The rapid pace of urbanization in India demands that cities use data in a strategic manner to ensure sustainable and resilient development. Through the research conducted by the Urban Resilience Unit at National Institute of Urban A airs the authors suggest that cities need support with decision-making and using data e ectively and highlight the value of decision frameworks and data observatories to support Indian cities prepare for their urban future. This requires an intervention at the city level to facilitate the use of information for decision support and institutionalize urban data observatories. Through this paper, the authors highlight global case studies in which data observatories have facilitated smart decision making to reduce citywide and community risks to shocks and stresses. The authors also discuss the initiatives being taken at the national level by the Indian Government and propose options for implementing urban data observatories as a part of the ongoing national urban missions Copyright c 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
India is moving towards an urban future. According to the McKinsey Global Institute [SVD+10], urbanization rates in India suggest that cities will need to accommodate more than 200 million additional people by 2030 [Gov11]. Yet, the status of cities indicates that India needs to be better prepared to accommodate this expected growth. For example, several cities struggle to provide basic services to all their residents, and many are not able to maintain their existing natural resources [Sin03]. Currently, the de cit in urban infrastructure spending is more than Rs. 7,355 per capita (McKinsey Global Institute 2010), which has resulted in inadequate maintenance of existing infrastructure and gaps in new infrastructure development to keep up with the already growing urban population.
Furthermore, urban areas are already prone to a variety of acute shocks and stresses like urban ooding, cyclones, heat waves, tra c jams, air pollution and disease outbreaks placing both citizens and infrastructure at risk. Based on the vulnerability maps recently launched by the Ministry of Housing and Urban A airs, India, more than 50% of smart and AMRUT cities are either at very high or high risk to hydro-meteorological hazards especially oods and cyclones [BMP19]. According to a recent study, without better planning and disaster preparedness, trends like climate change will cost the Indian economy Rs. 8.42 lakh crore by 2050 if emissions continue at their current rates [MBC+18]. In order to address the above challenges and plan for long-term development of urban areas cities should start making informed decisions and include environmental, social and economic risks in both design, construction and management of their public infrastructures and services. 2
Around the world, planners, policy makers, civil society groups and other stakeholders use data to help them make informed decisions that have positive impacts on their city. Data can enable stakeholders to be better prepared against shocks and may also support them in allocating resources in preparation for growth and alleviate stresses within a city. For example, the city of Rio de Janeiro in Brazil has worked with IBM to set up a data operations center to mitigate the impacts of shocks like landslides and oods in informal settlements. The operations center collects and visualizes data from government agencies to map vulnerable areas. Then, by integrating weather forecasts, the government has been able to issue warnings to citizens in risk prone areas about impending ooding and landslides. The operations center has also bridged the information gap during response and recovery, allowing response teams to coordinate to mitigate damages during shocks like a building collapse in time.
Other stakeholders are also able to use data to create a positive impact: researchers at Newcastle University are collating data from more than 100 sensors across the city of Newcastle, UK to understand urban ood management and the impact of extreme weather events. They have set up a Newcastle Observatory to visualize ooding and drainage data to conduct research on these events. Another example of data informed decision-making is in Nairobi's largest informal settlement, Kibera. Civil society groups worked with resident volunteers to map the social infrastructure across the settlement. The community was able to collect information about the state of water, health and education infrastructure within the settlement. Elected representatives for the area have used the data collected about sanitation and education infrastructure to allocate nances for more latrines and hire more teachers in the local schools [Hag17].
This multistakeholder approach for using data to alleviate shocks and stresses has also been implemented globally through the 100 Resilient Cities (100RC) program to build resilience in 100 cities. Across its network of cities, 100 RC has enabled the development of more than 80 resilience strategies with more than 3,000 initiatives to push the resilience agenda forward [10019]. These strategies and projects were conceived with the support of governments, businesses and local communities and they utilized a wealth of city data that these groups provided. For example, through mapping of social assets like schools, Paris was able to determine that schoolyards represented more than 70 hectares of paved surface in the city. As the city has experienced dangerous heatwaves in the past, city o cials, with community support, were able to use this information to allocate funds and resources to greening these spaces to strategically cool the city and provide public play spaces for the community [10019].
