Implementation of the Urban Observatory and the Role of Artificial Intelligence

This study focuses on the implementation of the urban observatory and the role of artificial intelligence (AI) in urban development and management. It explores how AI technology can help reduce the impact of urbanization and improve city planning and management. The study discusses the Indian Urban Observatory as a case study and highlights the importance of involving all stakeholders in the development and management of cities. It also examines the use of modern technologies, such as AI and data analytics, in improving resource utilization and data management in smart cities. The study aims to leverage AI technology to understand city resources and systems and reduce the impact of urbanization.

Implementation of the urban observatory and the role of artificial intelligence
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Table of Contents
1. Implementation of the urban observatory and the role of artificial intelligence..........................3
2. Overview of study..............................................................................................................................3
3. Project rationale................................................................................................................................4
4. Aim of study.......................................................................................................................................4
5. Objectives of study.............................................................................................................................4
6. Relevant literature.............................................................................................................................5
International perspective on urban observatory.................................................................................5
Artificial intelligence and urban cities transformation.......................................................................6
7. Research design and methodology....................................................................................................7
8. Ethical considerations.......................................................................................................................9
9. Risks considerations........................................................................................................................10
10. Assumed outcomes and the study implications..........................................................................10
11. Bibliography.................................................................................................................................11
12. Project Timeline...........................................................................................................................13

1. Implementation of the urban observatory and the role of artificial intelligence
2. Overview of the study
Urban development and management have been on focus for the past century. The
number of city dwellers has been on increase and by 2014, it was estimated to hit 54% with a
projection of an additional 2.5 billion by the year 2050. Due to inefficient urban planning and
management, there has been an increase in the unsustainable urban settlement which hinders
people from advancing either socially, economically or personally. In this regard, The Indian
Urban Observatory would be at the centre of the discussion as an important aspect of the recently
launched Data Smart Cities strategy (Pettit, Lieske & Leao 2016, pp. 176). The focus would be
on making use of the data in the development and management of cities. The observatory would
acknowledge the importance of involving all stakeholders such as citizens, state government
entities, academia and entire urbanization industry. The study would address the application of
smart and modern technologies such as artificial intelligence to change the processes involved in
the management of the cities. It is expected that the use of modern technologies would help
urban management to make use of available resources effectively and improve data management
and sharing capacity across various systems (Farago 2019, pp. 1407). The data required for
management of the Smart Cities by The Indian Observatory would be collected from several
devices over the Internet of Things (IoT). The data would be analyzed to make it useful to all
stakeholders including city planners. To automate the process, Cisco would offer its
infrastructure such as WebEx which would serve as an observatory platform with artificial
intelligence and data analytics capability. The study and the Indian observatory case study is
important because it addresses global phenomenon which has remains a challenge in the modern
urbanization processes (Ng 2012, pp. 586).

3. Project rationale
The study has been motivated by the need to solve modern urbanization challenges.
There has been an increase in the number of people living in the cities and state governments
have not been considering the impacts of urbanization (Mongeau 2012, pp. 12). It is important
for state governments to analyze the effects that have been created by urbanization and come up
with strategies of solving such issues. Since different cities have various challenges, Artificial
Intelligence (AI) technology would be applicable in resolving urbanization management and
planning challenges. The study would be based on the perennial urbanization challenges dating
back 1971 when the idea of “making the city observable” was first conceived (Nandy & Nandy
2016, pp. 15-16). Since the idea was conceived the urbanization issues have become more
complex due to an increase in urban population. The city observatory idea would be steered by
the presence of technology which would be used to collect data from different sources, analyze
and present the result to the relevant authorities for decision making. Through the use of AI
technology, it would be possible to have real-time visualization of the modern cities.
4. Aim of the study
To leverage the use of Artificial Intelligence (AI) technology to understand city resources
and systems to reduce the impact of urbanization.
5. Objectives of the study
1. To understand the nature of the activities that city dwellers do to earn their living.
2. Identify the main form of the transport system that the majority of city dwellers use.
3. Understand the nature and availability of the systems that should be made available to
make the life of city residents better.

