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Open Data-Set of Seven Canadian Cities

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Open data has attracted huge attention for the construction of smart city in terms of delivering useful city information to citizens and interacting with citizens from the city council perspective. In this paper, we present an overview of the current status and issues of open data opened by different seven Canadian cities. We start by presenting the characters of open data, followed by data format conclusion and detailed dataset explaination for each Canadian city (e.g., Calgary, Halifax, Surrey, Waterloo, Ottawa, Vancouver, and Toronto) including the different data catalogues and their detailed characteristics. Next, we discuss the state-of-the-art of the tools and applications developed over each city’s open data. Here, we not only illustrate the most successful examples, but particularly consider the potential issues due to the characters of the city datasets. This paper is not only beneficial for a government which can compare its open data status with that of the Canadian cities but also quite useful for users or companies interested in tool development over open city data.
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1
Open Data-Set of Seven Canadian Cities
Haiwei Dong, Senior Member, IEEE, Gobindbir Singh, Aarti Attri, and Abdulmotaleb El Saddik, Fellow, IEEE
Abstract—Open data has attracted huge attention for the
construction of smart city in terms of delivering useful city
information to citizens and interacting with citizens from the city
council perspective. In this paper, we present an overview of the
current status and issues of open data opened by different seven
Canadian cities. We start by presenting the characters of open
data, followed by data format conclusion and detailed dataset
explaination for each Canadian city (e.g., Calgary, Halifax,
Surrey, Waterloo, Ottawa, Vancouver, and Toronto) including
the different data catalogues and their detailed characteristics.
Next, we discuss the state-of-the-art of the tools and applications
developed over each city’s open data. Here, we not only illustrate
the most successful examples, but particularly consider the
potential issues due to the characters of the city datasets. This
paper is not only beneficial for a government which can compare
its open data status with that of the Canadian cities but also quite
useful for users or companies interested in tool development over
open city data.
Index Terms—big data, characters of open-data, smart city,
city application tools.
I. INT ROD UC TI ON
NOWADAYS, all cities in the world are great producers of
data, and this data is given a new term which is known
as Big Data [1]. Efficient management of this huge data is
very essential to constitute powerful tool to have structured
form of data and giving back to the public with the data
having more utility. This real-world data is a key to the
implementation and validation of cities’ social, economic and
educational structure as it contains useful reviews from public
about various activities within the cities. City administrators
need integrated the factual data to make better decisions and
policies to make cities smarter and sustainable. Thus the
availability and accuracy are the major parameters that may
affect the reliability of the resulting estimates. Thus there
are various steps under which a city open data constantly
goes through before it is converted into useful information.
The process involves the collection and then storage of data
for further processing [2]. The data is closely analysed and
segregated in different categories and formats to convert it into
a meaningful information. Furthermore the data visualization
is important concept to make the information available for
citizens in a better structured form. The main purpose to
analyse Open Data is to extract the meaningful information
from this open government data that can contribute to the
H. Dong and A. El Saddik are with Multimedia Computing Research Lab-
oratory (MCRLab), School of Electrical Engineering and Computer Science,
University of Ottawa, 800 King Edward Avenue, ON K1N 6N5, Canada e-
mail: {hdong; elsaddik}@uottawa.ca
G. Singh is with the Department of Electronic Business Technologies,
University of Ottawa, 800 King Edward Avenue, ON K1N 6N5, Canada e-
mail: gsingh032@uottawa.ca.
A. Attri is with the Department of Electrical and Computer Engineering,
Carleton University, 1125 Colonel By Dr, ON K1S 5B6, Canada e-mail:
aarti@cmail.carleton.ca.
betterment of public [3]. There are many efforts made by
researchers and by different organizations to study this huge
data [4].
The significance of open data to bring in innovations and
developments is given a lot of stress to bring sustainability in a
city. Thus many entrepreneurs [5] of IT companies [6] mention
the importance of open data in a sense that it helps to figure
out present issues in a city and give notion to work towards
the betterment of citizens. This is the basis of selecting seven
cities from Canada which has a flourishing IT market so that it
can be helpful for professionals looking for ideas and specific
sectors to work on that ideas. A substantial work has been
done by researchers to emphasize on developing e-services
from government open data [7], [8]. An effort has been made
where a framework has been developed [9] to explore the
status of present Open Government Data (OGD) by using the
content analysis of web portals of government open data from
35 countries and also actually working briefly on open data
portals from four countries Morocco, UAE, Kenya and Ghana
listing only number of data-sets and the formats used.
However there is still a void for detailed analysis at city level
in Canadian cities, which is one of the driving force for this
paper. A city level discussion for open data utilisation in five
smart cities namely Barcelona, Chicago, Manchester, Amster-
dam, and Helsinki has been done [10] in order to highlight the
significance of open data and its resulting innovations in these
cities. In different parts of the world researchers are working
on the initiatives taken by governments for OGD and how
these initiatives has valued the country in its economic growth
as well as democratic empowerment [11]. A considerable
effort has been made in [12] where an event management
system has been developed to generate events in different
categories, after which the huge amount of data generated from
events is analysed and is utilised in a manner to extract results
from it. Although this platform is presented for big data in
general but this can be used as a good platform to study open
data from different categories listed by the cities and develop
tools that might result in an innovation. It has defined the data
collection from different sources such as social media, hard-
sensors and public itself. Also this includes analysis of this
data in structured form to create events that include various
categories as municipal, traffic, medical etc. and thus gives
useful information to the public. Therefore it is a public-
oriented platform which takes information from public and
gives informative knowledge of the city to the public.
However the domain of applications is not limited to city
organization or locations only, it can be different based on
the category of data provided by the city. Application de-
velopment has been done on open city data in the field of
environmental science [13] where visualizations has been used
for Geographical Information System (GIS) in order to show
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2
the effect of clusters of buildings in coastal areas on non-
mitigation of higher temperatures even if sea breezes flows into
inland areas due to obstruction offered by these buildings. The
motivation of this paper is to study open-data-sets available in
different Canadian cities (Calgary, Halifax, Ottawa, Toronto,
Surrey, Waterloo and Vancouver) and perform analysis on this
data which can be used by IT professionals to build a platform
to use this information.
The paper is organized in the manner that firstly the section
II briefs about the characteristics of open data based on which
the quality of of open data provided by a city can be judged.
Further, section III describes the current status of the data
and emphasizes the various characteristics, formats and diverse
catalogues describing the open (government) data. Section IV
addresses various tools developed for open data in different
cities. Further, section V discusses some challenges for open
data and briefly touches the proposed model to deal with
heterogeneous data.
II. CH AR ACT ER IS TI CS O F OPE N DATA
This section will explain a few characteristics of open-data.
The idea has been taken form the general characteristics of
data discussed in the literature [14] which clearly mention
that government if government or any organization wants to
makes its open for public then the deep study of the data
characteristics is very essential. It helps the organization to
look into the resources and then decide what to make open
and in which format it should be flooded to the public. Those
characteristics are as explained as follows:
A. Completeness (Volume)
The completeness of open data refers to the amount of open
data released by the city. It does not refers to the data in
different domains but volume of data in a particular domain.
Most of the cities do not release the full-fledged data available
for a particular category but instead release a sample of the
data which can help the user to work around with the available
attributes and sample data with the attribute values. Ideally
the public data should be available completely to the public
because this data does not have privacy or security issues. The
sense of completeness can only be achieved when electronic
copies of bulk files for public data are provided to the user.