To manage the impacts of urban growth and safeguard city investments from physical, economic, climate and social risks, there is an urgent need for cities to adopt data informed approach to planning. The above examples demonstrate the value addition of using data strategically with a multi-stakeholder and multisectoral approach. The stakeholders in these cities were able to use the data to enable them to make e ective decisions about resource allocation, infrastructure projects, and disaster risk reduction. These decisions ultimately mitigate the impact of shocks and ease stresses, building the resilience of cities. 3
Indian cities require risk-informed investments and evidence-based planning. Unfortunately, city managers are not using data e ectively to aid their decisionmaking due to several constraints. Firstly, policy makers do not have access to data that they need. Secondly, di erent departments collect urban data across city, state and national government agencies using di erent methodologies, at di erent resolutions and at di erent time periods. Thirdly, data are also not collected a timely manner to inform day-to-day decision-making [UNH15].
For example, in Delhi, there are around 18 separate agencies spanning across city, state and national governments that are responsible for various aspects of environmental management of the city and its surrounding. Data collected by one department is usually not made available to another department easily, thereby leading to uninformed decision making. There are also limitations with tools available to assist decision makers with visualization of data. Without visualizations, policy makers may not be able to recognize spatial and temporal trends, resulting in inaction or delayed action [UNH15]. The fragmented data landscape and lack of adequate analytical tools also makes it even more challenging to identify cross-sectoral linkages. 4
The importance of data driven governance as an opportunity to create a more sustainable and resilient urban future has been actualized through both national initiatives (Smart Cities Mission) and international agendas (Sustainable Development Goals). The Ministry of Housing and Urban A airs (MoHUA), Government of India will be launching \DataSmart Cities" initiative to help 100 Smart Cities promote data for better governance and innovation. The initiative aims to develop applications which can guide Smart Cities in achieving data driven governance through data management, information sharing and knowledge exchange. This will lead to increased transparency in governance and foster innovation within service provision [Gov18].
As a part of this proposed initiative, City Data O cers (CDOs) will be provided to each city to help them establish a City Data Alliance, which will include relevant stakeholders (including, government, business, academia, citizens and NGOs). In addition, the CDO will work with the stakeholders in managing and maintaining their data in compliance with the National Data Sharing and Access Policy. The city government and their stakeholders will be able to choose to make either parts or all of their data available on a city speci c open government data portal using an India Urban Data Exchange (IUDX) platform. This is expected to \facilitate easy and e cient exchange" of information across various departments and institutions for their respective decision making purposes [Gov18].
Cities are also working on benchmarking themselves against targets set by international agendas like the SDGs and national indices like the Ease of Living Index. The SDG targets and indicators help monitor a city or country's progress in achieving sustainable development [Nit18]. Similar to SDG targets, the MoHUA (Government of India) has released The Ease of Living Index as a metric for Indian cities to assess how liveable they are. Cities evaluate their rating based on a series of 78 indicators measuring institutional, social, economic and physical characteristics on an annual basis. However, cities need to take a holistic approach to data collection and visualization. The data being collected must be functional for decision-making purposes to help improve the condition of the city. For example, many of the SDG indicators are meant to be monitored at a state or country level and the indicators still need to be contextualized for the city scale. The data for each indicator also needs to be available at the city scale. There are also challenges related to the frequency of data collection: much of the data is being collected at a frequency that is too low for day-today decision-making. Di erent information needs to be captured at useful time frames to enable its utility. Finally, to understand how the data can be used for decision-making, cities need to move beyond just collecting the data and need tools for spatial and temporal trend visualizations. This will allow city managers to use the data for informed decision making. 5
The Urban Resilience Unit at the National Institute of Urban A airs is an initiative supported by 100 Resilient Cities { pioneered by the Rockefeller Foundation. The Unit is working to mainstream urban resilience in Indian cities through data informed decision making by developing decision frameworks and purposing that Indian cities implement urban data observatories.
