of Australia, we will exploit CartoDB’s real-time Big
Data Connectivity and CartoDB’s Deep Insights Technologies.
Using Deep Insights one may manipulate and visualise
hundreds of millions of spatial data points. The use of such
online mapping platforms provide a powerful vehicle for city
planners and policy makers to interact with the data and make
more evidence-based decisions about the shaping of our cities
(Pettit et al. 2015). In this paper we have focused on data and a
visualisation platform which can be used to support city
planning in relation to active transport and providing
recreational opportunities. However, it is important to note the
data collected from smart phone applications such as Riderlog
are not considered statistically rigorous. Future research will
examine data fusion techniques that can be deployed to
combine Riderlog data with more systematic bicycle count data,
household travel survey data and other sources to provide a
richer picture and more robust data source to support evidenced
based decision making.
In an increasingly urbanised world we continue to plan for the
sustainable growth of our cities. This includes promoting active
transport and looking for solutions to alleviate congestion.
There is a critical need for evidenced based city planning and
policy making which uses data from a rich variety of sources to
address such concerns. The potential of smart phone collected
data such as the Riderlog data presented in this research
provides an important source of truth which can be further
interrogated to understand the flow of people and how they
interact with each other and the built environment.
5. ACKNOWLEDGEMENTS
The authors would like to acknowledge Bicycle Network for
supplying the RiderLog data which has made this analysis
possible. Peter Patterson, from the City Futures Research Centre
is also acknowledged for his preliminary exploratory work with
the Riderlog data.
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