B. Availability
This characteristic defines the availability of the city open
data either through the city open data website or by other
means. Every city provides an open license to all the viewers
to access the data. Usually there are no restrictions on open
data because the sense of open data is lost when the authorities
put restrictions on the released data. It may also happen that
sensitive categories like criminal records might not be made
available without a license or if it is made available, the user
must accept that the city is not responsible for any inferences
made from the available data.
C. Usability
The data provided should be such that the data can be
easily used by the user. Thus the cities are producing data
in digital formats (CSV) over internet. Similarly a particular
data demonstrating number values is much more usable when
represented in tables instead of plain text. So lesser the
processing required on data, more usable the data is.
D. Non-Proprietary
The control of a particular entity or organisation over the
data proves the proprietary rights over the data. However the
open data must have the feature that no one should have
proprietary rights over the data. Therefore, non-proprietary is
a characteristic that open data needs to have.
E. Non-Discriminatory
The data released by the responsible entity should not be
biased towards a group, community, religion or region. The
availability of data should not depend whether the user belongs
to the same city or country, a particular religion, race or
community. Thus city data should be available to anyone who
want it without any prior registration.
F. Variety
The categories of open data must not be limited to selected
sectors. It should have the variety in terms of the categories for
which the open data needs to be collected. The data-sets of the
open data should not focus on the same subject. For example,
if all the data-sets of the city focus on transportation of the
city and ignore the categories like criminal records, buildings,
municipal operations, elections, electricity and water, then the
data-sets of the city lacks variety.
G. Timely Processed and Updated
This characteristics is defined in a sense that the data is
made available as quickly as necessary to reserve the value of
data. Thus the released data for the public must be up to date.
The access to these updated data-sets should also be given
by providing APIs (Application Program Interface) which can
be used directly in applications to fetch the latest data. The
usefulness of the data decreases as the data gets outdated.
H. Summary
Based on the data analysis performed on data-sets for each
city, it would be useful to discuss whether the cities has
attained majority of the open data characteristics discussed
above or not. All the cities except Halifax has maintained
variety in their data-sets in different domains. However, in
terms of completeness Halifax, Surrey, Vancouver and Wa-
terloo have worked on building open data with considerable
number of entries in majority of the data-sets whereas Calgary,
Toronto and Ottawa are still struggling in developing in terms
of numbers. All the cities have made the data available online
on their web portals irrespective of the user’s race, gender,
religion or a region. None of the cities has any proprietary
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3
rights on the open data available on its website. Every city
has listed formats of its data which are machine processable
and are usable in their original form for the user. Each
city is making best of its efforts to process the data timely
or frequently so as to provide users with the latest data.
However, the update frequency actually depends upon the
kind of data-set. For example, “transportation” data is more
frequently updated as compared to “elected officials” data-
sets as “transportation” data is more vulnerable to changes.
For all the cities the laws of usage of open data are governed
by respective provinces of the cities. The detailed description
and analysis of each city’s open data has been done in section
IV where these characteristics are discussed with respect to
the city’s open data.
III. OPE N DATA BY DIFFERENT CANA DI AN CI TI ES
This section discusses and analyses the open data collected
by seven Canadian cities namely Calgary [15], Ottawa [16],
Surrey [17], Toronto [18], Vancouver [19], Waterloo [20]
and Halifax [21]. The data is collected from their respective
websites, on which it is available open to public in the form of
different visualization tools and also to make any changes as
per their needs. This data is categorized in various catalogues
and formats. Several open data catalogues are defined in
various categories which are explained in section IV. These
catalogues are available in different formats as discussed
further in this section. A few of the catalogues from different
cities are available in common formats.
A. Different Formats of Data
There are various formats in which data is represented in
different cities. Some of them are machine-readable which are
difficult for users to understand but there are some formats
which can be easily understood by users. All formats in which
data-sets from seven Canadian cities are defined are as follows:
1) CSV (Comma Separated Files): This formats is very
useful as it is compatible to define large data-sets. Usually the
data-sets like censuses data results, election results, number of
parks/beaches, traffic volume etc. within the cities are available
in CSV formats.
2) DWG (from Drawing): It is a binary file format meta
data related with various open data-sets. It also includes
geometric data information such as maps to define locations
and corresponding photos. Data sets such as road networks,
transportation bikeways, parking etc. are defined in this format.
3) JSON (Java Script Object Notation): Java Script Object
Notation- JSON is one of the format which is easy to read
for any programming language. This file format has been
used by Ottawa, Surrey, Vancouver, Toronto and Waterloo for
listing various data-sets such as Traffic Data (Ottawa), Park
Lights (Surrey), Bicycle Stations (Toronto), Business License
(Vancouver) etc.
4) KML (Keyhole Markup Language): Keyhole Markup
Language is an XML notation for representation of geographic
/geospatial information related with any data-sets for example
traffic cameras location, transport route information, traffic
count, intersection location, parking location etc.
5) SHP (Shapefile): It is one of the most widely used
format for geospatial data representation specifically for geo-
graphic information system (GIS) software. Thus all the data-
sets of various cities which involved with GIS software to de-
fine them more precisely, are essential to write in this format.
Thus the data-sets related with transportation or any service
location (health, recreation, parking) etc. are represented in
this format.
6) XLS (Microsoft Excel Spreadsheet): Many of the data-
sets are available in XLS formats which can be directly used
with correct description given by each columns. It is one of the
format which is easily understood by users. Data sets related
with elections, city boundaries, events etc. are defined in this
format.
7) XML (Extensible Mark-up Language): It is the most
commonly used file format for data exchange as it keeps
the exact structure of the data. It also gives opportunity to
developers to divide and modify different parts of the file.
Various data-sets of different cities like city transit routes and
schedule, traffic cameras, job opportunities etc. are represented
in this format.
B. Open Data-sets of Calgary
According to the city of Calgary, open data is defined as
the information gathered by government on citizen’s behalf
also including their personal information, which is relevant to
use for any purpose including commercial. This information
can be found in different data-sets on the website. Users are
granted a non-exclusive licence to change the data or modify it
according to their own needs but should follow the terms and
conditions defined as “open data catalogue terms of use” on the
city’s website [22]. Transparency of this data is the key factor
to promote accountability and provide useful information to
citizen about their government and their personal needs. The
main purpose of this open data is for the public to explore the
whole information regarding city on a single web link. This
data is categorized in different data-sets which are available
in alphabetical order [15] or in different categories. This data
is extracted by different department such as transport, hydro,
public security, city welfare societies etc. The main purpose
to cover all these sectors of a city is to give an opportunity to
reuse this diversified data in innovative way by citizens and
other organizations.
There are total 12 categories which are related with social,
educational and business areas of citizens and are also based
on government activities in these areas. Various data-sets are
defined under these categories and data is given in different
formats as explained below:
Administrative Boundaries: It includes data related with
the city, community, election wards and natural sites
boundaries (parks, rivers). The data is defined mainly in
DWG, SHP, KML and XML formats.
Census Information: This category defines census data
for residential units and total population count in those
areas along with the area names. This data is given by
community district and by wards from the year 1999-
2015. The data is given in CSV, DWG, SHP, KML and
XML format.
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City Facilities: This category includes data for public
provision, thus the data-sets under this category are
park amenity equipment, parks monuments, playground
equipment, sport equipment, sport surfaces etc. This data
is also defined in DWG, SHP, KML, XML formats.