An urban data observatory is a platform that serves as a repository for accurate and frequently updated city speci c spatial and non-spatial data that will serve as a decision support system. The observatory adopts a multistakeholder and multi-sectoral approach, collating and visualizing data from various government and non-government agencies. This will help eradicate data gaps that currently plague decision makers. Decisions makers will be able to use the data observatory to visualize trends, analyses data and build scenarios to help stakeholders solve complex urban problems. 5.1
The data observatory will eradicate the challenges that decision-makers currently face in utilizing data to take action and solve challenges. The Urban Resilience Unit organized an expert consultation with decision makers from local governments, businesses, research and educational institutions and civil society to understand in more detail, the challenges they face in using data for e ective decisionmaking.
One of the primary challenge that plagues decision makers today is the ambiguity around protocols regarding the quality, collection, sharing, storage, format and ownership of data. Many of these factors constrain stakeholders from using data as decision support and for research purposes. Currently, no formal mechanisms exist to enable data sharing between data producers and users and many government agencies are reluctant to share data with the public. There are also no standards for sharing data to ensure quality control or monitor the usage of sensitive information. Finally, data sets that are currently available and veri ed are often accessible in an impractical format or at frequencies that are not suitable for analysis: for example, tabular data is uploaded as a jpeg or pdf, and therefore cannot be used in database management systems for analysis.
The data observatory addresses these challenges as a common platform for data, which is accessible for all stakeholder groups. The observatory operations will require an operational team that will be led by a Chief Data O cer and will include of data scientists, IT programmers and urban planners. The team will have three main roles: center operations + maintenance, data processing and innovation. Through these roles, the team will ensure quality control of data as it is uploaded to the observatory. The team will be responsible for cleaning and ltering the data for errors, ensuring the formats are compatible for spatial and temporal analysis and maintaining the metadata of the data sets. The team will also be responsible for ensuring sensitive information is not made public and that data will be compliant with data protection laws, including recommendations in the \A Free and Fair Digital Economy: Protecting Privacy, Empowering Indian" report by Committee of Experts under the Chairmanship of Justice B.N. Srikrishna.
The team will also be responsible for conducting trainings and data sensitization workshops with stakeholders, to raise awareness about the importance and value of an open data culture. This would include workshops around methodologies to ensure transparency regarding data sets on the observatory. 5.2
Another key nding from the consultation with stakeholders around data and decision-making was the need for a multi-stakeholder and collaborative approach to data. Stakeholders are critical to the success of the data observatory. Much of the urban data generated comes from citywide stakeholders on a daily basis but currently no formal platform for such multi-stakeholder engagement exists. To ensure that decision makers can make the most e ective decisions, it will be necessary that all stakeholders are associated with the observatory.
As seen in 1, the observatory will be a platform that will have a symbiotic relationship with its stakeholders: their contribution in terms of city data will allow decision makers and government agencies to ll data gaps and understand the landscape holistically. Also, stakeholders will have access to data, which they can use to inform their decisions: Citizens will bene t from more access to information about service availability, transportation and disaster risk, for example. This will lead to more accountability in government provision of services. Furthermore, the observatory increases the opportunities for public engagement between government and citizens.
City governments, as one of the main decision makers, will greatly bene t from the collation of data across sources. They will have tools to enable the creation of evidence-based policy making and decision support for complex problems.
The business community stands to bene t from the data observatory, as they will have a better understanding of city services, energy provision, infrastructure, environmental data and disaster risk. These topics can be used in business planning and for starting conversations with the government to create a more opportune business environment.
Students, professors and researchers stand to bene t from a variety of data about the city that they may previously have been unable to access. Furthermore, educational institutions are a source of rich data collected during studios and theses that can be integrated into the platform 5.3
The nal challenge that stakeholders currently face in using data is maximizing the potential of data for decision-making. Decision makers at the workshop were unable to de ne decision questions and mostly discussed the use of data for diagnostic purposes but not decision making.
The Urban Resilience Unit is developing a decision framework to help cities make decisions to reduce the risks of shocks and stresses. The decision framework is multi-sectoral and takes a holistic approach to challenges within cities. The Unit has conducted research on more than 600 indicators to develop this framework. As seen in 2, it consists of ve broad themes: Basic Services, Infrastructure, Social Infrastructure, Natural Systems and City Planning Systems. Each theme is broken down into sub themes, which have a series of indicators and decision questions to help measure a city's performance across time and space.