6. Relevant literature
International perspective on the urban observatory
In the current urbanization and digitization process, Urban Observatory would be driven
through big-data. In the case of the urban planners and developers, location of sites and nearest
stores would only require the use of the observatory system (Ottenburger & Ufer 2019, pp. 64-
65). It is evident that there are no cities that would have a similar scale of mapping scale due to
variation in sizes, functionalities and business processes. The urban development and
management should be focused on urban design, planning, development and management. To
make the management of the cities effective, artificial intelligence through the utilization of big
data collected over the Internet of Things presents stakeholders with a solution to the
urbanization challenges. The observatory needs to be structured in such a way that big data can
be analyzed and visualized over the platforms such as geospatial. On the same note, in 1977
United Nations developed an urban observatory initiative at Istanbul. Other examples of the
observatory cities are Global urban observatory network and the city dashboard of London
(Sintoris et al. 2013, pp. 48). On the recent past, the urban observatory initiative was established
in India. To actualize its implementation, the national institute of urban affairs formed some
associations which are mandated with the responsibility of setting up observatories in India cities
such as Mumbai and Chandigarh. On the same note, the centre for Study of Science, Technology
and Policy (CSTEP) has established links with governments such as Karnataka to create a full-
pledge concept on urban observatory for the city. The flagship project is expected to cover
several cities under Smart Cities Mission and AMRUT. In this case, urban observatories are
expected to serve as the key pillars for management of the smart cities which need to collect,
analyze and visualize the data (Skouby et al. 2014, pp. 8-9).

Artificial intelligence and urban cities transformation
It is expected that by the year 2030, artificial intelligence technology would have
transformed some of the biggest urban cities in North America. Such achievements are expected
to be witnessed in the cases of self-driving cars, robotic delivery of packages and the use of
drones in the surveillance (Kim & Wang 2014, pp. 487). Implementation of artificial intelligence
in the management of cities has been thought of as fiction but it has come to be a real solution in
urbanization challenges. Through the implementation of Artificial Intelligence, state
governments are able to utilize data mining and machine learning to effectively control lead
poisoning and distribute foods to needy communities. On the same note, the use of drones in the
cities are being used to monitor daily activities and ease of doing business. By collecting such
data and analyzing it, city planners and management is able to plan for social amenities and
public services required by city dwellers to make life more comfortable. The state government
focus remains on developing the global economy as well as propagate inclusive income
distribution. It is evident that data has power in policy formulation and decision making. In this
regard, data collected from the different sources would be utilized to formulate policies which
would ease the process of managing urban activities. The transportation sector has been lagging
behind in adopting AI but Uber as the pioneer in the industry has started to adopt AI with aim of
changing transportation within the cities (Nathan & Reddy 2013, pp. 301-302). The main goal
would be to accelerate the speed at which the government would initiate development which
makes it simple to conduct business within the cities.
Security in the urban cities has been a thorny issue that management of the cities has been
looking for a long lasting solution. With the adoption of urban observatory technology in smart
cities, security concerns would be substantially reduced (Djeffal 2019, pp. 188). Drones and
CCTV cameras installed in different parts of the city can be used to monitor security aspects in

the cities in a bid to reduce crime. Some of the powerful tools such as CompStat has been used
by the New York Police Department has been used to increase predictive policing. On the same
note, other similar tools have been used in predictive analytics to increase security surveillance.
A good example can be deduced from California-based Armorway which is commonly known as
Avatar Intelligence (Majale 2003, pp. 16). The Avatar has been using AI in the game theory with
the aim of predicting the terrorist possibility of hitting the target. Notably, Coast Guard in New
York and Boston has been using Avatar software in the management of port security. It runs on
the data collected from passengers and load number of traffic changes into the system making it
difficult for a terrorist to plan when there would be security lapse. To increase security
surveillance, the transport sector has been aiming to reformulate security operations in airports
worldwide (Sanchez-Puchol, Pastor-Collado & Borrell 2018, pp. 113). Instead of using manual
scanning, airports are encouraged to make use of AI in the scanning of all passengers. Finally, to
monitor security effectiveness in the cities, drones have made it easier for security entities to
collect data that forms part of the big data. The collected data is analyzed and presented to
security bodies for effective decision making. With such data, the number of police surveillance
can be reduced from areas where crime is relatively low and deployment is done on high-end
crime areas. In this case, there would be proper deployment and utilization of police personnel.
7. Research design and methodology
The study would make use of descriptive research because it would be suitable to collect
and analyze data on cities planning and management. The collected data would be presented
through the use of different data analytics tools to make it simple for use by management. The
descriptive design would make it possible to evaluate the context of urban management with
current data (Rahi 2017, pp. 2-3). Surveys would be used to collect data on the different cities
across the globe that have implemented AI in the management of its city activities. The data