City of Calgary 2013 election information: It defines the
city’s election information in three different data-sets as
2013 election results by station (XLS and XML), 2013
election results companion guide (XML and DOCX),
2013 election summary results (XML and FILE).
City of Calgary Human Resources: This category defines
data related with the career opportunities within the city
which helps users to explore different career options as
per their needs and skills. This data is available in XML,
URL formats.
City of Calgary News Feed: It describes data-sets related
with various newsroom of the city in XML, URL formats.
This category has the least downloaded data-sets number
as only 33 in the month of June 2016 as per data available
in July 2016.
City Services: It emphasis on various emergency facilities
provided by different government bodies within the city
such as 311 customer satisfaction, fire emergency, fire
station locations and services etc.
Environmental: It includes various data-sets mainly as
habitat, hydrology, natural areas, parks water delivery,
waste recycling facility, water features, water single
family consumption, land use information. The data is
available in DWG, SHP, KML, XML formats.
Geospatial Reference: This category contains only one
data-set i.e. high precision network which defines a con-
trol network within the city for its development, surveying
and mapping. This is monitored by “field surveying
service division”. It is available in XML and PDF format.
311 Requests: This category has data-sets such as 311
call center activity, march 2015 public service requests by
community, road service requests. This data is available
in CSV, KML, SHP and XML formats.
Subdivision and Development Appeal Board Rulings:
This category contains Subdivision and Development Ap-
peal Board Rulings for 2014-2016. The data is available
in XML format. It gives a complete view of all the
hearings. Each hearing has a unique file number and
also a appeal and decision number. The file includes a
summary of hearings, their reason, place and date etc.
Transportation: This is the last category which has maxi-
mum data-sets describing all information related with city
transportation such as road network, traffic volume, traffic
cameras, truck routes etc. Data is defined in CSV, XML,
SHP, KML, DWG under this category. This category has
maximum download number as 2573 in the month of
June 2016 as defined by the data available in July 2016.
Furthermore, RSS feeds for these data-sets are available
online and links to Google maps and bing maps are available
in case of geo-spatial data-sets. The website also have a
few sections to involve the citizens to have their reviews on
the available data-sets. There is a “discussion forum” and a
“citizen dashboard” for public to write their ideas about the
data-sets to improve existing categories.
There is one major drawback of the data available and
that is incompleteness of data i.e. data-sets include only the
sampled data. For instance the data set “traffic cameras” under
“transportation” category has only 77 total entries in CSV
file. This file includes the information about the location of
the traffic cameras along with the reference image but from
researcher point of view or even for commercial use, this data
is not enough to explore it with proper results. This is a prime
challenge for the concerned authorities.
C. Open Data-sets of Halifax
Halifax (Halifax Regional Municipality-HRM) is a new
name in the field of open data. It defines the data as “something
for everyone”, which means that the data is available for public
including technical and non-technical users. The data is man-
aged by ESRI’s ArcGIS (Aeronautical Reconnaissance Cover-
age Geographic Information System) Open Data platform and
is available in different data-sets which can be accessed on [23]
but these data-sets are quite scattered as they are not segregated
either alphabetically or clubbed in categories for easy lookup.
Only 33 data-sets are defined as open-data catalogues [21]
which are named as zoning boundary, transit areas rates, trails,
street centrelines, soild waste collection areas, residents asso-
ciation area rates, recreation area rates, local improvements
area rates, HRP parks, community boundaries, fire protection
area rates, crime, civic addresses, by-law areas, bus-routes,
building symbols, building permits, building outlines, BID area
rates, HRM park recreation features, contours 5m, bus stops,
parking meters, tax designation, transportation area rates,
spot heights, pre-amalgamation boundaries, polling districts,
polling division and community council boundaries. All these
data-sets are avaibale in CSV, KML and Shapefile formats.
Furthermore, the city of Halifax has failed to bring much
variety into the categories but there are data-sets such as
data defined by transportation department that contains enough
data for users (citizens, researchers, analysts, developers) to
extract useful information as per their needs. Thus the city has
definitely provided quality data having considerable number of
entries in most of the data-sets. This data is easy to use by
citizens, researchers and analysts as almost all data-sets are
defined in tabular format. Also different APIs (Application
Programming Interface) are available for software developers
to easily access the data directly. A lot of detailing about
the attributes, data-sets and the creation of data-sets has been
provided as metadata.
D. Open Data-sets of Surrey
The city of Surrey in the province British Columbia has
defined the open data to be an idea according to which some
data must be available for every one free of cost and can be
used and republished. The data is available worldwide under
the open non-exclusive license provided by the City of Surrey
and governed by the laws of province of British Columbia and
the applicable laws of Canada which allows the user to copy,
modify, reuse, publish and translate the data without giving
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5
any warranty over the errors, omissions and completeness of
the data. The objective of the city to promote open data is
to empower citizens with a good quality data, help small
businesses flourish, build a chance to develop its health and
education facilities, economic productivity and create more
scope for scientific research. Moreover the city of Surrey also
expects that a useful research or data evaluation done on this
data could come handy for the city in future. The city has taken
the help of open data portal platform CKAN (Comprehensive
Knowledge Archive Network) for maintaining its open data.
Surrey has broadly classified its 325 data-sets among 12
categories which are explained as follows:
Business and Economy: It has mainly 11 data-sets which
are business licenses, population estimation, employment
in arts, culture, individuals with low income, land in
food production, availability of employment, businesses
by sector, employees by sector, rental market, business
improvement areas, restaurants and majority of these
data-sets are available in CSV formats.
Community Services: The data-sets under this category
are places of interest (available in CSV), licensed child
care (in CSV), low cost and free resources (in CSV,
KML, FGDB), registration in city programs (in CSV),
social housing (CSV), schools (available in DWG, FGDB,
KML, JSON, API), garbage recycling collection days
(available in DWG, FGDB, KML, JSON, API) and
collection route boundaries (available in DWG, FGDB,
KML, JSON, API).
Environment: It has various data-sets related with en-
vironmental bodies such as water consumption, trees
planted, parks, drainage water bodies, drainage flood
control, ecosystem sites, community waste etc. and the
majority of the data-sets are available in CSV formats
thus easy to understand and use by the users.
Finance: It includes three major data-sets namely city
spending on public art, city tax base and city funding
on beautification projects and all these three data-sets are
available in CSV format.
Health and Safety: It includes data-sets for public health
based facilities such as availability of doctors, crime and
collision incidents and criminal offenses. All of these
data-sets are available in CSV formats thus easy to
explore by public.
Infrastructure: It is mainly defined to give the idea of
whole network connectivity within the city such as water
supply, sanitary manholes, sanitary valves, signs etc.
Almost all of these data-sets are defined in the formats
which are specifically built to locate the geographic
location of the entity such as KML, JSON, DWG, FGDB.
Land Use and Development: It is built to define pro-
portion of land utilization for different purposes such as
buildings, farming, urban centers etc. This data is need to
be defined with exact location thus the formats which are
used for these data-sets are DWG, FGDB, KML, JSON,
API.
Recreation and Culture: It includes data-sets as heritage
sites (CSV), public art (CSV), arts and culture groups
(CSV), youth centered events (CSV), events (JSON) and
heritage routes (available in CSV, FGDB, KML, JSON,
API ).