This framework will support cities in understanding how to operationalize data while using the data observatory. This framework has been developed considering the cross linkages between themes and sub themes. For example, if the city has adequate infrastructure catering to improved economic opportunity, it is likely that is has strong urban planning and implementation systems. Therefore, as cities use the framework and identify areas for improvement, strategic decisions can enhance the strength of multiple systems. It is designed to enable decision makers prioritize city needs and develop targeted and riskinformed policies. As cities use the framework, they can identify areas for resilient infrastructure development, improvement in service provision, alleviation of stresses and mitigation of shocks.
A series of use cases are being developed for the 17 sub themes to highlight how the framework will support decision-making and can help stakeholders answer key questions about their city. 1 to 5 are below and highlight examples of use cases for the themes of water, health, buildings and real estate, environment, and transportation.
Along with developing use cases, the Urban Resilience Unit proposes to work with cities to identify the speci c datasets which are required to help answer take these decisions and address challenges. This would include identifying the source agency, the scale/resolution at which the data is required, the frequency the data must be collected, and visualization and models that are necessary to utilize data e ectively for problem solving: 6
Data will support cities to embody the characteristics of resilience to enable them to cope with a variety of shocks and stresses. These characteristics of resilience are that systems are re ective, resourceful, inclusive, integrated, robust, redundant and exible [ARU14]. Used e ectively, data will ensure that city systems are re ective, so that decisions are made based upon holistic understanding of the needs and gaps they are meant to solve the urban challenges. Data can also help a city be resourceful, helping planners understand how assets and resources are distributed spatially and how they can be used more e ciently and for di erent needs. Data can help a city be inclusive and integrated if it is collected from di erent stakeholders. It can also help devolve governance and empower stakeholders to take informed decisions within their own institutions, reducing the burden on ULBs. Data can also help cities incorporate the risk of priority shocks, enabling them to design infrastructure and networks with spare capacity and speci c designs to o set disasters, helping the city infrastructure and services be both robust and redundant.
Indian cities must act now to prepare for its urban future and the use of data for informed decision making will be imperative in ensuring that cities grow in a sustainable manner with the ability to thrive despite social, economic, physical and climatic risks. The value of data can only be unlocked when it is used holistically and with the correct decision support and visualization tools so that decisions a ecting the city can be taken based on evidence. The Urban Resilience Unit's strategy to develop data observatories is an initial e ort to support cities in both collecting, visualizing and analyzing data through a holistic, multi-sectoral and multistakeholder approach. The decision framework, based on indicators and research about on the ground challenges cities face, will support stakeholders in using data to take informed decisions, enabling transformative change and creating a resilient urban future. 6.0.1
The authors would like to thank the 100 Resilient Cities program { pioneered by the Rockefeller Foundation for their support for the Urban Resilience Unit. The authors would also like to acknowledge the National Institute of Urban A airs with gratitude for their supporting this initiative. [10019] 100RC. Resilient Cities, Resilient Lives, Learning from the 100 Resilient Cities Network, 2019. [ARU14] ARUP and The Rockefeller Foundation. City resilience framework, 2014. City Resilience Index. [BMP19] BMPTC. Vulnerability atlas of india. Technical report, Building Materials Technology Promotion Council, 2019.
Government of India. Census of india: Executive summary - primary census abstract. Technical report, O ce of the Registrar General & Census Commissioner, Ministry of Home A airs, 2011.
Government of India. Smartcity as platform data exchange framework for indian smart cities. Technical report, Smart City Mission Transform Nation - Ministry of Housing and Urban A airs, 2018. Erica Hagen. Open mapping from the ground up: learning from Map Kibera, Making All Voices Count Research Report. Technical report, Brighton: Institute of Development Studies, 2017. [Gov11] [Gov18] [Hag17] [Nit18] [Sin03] [MBC+18] Muthukumara Mani, Sushenjit Bandyopadhyay, Shun Chonabayashi, Anil Markandya, and Thomas Mosier. South Asia's hotspots: The impact of temperature and precipitation changes on living standards. The World Bank, jul 2018.
Niti Ayog-Government of India. Sdg india index: Baseline report. Technical report, NITI Aayog, 2018.