would be used to assess the success of using AI in the urban observatory. Quantitative methods
would be used in the study to help collect numerical data on the number of cities that have
adopted smart cities (Almalki 2016, pp. 288). The collected data would be presented in different
formats such as graphs to make it usable to the decision makers. Table tabulation would be used
to consolidate data from survey and questionnaires. After data has been consolidated, the
analysis would be done and visualization to the relevant authorities would be done through the
use of graphs and charts. Besides, questionnaire, interview and observation techniques would be
used in the collection of data. The process would mainly entail visiting relevant city employees
and state government officials to observe aspects such as security level, transport traffic
management, tax collection and business control processes within the city. Interviews would be
done with both city dwellers and government entities to assess the improvements witnessed after
the adoption of the urban observatory (Rahi 2017, pp. 4). Questionnaires would be sent to some
of the offices which are not easy to get a dedicated respondent to interview or observe as daily
activities are being done.
Every study is coupled with some challenges that may cripple project actualization if not
well managed. Some of the anticipated challenges during the study are; lack of the right
respondent to provide the right information required for decision making in urban observatory
decision making. Some of the organization such as security entities does not allow their
employees to communicate or share any information on behalf of the entity. With such
organizations, it would be difficult to get questionnaires to send back. Next, the funding required
to conduct the study research has been a challenge in most of the projects and this cannot be an
exception. It is highly expected that project funding would be a challenge. Since urban
observatory is not common in current cities’ design, the process is expected to be expensive due

travel requirement (Laguzova & Molkova, 2018, pp. 152). Thirdly, there are very few cities that
have implemented AI in urban planning and management and this makes it challenging to get
reliable resource persons for the study. Additionally, urban observatory requires data collection
from different sources including the Internet of Things (IoT) devices. Such devices are very
expensive and require technical personnel to run and manage. Due to the limitation on project
funding, it would be challenging to get the right technical manpower to set up the IoT and
manage required infrastructure (Das, Dey, Pal & Roy 2015, pp. 33). Finally, there would be
security concerns on both the hardware and software. Some of these devices need to be placed on
public places where their security cannot be guaranteed. On the same note, urban observatory
makes use of data which is prone to cyber-attack. In this regard, it is expected that the loss of
sensitive data to unauthorized individuals might happen.
8. Ethical considerations
There are several ethical issues that should be considered during and after the study. Data
is very vital and should be protected from access by third parties. Privacy of data should be
guaranteed during and after its collection. The data collected should not be associated with any
individual responded. During data collection, respondent personal details should not be collected
to conceal the anonymity of the information collected (Khatib et al. 2012, pp. 86). Next, it is
expected that adopting AI in urban observatory would create unemployment. Despite this
perception, technology adoption in the urban observatory is expected to create eliminate
repetitive jobs. On return, the human being is deployed on complex activities that cannot be
automated. Consider automating cars for self-drive, many people might lose jobs but the choice
of reducing the number of accidents should be the main priority. Thirdly, the inequality in wealth
distribution created by AI dominant companies remains an issue of concern (Rabah 2018, pp. 3-
4). It becomes a challenge for the state government to guarantee a fair distribution of income

generated through the use of AI. In 2014, companies which were running the business through
AI with less number of employees generated more revenue compared to those with a higher
number of employee. Finally, their security concerns that should be addressed because AI can be
used adversely if not well controlled. Security concerns should be both on physical and cyber-
attack related. In this regard, it would be important for urban observatory stakeholder to focus on
ethical issues for effective planning and management of smart cities.
9. Risks considerations
Project implementation is coupled with several risks which should be well maintained to
prevent project failure. In this case, some risks that seem to be more prone to affect urban
observatory implementation are;
Sr No Anticipated risks Probability of
occurrence
Impact
1 Human-related errors 0.8 High
2 Unmanageable project schedule
and estimation of budget
0.5 Medium
3 Inadequate personnel capacity 0.6 High
4 Quality and security of the
software
0.3 Low
5 Project requirements change/scope 0.9 High
6 Sub-standard IT infrastructure and
security issues
0.7 High
10. Assumed outcomes and the study implications
Urban planning and management should be an area of concern because more people are
adopting urban residents. It is expected that by the year 2050, half of the world population would
be living in the cities. With this concern, it is important for state governments to start coming up