Transportation: It defines various data-sets related with
traffic network within the city such as traffic cameras
(CSV), traffic signals (CSV, JSON, KML, DWG), poles
(CSV, JSON, KML, DWG), railway crossing (CSV,
JSON, KML, DWG), traffic count (CSV), etc.
Thus Surrey has surely tried to achieve variety in its data-
sets and has covered many different domains. Further a lot of
serious efforts have been committed by the city to build and
maintain this much amount of data.
E. Open Data-sets of Waterloo
The city of waterloo has maintained a very good quality
of open data covering all the aspects of open data. The data
is open to everyone irrespective of the region, age, race or
community. Hence, the open data is purely non-discriminatory.
The work done by the city of Waterloo to collect and organize
the data is clearly remarkable in contrast to other Ontario cities
like Toronto or Ottawa. Waterloo has focused on maintaining
the variety as well as the volume in the city open data. The
city has provided access to 13 categories which further have
various data-sets. These categories are explained below:
Events: This category contains data-sets related to differ-
ent events in the city on different dates along with loca-
tions helping the public to plan their activities according
to their interests. The data available in these data-sets is
provided in spreadsheet format.
Base Data: It mainly has data-sets as buildings, railway
contours 2012 city boundary historical, addresses and
roads which are available in spreadsheet, KML and SHP
file format.
Boundaries: It is having various data-sets such as city
boundary, polling 2014, wards 2014, wards, 2010, neigh-
borhood associations, district plans and all are available
in spreadsheet, KML, shapefile format.
Closures: This category is available in spreadsheet, KML
and SHP file. The spreadsheet consists with only sample
data with a very few entries. It gives information re-
garding closure name (sidewalk, road, intersection), street
name, date for closure and some specific information as
“sidewalk closed- please use other side of street” to for
user’s convenience.
Community: It has four data-sets which are community
access bikeshare stations, neighborhood matching fund,
neighborhood associations, older adult housing directory.
All are available in spreadsheet, KML and SHP format.
The first data-set gives complete information regarding
bike station such as station name, location, access time
and bike counts etc. The second data-sets defines projects
name which are focusing on one community such as arts,
education, environment, history, public safety, community
building or recreation. They have received neighborhood
fund matching grant. The third data-set describes the
boundaries within neighborhood association within the
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6
city. The last data-sets defines information about residen-
tial areas for older adults in the city.
Elections: It is having 19 data-sets equipped with data
from different wards related to candidates, results etc for
different years in the city.
Environment: It is mainly for the users to explore the
current status of the data and help in maintaining sus-
tainability of the resources. The data is available in
spreadsheet, KML, SHP file.
Heritage: It defines data-sets as walkability network,
heritage buildings, city boundary historical, historical
streets and all of these data-sets are avaiable in three
formats- spreadsheet, KML, SHP file.
Parks and Recreation: It has 9 data-sets namely parks,
bicycle parking, bylaw parking infraction, parking lots,
sports field and diamonds, trails, playground, recreation
points, outdoor rinks. All these data-sets are easily ac-
cessible by all users as it is available in simple tabular
format too.
Points of Interest: It is available in spreadsheet, SHP,
KML formats and it contains the data-sets which describe
the points of interest in this city like important dates and
places.
Records: Its data is also in the same three formats as
define above.
Transportation: It has maximum data-sets as a total of 23
which are related with the transportation activities within
the city such as bicycle counts, walkability network,
major transportation routes, parking etc.
Thus each of the mentioned categories have subsets falling
under that particular category which sums up to 122 data-sets
overall.
F. Open Data-sets of Ottawa
The capital city of Canada has provided access to its open
data with the help of CKAN (Comprehensive Knowledge
Archive Network) and it can be downloaded in multiple for-
mats as well as the data can be fetched by using API functions
provided on the website which ensures that every time the
API is hit, the most recently updated data is fetched. The
city of Ottawa provides an open non-exclusive license to the
user worldwide to use, distribute and modify the data without
granting any proprietary rights to the user. This user license
is governed by the laws of the Province of Ontario. However
the city does not give any warranty for the completeness or
accuracy of the data.
The city of Ottawa has listed 15 organizations which have
helped the city to build and maintain this data. These organiza-
tions are- City Clerk and Solicitor (7 Data-sets), Community
and Social Services (8 Data-sets), Crime Prevention Ottawa
(1 Data-set), Emergency and Protective Services (2 Data-
sets), Environmental Services (5 Data-sets), Financial Services
(2 Data-sets), Human Resources (3 Data-sets), Infrastructure
Services (35 Data-sets), OC Transpo (3 Data-sets), Ottawa
Public Library (5 Data-sets), Parks, Recreation and Cultural
Services (23 Data-sets), Planning and Growth Management
(12 Data-sets), Ottawa Public Health (3 Data-sets), Public
Works (7 Data-sets), Service Ottawa (7 Datasets). All these
organizations belong to different domains and have provided
data according to that particular domain. The data is placed
under 9 groups or categories namely Business and Economy,
City Hall,Demographics,Environment,Geography and Maps,
Health and Safety,Living,Planning and Development and
Transportation. These 9 categories contain 129 data-sets from
different sectors ensuring the variety in the open data. Those
are explained as follows:
Business and Economy: This is the first category which
has defined 2 data-sets namely business improvement
areas (available in SHP, GeoJSON formats) and Job
Opportunity (available in XML, JSON format).
City Hall: It has information related with the elections,
voting places and nominated candidates in SHP, CSV,
GeoJSON and CSV formats respectively.
Demographics: This category has mainly two data-sets
providing data for 311 monthly service requests submis-
sion from 2013-2014 (available in XLS, XLSx formats)
and ward data from census for 2006 and 2011 (available
in CSV format).
Environment: It mainly emphasise on natural bodies
within the city as water, rivers, beach water, water quality,
street trees and various formats are used for these data-
sets such as XML, DWG, CSV, SHP, GeoJSON for water,
XML, DWG, CSV, KMZ, SHP, GeoJSON for rivers and
XLS for beach water sampling data, CSV format for
water quality, KMZ, SHP, CSV, XML for street trees and
XLS for drinking water.
Geography and Maps: It defines various routes with exact
locations for various means of transportation such as bus,
rails, airports runways. It also has data-sets related with
beaches within city, also the sports field, basketball, ten-
nis, volleyball courts, truck routes and pedestrian network
in all over the city. The main formats for these data-sets
are XML, DWG, KMZ, SHP, GeoJSON.
Health and Safety: It mainly includes data related with
health clinics which is mainly in tabular format.
Living: It defines the data for public facilities such as
cultural resources, garbage schedule, library, street food
vendors, library programs, library hours and locations,
museums etc.
Planning and Development: It has main three data-sets
as large buildings, drainage and neighborhood names. All
these data-sets are avaiable in XML, DWG, KMZ, CSV,
SHP, GeoJSON formats.
Transportation: It has the data-sets as O-train stations,
tracks, cycling network, OC-transpo schedule, parking
lots, truck routes, railway, traffic data etc. The data related
with OC-Tranpo gives live changes in the schedule of
buses to the users.
Thus the city of Ottawa has diversified data however, as
stated above the city clearly states that it provides no warranty
of the completeness of the data which is pretty much clear by
looking at the data. The data entries range maximum up to a
few hundreds and with very less detailing. The city of Ottawa
has left a lot of room for the workers to make more progress
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7
in building a good quality of city data.