Shaheen Singhal. India 2025 - Environment.
Planning Commission of India, 2003.
Technical report, [SVD+10] Shirish Sankhe, Ireena Vittal, Richard Dobbs, Ajit Mohan, Ankur Gulati, Jonathan Ablett, Shishir Gupta, Alex Kim, Sudipto Paul, Aditya Sanghvi, and Gurpreet Sethy. India's urban awakening: Building inclusive cities, sustainaing economic growth. McKinsey Global Institute, 2010. [UNH15] UNHabitat. A guide to setting up an urban observatory. Guidelines, 2015.
Citizens are facing Citizens upload water shortages their water frequently and complaints through in some slums, a single window municipal water platform to the is not available. data observatory.
This has resulted The complaints in poor spending are monitored more on accessing regularly by urban water from tankers. managers and Evidence of poor e ective actions in health outcomes 1c0ritical areas are exists in these areas planned in a timely due improper water manner.
storage. Mobile based reporting by the anganwadi workers to the observatories provide hourly report to public health o cers.
Also, they can map areas that have water logging and provide advisories to select communities Health data about incidences and causal factors are shared by health care providers to observatories inturn they get predictions and projections to help plan for any adversities.
The observatory can be used to promote materials that help raise awareness about water and vector borne diseases only in selected areas thereby maximizing their communication budget and minimizing the impact
The city is able to increase health and sanitation services especially for poor and vulnerable. Fogging is also carried out only in strategic locations on a priority basis. There is enough storage for required medical supplies and treatment material. Hospitals can also plan and sometimes prevent the diseases before they turn to an epidemic.
There are fewer occurrences of water borne disease in the city, especially in slums. When disease does occur, citizens get checked and treated in time. Planners in the city want to rehabilitate residents living in informal settlements in environmentally sensitive areas insitu. However, they do not have strategic information on where to locate to have mutuall bene t the stakeholders.
Many houses are built as a ordable housing in the city are unoccupied or rented to tenants who were not the original benefactors.
Many migrants move to the city without an understanding of which areas are at risk to shocks like landslide leading to high impact in case of extreme events
NGOs working in slum communities regularly upload demographic characteristics along with settlement status and user need to observatories which is used to determine the housing needs for rehabilitation.
NGOs and educational institution reports on the staus to the observatory for policy makers to understand the challenges and device contextual solutions The observatory displays risk maps for relevant disasters and shocks in the city. New migrants are able to identify areas that are prone to disasters and are able to select housing options in safer areas.
The city is able to provide families living in informal settlements within proximity to their livlihood and their critical needs are addressed within the new housing Policy makers are able to rectify
aws in older designs to ensure new settlements meet the socioeconomic needs of the residents.
Residents incur fewer losses from disasters as relatively few people live in riskprone areas. Lives and investments are secured.
The city is prone to ooding during high rainfall events.
Communities do not know where it will ood and are therefore underprepared. In urban heat islands that have space for greening, the city has taken e orts to plant trees to reduce UHI in the area. Motor vehicle By observing collisions are a camera and sensor leading cause for data, tra c and injuries and loss pedestrian ows of life. The city are mapped and is responsible for visualized on the providing safe data observatory. streets for its These are overlaid residents and with data regarding o cers want to locations of tra c reduce the number accidents and of accidents. timings of the accidents to create design interventions for risk prone intersections and streets.
Businesses su er On the data loss of productivity observatory, due to high businesses can congestion in the query and city. Their logistics determine which and shipping streets are the are also delayed most congested considerably. at di erent times. They use this data to change the timings of their shipments and logistics as well as plan alternative routes.
In many With the data communities, observatory, NGOs public and researchers transportation can see the public access may be transportation low and may not routes in the city be routed for the to determine the desired destinations unserviced areas. of the community. 1N4GOs specialized Citizens thus in transportation rely on more help design e cient expensive or time- routes to the consuming modes transportation of transportation. authority. The city has identi ed the top 10 accident prone areas in the city and has been able to redesign the streets and intersections to reduce the number of accidents and collisions.
Business productivity improves and costs of shipping and logistics are reduced.
The city makes revisions to the bus routes to reach the maximum number of users.