with the best scenario to address urbanization challenges by monitoring its processes through the
use of Artificial Intelligence (Eiben & Smith 2016, pp. 6). The study would unveil new strategies
required in the modern urban observatory processes. It would be possible for city management to
understand activities that earn a living to city dwellers and generate revenue to the city. This
would result to increase in revenue collection as well as the formulation of policies that would
make it possible to improve cities living standards. Finally, it would help state governments to
secure the cities by implementing real-time security monitoring.
11. Bibliography
Almalki, S., 2016. Integrating Quantitative and Qualitative Data in Mixed Methods Research--
Challenges and Benefits. Journal of Education and Learning, 5(3), pp.288-296.
Das, S., Dey, A., Pal, A. and Roy, N., 2015. Applications of artificial intelligence in machine
learning: review and prospect. International Journal of Computer Applications, 115(9), pp. 31-
39.
Djeffal, C., 2019. Sustainable Development of Artificial Intelligence (SAID). Djeffal C:
Sustainable Development of Artificial Intelligence (SAID), 4, pp.186-192.
Eiben, A.E. and Smith, J.E., 2016. Towards the evolution of things. ACM SIGEVOlution, 8(3),
pp.3-6.
Farago, P., 2019. A conceptual model for smart city evaluation: attributes and rules. Economic
and Social Development: Book of Proceedings, pp.1407-1415.
Khatib, T., Mohamed, A., Mahmoud, M. and Sopian, K., 2012. Estimating global solar energy
using a multilayer perceptron artificial neural network. International journal of energy, 6(1),
pp.82-87.
Kim, J.S. and Wang, X., 2014, May. Rethinking the Strategic Dimensions of Smart Cities in
China’s Industrial Park Developments: the Experience of Suzhou Industrial Park, Suzhou, China.
In REAL CORP 2014–PLAN IT SMART! Clever Solutions for Smart Cities. Proceedings of 19th
International Conference on Urban Planning, Regional Development and Information Society,
pp. 487-496.
Laguzova, A.A. and Molkova, A.S., 2018. The AI impact on the strategy of advanced industrial
companies to 2025. Russian economy: goals, challenges and achievements, p.152.
Majale, M.I.C.H.A.E.L., 2003. An integrated approach to urban housing development: has a case
been made. In Urban Research Symposium (pp. 15-17).

Mongeau-s, S., 2012. An Integrated Platform for Smart City Design: Structured Market-Based
Incentive Architecture Design for Sustainable ‘System of Systems’ Supply Chain Orchestration
1(1), pp. 1-29.
Nandy, U.K. and Nandy, A., 2016. Materials, Tools & Technologies to Enhance Efficiency &
the Green-Quotient of Smart Buildings. International Journal of Engineering Research 1(5), pp:
14-17
Nathan, H.S.K. and Reddy, B., 2013. Urban transport sustainability indicators- application of
multi-view black-box (MVBB) framework. International Journal of Environment and
Sustainable Development, 12(3), pp.285-312.
Ng, E., 2012. Towards planning and practical understanding of the need for meteorological and
climatic information in the design of high‐density cities: A case‐based study of Hong
Kong. International Journal of Climatology, 32(4), pp.582-598.
Ottenburger, S.S. and Ufer, U., 2019. Abstract Smart Space and Concrete Risks. In real CORP
2019–is this the real world? Perfect Smart Cities vs. Real Emotional Cities. Proceedings of 24th
International Conference on Urban Planning, Regional Development and Information Society,
pp. 63-67.
Pettit, C.J., Lieske, S.N. and Leao, S.Z., 2016. Big bicycle data processing: from personal data to
urban applications. ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information
Sciences, 3(2), pp. 173-178.
Rabah, K., 2018. The convergence of AI, IoT, big data and Blockchain: a review. The Lake
Institute Journal, 1(1), pp.1-18.
Rahi, S., 2017. Research design and methods: A systematic review of research paradigms,
sampling issues and instruments development. International Journal of Economics &
Management Sciences, 6(2), pp.1-5.
Sanchez-Puchol, F., Pastor-Collado, J.A. and Borrell, B., 2018. A Critical Review on Reference
Architectures and Models for Higher Education Institutions. Big data analytics, data mining and
computational intelligence 2018 theory and practice in modern computing, p.113.
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city: Location-based games for learning in smart cities. IxD&A, 16, pp.47-64.
Skouby, K.E., Lynggaard, P., Windekilde, I. and Henten, A., 2014. How IoT, AAI can contribute
to smart home and smart cities services: The role of innovation. 25th European Regional
Conference of the International Telecommunications Society (ITS), pp. 1-13.

12. Project Timeline
Time (Months)
Phases/ events
1-6 7-12 13-18 19-30 31-34 35-36
Project planning
Literature analysis and review
Data collection
Project implementation
Testing
Handover