The city of Ottawa has maintained the metadata for each
set describing the creation date as well as the last update for
the data-set. The city has not failed to deliver the details about
the dataset and its attributes or fields and also the frequency
of updates for each dataset and has also listed the accuracy
for each dataset.
G. Open Data-sets of Vancouver
The city of Vancouver defines open data as the the data
that people want, i.e., the data that is of the most interest to
the community. The notion of this project came into existence
when in May 2009, Vancouver City Council passed a project
named as Open3 which was to make data public for everyone
to explore and make changes as per their requirements. The
prime root for this draft was to do research to find out
which data would be more useful for public such as legal,
public services, technical and business related data. After the
successful efforts, the city was able to launch its open data
website in September 2009 [19] and have been adding new
data-sets on the website since then. It is for public to use or
make changes as per the open government license defined on
the website. The important thing to be noticed here is that this
city have not defined any category for its data rather the data
is made available to public in data-sets. Those are on the city’s
website in alphabetical order. There are total 186 data-sets and
are related with social, business and government areas within
the city.
The first data-set is accessible Parking that is available in
DWG, SHP, KML format. It defines all locations which is
meant for parking. The next data-set is parks, rivers. The
apartment recycling area in which data is defined mainly in
SHP, KML formats. The next data-set is bike ways that is
available in DWG, SHP, KML. The data-set business license
defines all the business related license in CSV, XLS, JSON,
XML format. The next data-set is census local area profile
2001, 2006, 2011 including data for public provision. This
data is defined in CSV, XLS formats. The next data-set is
city boundaries which is available in DWG, SHP, KML.
Further alphabetically defined data-sets are community centers
(CSV, DWG, SHP, KML, XLS), community gardens and food
trees (CSV, XLS), crime (CSV, XLS, JSON, SHP), drinking
fountains (DWG, SHP, KML, CSV, XLS, JSON), employee
remuneration and expenses (CSV, XLS), elementary school
boundaries (DWG, SHP, KML, XML), food vendors (CSV,
KML, XLS), garbage collection schedule zones (DWG, SHP,
KML), heritage property (CSV, KML, XLS), intersections
(DWG, SHP, KML), libraries (CSV, DWG, KML, SHP, XLS),
local area boundary (CSV, DWG, XLS, SHP), motorcycle
parking (DWG, KML, SHP), municipal election results (CSV,
XLS), noise control areas (DWG, KML, SHP), Olympic city
site (DWG, KML, SHP), one way streets (DWG, KML,
SHP), parks listing (CSV, XLS, XML), parking meter (DWG,
KML, SHP), public art series (DWG, KML, SHP), public
streets (DWG, KML, SHP), public washrooms (CSV, XLS,
KML), railway (KML, SHP), road ahead closures (DWG,
KML, SHP), road ahead under construction (DWG, KML,
SHP), sanitary mains (DWG, KML, SHP), sanitary manhole
(DWG, KML, SHP), schools (CSV, XLS, DWG, KML, SHP),
street lighting pole (DWG, KML, SHP), street trees (CSV,
XLS, JSON, XML), traffic count directional (DWG, KML,
SHP), traffic signals (DWG, KML, SHP), truck route (SHP),
voting places (CSV, XLS, KML, SHP), water control valves
(DWG, KML, SHP), weekend play-field status (CSV, XLS,
JSON, XML), water transmission mains (DWG, KML, SHP),
zoning districts and labels (DWG, KML, SHP). The data is
available in different formats and most of the data-sets have
the entries in thousands which is good for researchers and also
for analysts for evaluation purpose.
H. Open Data-sets of Toronto
Toronto, the largest city of Canada is working alongside
Montreal, Vancouver, Ottawa and Edmonton on improving
the quality standards and maintenance of open city data. This
project is being called G4Plus. Toronto has definitely achieved
a good rating in developing tools over its city open data but
it still has not provided the users with a good volume of data.
The city has provided access to 214 data-sets which are defined
under ten categories which are explained as follows:
Business: This is the first category which has mainly
five data-sets namely bicycle shops (available in SHP
file), business improvement areas (SHP file), economic
indicators (excel file), Toronto economic bulletin(excel
file), Toronto employment survey summary table (excel
file).
Community Services: It has seven data-sets which are
defined to give information to public for various social
activities such as human right office service statistics,
licensed child care centers, marriage licensed statistics,
Ontario early year centers (Toronto), school locations,
social housing, sports and recreation, Toronto public
library branch locations. These data-sets are defined in
different formats such as CSV, Excel file, SHP, KML etc.
Culture and Tourism: This category is defined with six
data-sets which gives useful information to citizens and
tourists about city’s cultural life. The data-sets which are
involved with this category are bicycle stations (XML,
JSON), cultural hot-spots (SHP), festival and events
(XML), places of interest and Toronto attractions (excel,
SHP), places of worship (SHP), sports and recreation
(excel).
Development and Infrastructure: It defines the city’s
infrastructure and also give an idea of construction-
standards within the city. Main data-sets under this cat-
egory are building permits (available in CSV, XML),
heritage districts (SHP), intersection file (SHP), urban
centers (DWG, FGDB, KML, JSON, API), farming pro-
tection development permit area (DWG, FGDB, KML,
JSON, API), town center land use plan( FGDB, KML,
JSON, API), legal plan boundaries (DWG, FGDB, KML,
JSON, API), agriculture land reserve (DWG, FGDB,
KML, JSON, API).
Environment: This category includes data-sets related
with various environmental activities like chemical track-
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8
ing, renewable energy installation and many more. These
data-sets are available in SHP, excel, XML formats.
Finance: It defines data-sets which provides city’s capital
budget (excel file), tax information (excel file), parking
ticket price (CSV format), water billing (excel file) etc.
Health: This category is mainly defined to give essential
information related with various health services such as
ambulance station locations, care centers etc.
Parks and Recreation: This category is mainly design to
give a brief knowledge of parks location and recreation
activities within cities. The main data-sets are forest and
land cover, parks, parks drinking fountains.
Public Safety: It has data-sets for public safety related
information such as fire station location, police station
locations, Also it has data-sets called road restriction to
provide safety instructions to public.
Transportation: This is the last category which has max-
imum number of data-sets. Those are traffic cameras
(CSV), traffic signals (CSV, DWG, FGDB, KML, JSON,
API), railway crossing (CSV, FGDB, KML, JSON, API),
poles (CSV, DWG, FGDB, KML, JSON, API), mode of
travel to work (CSV), traffic counts 2013-2015 (CSV),
traffic volume (CSV), walking routes (DWG, FGDB,
KML, JSON, API), truck routes (DWG, FGDB, KML,
JSON, API).
Furthermore, as explained above various data-sets are de-
fined under these categories for users to explore complete
information of the city. The users are given access to all data-
sets along with a very descriptive metadata for that particular
data-set having information about the data itself, data-set
publish date, update frequency, data owner and the available
formats. The data is updated frequently and API (Application
Programming Interface) functions have also been provided by
the organizations to get the latest data with a single API hit.
As discussed in the conclusion of Section II, Halifax has
left a plenty of room to be filled both on maintaining quality
open data. The best practice every city has followed is to
provide a descriptive metadata containing details about the
dataset, update frequency, creation date, attribute description
etc. CKAN (Comprehensive Knowledge Archive Network) is
helping most of the cities to maintain its data and the license
for all the cities is governed by laws of respective provinces.
IV. TOO LS DE VE LO PE D OVE R OPE N DATA OF DIFFERENT
CIT IE S
Data visualization is a very important aspect in this open
data study. The raw data has a very limited utility for a user
who is not working on processing and analysis of this data.
Therefore, there must be a method to create or derive mean-
ingful data from this raw data. This data needs pre-processing
and analysis to get inferences from this data. However the
inferences made can best be presented in form of visualizations
and comparisons. Hence almost every city has worked on
building applications aiming at doing the analysis part and
then visualizing the analysed data. Not only the cities has
built applications but also users has contributed by working on
data-sets from different categories and getting useful results.
Most of these cities has listed the applications built on its
city open data and have opted various visualization tools and
methods. Out of all those, one of the most widely used is
information graphics (Infographics) which is the combined
version of illustration methods and text representation. It is
good enough to give a clear idea to any user. The basis for
each application and visualization method is converting the
raw data into a form such that it is understood by a tool
on which the user is working. For example, a user Lauren
Archer has used the Garbage and Recycling Data provided by
the City of Toronto to produce a Web App named Garbage
and Recycling Day Google Calendars having visualisations in
form of Google Calendars [24]. The user has managed the raw
data by converting it into a data that can be directly used into
Google calendars application and has developed a schedule for
garbage and recycling days which can be downloaded by other
users. The following sub-sections illustrate the tools developed
by the respective cities on city open data.
A. Tools Developed over Calgary’s Open Data
There are various data-visualization tools which are formu-
lated with the city’s open data. Sports is the one of highlighted
area within the city as the city of Calgary is one of the sports
rich city which is proved by the fact that sport business group,
London shortlisted Calgary as “ultimate sport city 2016” in
the month of January, 2016. Thus it has attracted developers’
attentions and they made one applications which is quite
famous in the city, called Sportsity. This is meant to give
the information about various sports fields within the city all
in one place. The pictorial view of one of the application
Sportsity is shown in (Figure 1(a)) which is basically designed
to help users to find out any sport court or athletic park in the
city and also provides the user with the directions with the help
of integrated Google Maps in the application. This application
uses City of Calgary’s open dataset “city amenities” to figure
out various sports recreation centres for soccer, basketball,
cricket, golf, tennis and many more available throughout the
city. It also lists the reviews provided by the visitors for each
location and also provides the user with an option to write a
review.
Another tool developed with city’s open data is Calgary
Traffic Alerts which is designed to give live traffic alerts to
users. It includes information such as notification alerts for
traffic incidents as accidents, construction etc., paid and unpaid
parking lots, construction closures detours, traffic cameras
location. Similarly work has been done on other data-sets as
well to develop tools such as +15 Walkway,TransitGo and
Live Transit.
B. Tools Developed over Halifax’s Open Data
The city of Halifax worked progressively towards develop-
ing tools over its open data. The city has conducted the open
data application contest named “Apps4Halifax” with the help
of IBM where the users are open to post their ideas as well as
submit their developed tools [25]. By the end of this contest,
a winner is decided amongst both the categories-Ideas and
Apps. In the 2013-14 contest, a total of 275 ideas and 38 apps
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9
(a) (b) (c)
(d) (e) (f)
Fig. 1: Tools developed on open data of different cities. (a) Sportsity Application: The city of Calgary. (b) Low Cost and
Free Resources Application: The city of Surrey. (c) PingStreet Toronto Application: The city of Waterloo. (d) Save the Rain
Application: The city of Ottawa. (e) PayByPhone Application: The city of Vancouver. (f) Wellbeing Toronto Application: The
city of Toronto.
were submitted online. The major sponsors of this contest were
IBM (Main title sponsor), Esri Canada (Challenge sponsor),
Global Halifax (Media Sponsor) and Telus (Category sponsor).
The Ideas and Apps were submitted under 4 broad categories
namely Your City, Go Green, Live It Up and Keep’er Movin’.
One of the winners in the idea category was Garbage When?
which uses the GPS of the mobile device and based on the
location it notifies the user about the garbage day and also
sends notifications in case of cancellations. The app which
won in the Go Green category was Halification which is again
based on municipal notifications for the city residents. The
residents can choose from a number of subscriptions such
as crime, power outages, school closures, weather warnings
and traffic feeds. The sources of these alerts are the Open
data catalogue of Halifax city, Twitter, Environment Canada
and Halifax.ca. The data-sets used by this app from the
Halifax data catalogue are “civic addresses”, “solid waste
collection areas” and “crime statistics”. A contest ensures
more participation and interest from the users as it instils the
competitive spirit amongst the participants due to which they
thrive for achieving the best. Hence it a progressive step by
the city of Halifax for promoting the tool development over
its open data.
C. Tools Developed over Surrey’s Open Data
There are various applications which are built with various
open data-sets available on the open data website of the city.
Out of those application, one is built on the idea of the
Surrey’s poverty reduction plan i.e. which was made up by
Surrey’s planning and development department along with the
engineering department. This plan was based on the vision to
make ”low cost and free resources” available to every citizen
of Surrey. The application and its name is based on the same
concept. The pictorial view of the application is shown in
Figure 1(b), the complete view of one of the application is
shown which is to find out the location of low cost services
as food, health services etc. The system provides users to apply
filter as per their needs and thus shows the result accordingly.
There are many other tools which are developed from the
various open data-sets to help people in various ways. The
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10
most popular tools are My Surrey App,Surrey Request App,
Rethink Waste Mobile App,Building Inspection Request App,
COSMOS App,Surrey Libraries App. One of the applica-
tion that uses variety of data-sets is My Surrey App as it
gives informative access to users to have a almost complete
information about the city. It is the combination of all the
other applications that are mentioned above. The main page
includes news, point of interest, events, jobs, Surrey request,
COSMOS, rethink waste, library, parking, bike routes etc. It
has also introduced new services like Contact Surrey, which
allows users can write their questions and they can get answers
for those in pilot project within the city that is handled by
IBM Watson technology. This application has gained a lot of
popularity within the city of Surrey.
D. Tools Developed over Waterloo’s Open Data
Waterloo uses different mobile applications for users to
visualize the data which is collected from different government
bodies and from the citizens of the city. As the data collected
is very complex and unstructured, so software applications are
required to re-use this data in structured manner before giving
to users to explore it. One of the famous mobile application
which uses city’s open data is Pingstreet (Figure 1(c)), which
is designed for daily interaction of citizens and different
government organization, social media etc. It is quite popular
within the city as it provides real time access to various
activities such as road closures, events, garbage and recycling,
overnight parking etc. It is a location based discovery tool
and thus all information is delivered directly to user’s mobile
devices without any cost cost.
Waterloo park finder is an another tool that is using open
data-set parks that comes under Parks and Recreation category
to help users to find out the exact location of parks within city.
There are three options to do the search easily on the applica-
tion i.e. search can be made by name (albert green, Alexandra
lot etc.) of the park or by type of park (neighborhood park,
environmental reserves, special agreement parkland, culturally
significant parks etc.) or by facilities (benches, playground,
hydro, water service, etc.) included in the park. After this
selection the next page will show all the exact location of
the park. It can be seen as a small icon, which on selection
provides a short description about the park to the user. Another
popular tool is Public Art Waterloo which is developed to
give a complete knowledge about public art and its location
within the city. It also gives information about nearby public
art locations.
E. Tools Developed over Ottawa’s Open Data
There are more than 60 applications which are developed
based upon the open data of Ottawa by the users participating
in Ottawa Open Data App Contest sponsored by Microsoft.
There are four categories under which these applications were
developed [24]. Those are listed as follows:
On the Move (Sponsored by Telus)
Having Fun (Sponsored by Nova Networks)
Your City (Sponsored by CGI)
Data Analysis and Visualization (Sponsored by Oracle)
All these categories have a few applications to give simple
view of information as per their needs. The application that
has pleased the users is Save the Rain. It uses the open data-
sets Drinking Water Summary and Ontario Well Record Data.
The significance of this application is to make the users realise
that how much rain water could be harvested over their roof
tops in a year. The reports generated from this application
is amazing the users and the users like it as well. Moreover
this tool has been equipped with an attractive user interface
as shown in Figure 1(d).
There are many other tools developed over Ottawa’s open
data-sets in the contest. Another example of such tools is
Ottawa events which is designed to have a complete knowl-
edge of Ottawa’s present and upcoming events. The events are
grouped together on the basis of date, month and week. It also
shows the address and tickets for each events. The main menu
of this application gives two options to users to explore events
either by event type (dance, fair/festival, film/new media etc.)
or by location (Ottawa urban area, Ottawa rural area etc.). It
also gives a short description about the event such as dance
party timing, meal menu etc. Thus it is good way to explore
social activities within the city. There are many other applica-
tions like Ottawa Garbage collecting schedule,Recreation in
Ottawa,OC Transpo Tracker,Ottawa Construction permits,
Environmental Inspection App,Libraries Ottawa,Ottawa 311
Service Request etc. which are using various open data-sets to
give a useful information to citizens.
F. Tools Developed over Vancouver’s Open Data
There are main four mobile applications developed by open
data of Vancouver namely VanConnect, VanCollect, VanGolf,
PayByPhone. These are meant to provide required updated
information to the users such as road closures, emergency
alerts and many more. PayByPhone is a very useful tool with
maximum downloads by users that has been released by the
City of Vancouver that allows the user to pay for the street
parking by phone and also extend the parking via call or text.
The user is charged only for the specific time he occupies the
parking slot which costs less as compared to a specific time
interval charge. There is also a provision to set alerts for the
expiry of the parking ticket and also lets the user to get the
receipts on the phone. The user interface of the application
looks as shown in Figure 1(e).
City of Vancouver has developed another application Van-
connect that proves its utility to build an interaction bridge
between Vancouver city hall and the citizens of the city. It
allows the users to submit there requests as per their need
or they can submit any complaints regarding garbage, street
light, traffic light, abandoned vehicle etc. It gives users various
options to clarify their issues such as type, location, time and
description and users can also upload a pictures for the same. It
also updates users with news, emergency information etc. One
of the biggest advantage of this application is that it connects
users with Vancouver city hall 24X7.
G. Tools Developed over Toronto’s Open Data
The city of Toronto has made a huge progress in terms of
visualisations of its available data. Many different organisa-
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tions have come forward to utilise the open data and make
inferences from it and use various kinds of visualisations
for representation. The city has listed a number of mobile
applications as well as web applications which have used
the city data for visualisations. For example, a user Lauren
Archer has used the Garbage and Recycling Data provided
by the city to produce a Web Application named Garbage
and Recycling Day Google Calendars having visualisations in
form of Google Calendars [26]. The user has managed the raw
data by converting it into a data that can be directly used into
Google calendars application and has developed a schedule for
garbage and recycling days which can be downloaded by other
users. Similarly other users have contributed and worked on
various data-sets at different platforms to develop visualisation
tools for the given data.
City of Toronto has officially released a web based map
visualisation application that is designed to ensure as well as
evaluate the city’s well being and is thus named as Wellbeing
Toronto (WT) as shown in Figure 1(f). This visualisation tool is
targeted for residents who need to have a good understanding
of the area and communities they are living in and working
and also for businesses or organizations that require parameter
evaluations in order to be informed about their customers.
WT provides a central platform for discussion of issues at
neighbourhood level. It covers multiple domains such as
transportation, crime, safety, education, culture etc to select
indicators or parameters and provides an option to select the
reference period. WT allows to select parameters from listed
domains within the framework with a flexibility of changing
the weight of a particular parameter and provide corresponding
data for the users. It does not provide the open data in its raw
form but in a business processed form which is more usable
for the user. It also gives the user an option to use WT as a
basis of developing other tools by using indicators in WT.
One more tool build on Toronto’s open data is Open Toronto
which is build to provide an easy access to Events and
Festivals information within the city of Toronto. It allows
users to browse the events and festivals on a particular date
or in a particular months as it gives three options to users
for browsing the data i.e. all, today and selected date. Thus
as per the predefined information the next will show the
results accordingly. Users can add information to their personal
calenders in their mobile phones. Also they can share the
information to their friends and relatives by sending them
emails, SMS etc.
H. Summary
From the discussion about tools developed by different
cities, it can be deduced that researchers, business organ-
isations, city authorities and users as well understand the
importance of application development on city’s open data.
All cities discussed above have worked upon developing tools
on open data either directly or through the users. Toronto
leads all the seven city in this department as not only the
users have participated in enormous numbers but also city of
Toronto has developed extremely useful tools like Wellbeing
Toronto that has proved its utility in the users, businesses and
researchers. It can also be concluded about these tools that
most of the tools developed are using Maps as its basis for
visualisation and lesser of previously used methods like graphs
and charts as Maps look more presentable to display the useful
information. This analysis also leaves scope for developers and
organizations to set foot in and develop tools based on city
open data.
V. OP EN DATA CHA LL EN GE S
This section talks about the open challenges for open data.
This includes mainly the open data challenges/problems for
academia and industries who want to work on it on a large
scale.
A. Research Challenges
Based on the discussion on open data-sets and its charac-
teristics so far, some research challenges can be drawn. This
sub-section addresses the shortcomings in the study of open
data for educational and research prospective. Today the var-
ious technical/non-technical organizations and also different
educational institutes are working on open data and trying to
figure out some dynamic way to deal with such a huge open
data. Researchers are trying to find out that the solutions for
the following questions:
How much data is available for the public to access and
view?
Is the released open data useful enough for users
with different backgrounds (technical/non-technical pro-
fessionals etc.)?
How can applications be developed in such a way that
visualization, usage and comparison of open data can be
made easy accessible and understandable to everyone?
What amount of data and in which format should open
data in cities be opened to the public by governmental
and non-governmental organizations and how can they
manage this public data in an efficient way?
There are limitations related to the access of this data as some
cities either did not publish the complete data sets or did not
make the open to the public. Some of the existing data-sets
are only available with a few lines of field entries. To make
city data available (opened) completely there is a need to refine
and bundle complete set of data in public APIs. The developers
should publish the software patches related with the APIs as
an open source so that the researchers can contribute to public
code projects.
B. Open Challenges for Integration of Open Data
Integration of this heterogeneous open data is a very pow-
erful approach. It allows to put the open data together across
various data-sets in such a way that it can be easily explored by
the users. As the section III has explained the various data-
sets available in different cities, it is quite understood that
every city is working independently in its own way to put data
available for users and therefore these data-sets have generally
not been designed to integrate together. The first problem to
deal with integration process of this open data is a lack of
common open source platform to study this data.
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12
TABLE I: Characteristics of Open Data and Tools/Applications Used
City
Name
Open Data Characteristics Visualization Tools/Applications Used
Calgary Availability, Non-Proprietary, Non-
Discriminatory, Timely Processed and
Updated
Sportsity, Calgary Trafc Alerts, +15 Walk-
way, TransitGo and Live Transit
Halifax Availability, Non-Proprietary, Non-
Discriminatory, Timely Processed and
Updated, Variety
Halication
Surrey Completeness, Availability, Non-
Proprietary, Non-Discriminatory, Timely
Processed and Updated
My Surrey App, Surrey Request App, Re-
think Waste Mobile App, Building Inspec-
tion Request App, COSMOS App, Surrey
Libraries App, My Surrey App, low cost
and free resources
Waterloo Completeness, Availability, Non-
Proprietary, Non-Discriminatory , Timely
Processed and Updated, Variety
Pingstreet, Waterloo park nder, Parks and
Recreation, Public Art Waterloo
Ottawa Availability, Non-Proprietary, Non-
Discriminatory, Timely Processed and
Updated
Ottawa Garbage collecting schedule,
Recreation in Ottawa, OC Transpo
Tracker, Ottawa Construction permits,
Environmental Inspection App, Libraries
Ottawa, Ottawa 311 Service Request,
Save the Rain
Vancouver Availability, Non-Proprietary, Non-
Discriminatory, Timely Processed and
Updated, Variety
VanConnect, VanCollect, VanGolf, Pay-
ByPhone
Toronto Availability, Non-Proprietary , Non-
Discriminatory, Timely Processed and
Updated
Garbage and Recycling Day Google Cal-
endars, Wellbeing Toronto (WT), Open
Toronto
Furthermore, the differences in formats (CSV, XML, DMG,
KML etc.) is the another problem in the integration of open
data sources. It is easy to download the data from the re-
spective websites of the cities but it difficult and challenging
to recognize the common fields between data collected from
various cities. Moreover the characteristics of this open data
and tools/applications build on the data-sets are different in all
cities as mentioned in table I. Thus it is difficult for a developer
to integrate the data and make a common visualization tool for
this huge open data. Also the diversity of characteristics and
tools used in these seven cities create usage difficulties for the
users.
Also the other kind of problems that are shown up for
the integration of open data may include improper data entry,
missing data or a lack of common attributes between data from
different sources. But on the high level view of integrated open
data may see these problems in a way that cannot be seen by
individual data-sets and thus lead to data quality improvements
without the need for extensive polishing.
C. Proposed Model : Data Wrapper
We discuss an example of the performed study of open data
in seven Canadian cities by extracting common information
regarding some data in these cities. The data files are shown
in Figure 2 which specifies the three formats of same data-set
i.e. traffic camera. We developed a common model called Data
Wrapper which parses various datasets from different domains
and with different structure and formats (CSV, XML, JSON)
and fields (latitude, longitude, id, image, location etc.) and
produce a unified output in XML format.Table II shows the
various data-sets fields used for traffic cameras in a few cities
(Toronto, Ottawa and Surrey).
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13
(a) (b)
(c) (d)
Fig. 2: Different Formats of same data-set (Traffic Camera). (a) CSV format for the city of Toronto. (b) XML format for city
of Ottawa. (c) JSON format for city of Surrey. (d) the combined output in XML format.
TABLE II: Data Wrapper Fields
City Name Data-set Format Parameters
Surrey Traffic Camera JSON Location, Image, Rotation, Longitude, Latitude
Ottawa Traffic Camera XML Id, LocationDesc, Longitude, Latitude
Toronto Traffic Camera CSV Camera Number, Latitude, Longitude, Main Road, Cross
Street, Traffic Image, Reference, Static Image
VI. CO NC LU SI ON
In this paper, the current status of seven Canadian cities have
been depicted with respect to open data. It closely represents
the open data-sets and open data tools in those cities. One
of the biggest advantage of this data is to use it for making
applications which keeps its users up-to-date for all activities
within cities and also in their neighbourhood. This data helps
to improve the lifestyle of citizens as there is feedback column
on each city’s websites to get reviews of citizens for data and
also to know about needs and ideas of citizens to improve the
data presentation. Thus it intensifies citizen engagement.
Furthermore, the research on open data of seven cities
is quite complex. Users are inundated with this huge data.
As different cities have different data-sets and further which
are in various formats. Therefore to study this huge data on
one single platform is very tricky because of diversified data
collected from different cities. This is an open challenge for
researchers and for cities’ authorities as to bring all data-
sets in single format is the first thing that researchers and
analysts have to build out and to work on the same data-sets,
the same formats are the biggest tasks for cities. Thus cities
have made this data to be opened for researchers, analysts, IT
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14
companies to work on it and make useful tools for the city’s
social, cultural betterment and governmental, technological
development.
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Open Data initiatives are increasingly considered as defining elements of emerging smart cities. However, few studies have attempted to provide a better understanding of the nature of this convergence and the impact on both domains. This paper presents findings from a detailed study of 18 open data initiatives across five smart cities – Barcelona, Chicago, Manchester, Amsterdam, and Helsinki. Specifically, the study sought to understand how open data initiatives are shaped by the different smart cities contexts and concomitantly what kinds of innovations are enabled by open data in these cities. The findings highlight the specific impacts of open data innovation on the different smart cities domains, governance of the cities, and the nature of datasets available in the open data ecosystem.
Article
Recently, the big data emerged as a hot topic because of the tremendous growth of the information and communication technology. One of the highly anticipated key contributors of the big data in the future networks is the distributed wireless sensor networks (WSNs). Although the data generated by an individual sensor may not appear to be significant, the overall data generated across numerous sensors in the densely distributed WSNs can produce a significant portion of the big data. Energy-efficient big data gathering in the densely distributed sensor networks is, therefore, a challenging research area. One of the most effective solutions to address this challenge is to utilize the sink node's mobility to facilitate the data gathering. While this technique can reduce energy consumption of the sensor nodes, the use of mobile sink presents additional challenges such as determining the sink node's trajectory and cluster formation prior to data collection. In this paper, we propose a new mobile sink routing and data gathering method through network clustering based on modified expectation-maximization technique. In addition, we derive an optimal number of clusters to minimize the energy consumption. The effectiveness of our proposal is verified through numerical results.
Article
As a prominent subcategory of cyber-physical systems, mobile cyber-physical systems could take advantage of widely used mobile devices, such as smartphones, as a convenient and economical platform that facilitates sophisticated and ubiquitous mobile sensing applications between humans and the surrounding physical world. This paper presents Vita, a novel mobile cyber-physical system for crowdsensing applications, which enables mobile users to perform mobile crowdsensing tasks in an efficient manner through mobile devices. Vita provides a flexible and universal architecture across mobile devices and cloud computing platforms by integrating the service-oriented architecture with resource optimization mechanism for crowdsensing, with extensive supports to application developers and end users. The customized platform of Vita enables intelligent deployments of tasks between humans in the physical world, and dynamic collaborations of services between mobile devices and cloud computing platform during run-time of mobile devices with service failure handling support. Our practical experiments show that Vita performs its tasks efficiently with a low computation and communication overhead on mobile devices, and eases the development of multiple mobile crowdsensing applications and services. In addition, we present a mobile crowdsensing application, Smart City, developed on Vita to demonstrate the functionalities and practical usage of Vita.