Transforming cities into smart cities

Transcription

1 Transforming cities into smart cities Data Gaps and Opportunities Demetrios Sarantis 2018 National Workshop for Ethiopia

2 AGENDA 1 Smart cities: the context Transformation process Data gaps and opportunities Smart What kind city of challenges projects Transforming cities into smart cities: Opportunities, Challenges and Risks 2

3

4 How do we manage/govern a city with this dimension?

5 THE NEED FOR A SUSTAINABLE WORLD Transforming cities into smart cities: Opportunities, Challenges and Risks 5

6 Population in Billions INTERNATIONAL CONTEXT Trend: population growth 14 13,17 12,44 12 Median Upper Variation 11,18 10 Lower Variation 10,09 9, Growing 1.1% per year Lower 95 Lower 80 Median Upper 80 Upper World s population is projected to increase by one billion people over the next 12 years, reaching 8.6 billion in 2030, 9.8 billion in 2050 and 11.2 billion by Transforming cities into smart cities: Opportunities, Challenges and Risks 6

7 Population in Billions INTERNATIONAL CONTEXT Trend: population growth by region 6 Africa Asia Europe Latin America and the Caribbean Northern America Oceania In 2100 the main contributor for population growth will be Africa, with 3.4 times more inhabitants than now! TODAY: 60% Asia (4.5 billion) 17% Africa (1.3 billion) 10% Europe (742 million) 9% Latin America and the Caribbean (646 million) 6% Northern America (361 million) Oceania (41 million) TODAY: China (1.4 billion) and India (1.3 billion) comprising 19% and 18% of the total ,78 4,47 0,71 0,65 0,50 0,07 Transforming cities into smart cities: Opportunities, Challenges and Risks 7

8 Population in Billions INTERNATIONAL CONTEXT Trend: population growth by development level 12 More developed regions Less developed regions 10 9, Population growth will be concentrated in the less developed regions: 3.8+ billions in Africa and Asia 4 More developed regions will have approximately the same population i.e billions = 0.03 billions 2 1, Transforming cities into smart cities: Opportunities, Challenges and Risks 8

9 Population in Millions INTERNATIONAL CONTEXT Trend: population growth by income level 8 7 High-income countries Middle-income countries Low-income countries 7, , , Transforming cities into smart cities: Opportunities, Challenges and Risks 9

10 INTERNATIONAL CONTEXT Trend: the growth of cities Population living in cities grew 50% over the last 50 years! Today 55% of the overall world population resides in cities! Most urbanized regions: Northern America (82% of its population living in urban areas in 2018), Latin America and the Caribbean (81%), Europe (74 %) and Oceania (68%). Tokyo is the world s largest city (37 million inhabitants), followed by New Delhi (29 million), and Shanghai (26 million). Mexico City. Future In 2050 there will be billion people living in cities! (68%; 2 out of 3 people will live in cities) India, China and Nigeria will account for 35% of the growth between 2018 and 2050 Addis Ababa population is expected to grow to exceed 6.5 million residents. The annual growth rate of the city has been estimated in recent years to be 3.8% By 2030, the world could have 43 megacities (up from 31 today), most of them in developing countries Transforming cities into smart cities: Opportunities, Challenges and Risks 10

11 INTERNATIONAL CONTEXT The growth of cities Many countries will face challenges in meeting the needs of their growing urban populations, including for housing, transportation, energy systems and other infrastructure; as well as for employment and basic services such as education and health care (UN DESA) the need for more sustainable urban planning and public services the need for more sustainable and inclusive cities and communities the need for smarter cities!!! Transforming cities into smart cities: Opportunities, Challenges and Risks 11

12 How to transform cities into smart cities?

13 SMART CITIES AS A TRANSFORMATIVE PROCESS The development of smart cities is a continuous transformative process of building different types of capacities, e.g. infrastructural, technical, human, institutional, and others, in a city that contribute to improving quality of life of its residents, to achieving socio-economic development, and to protecting natural resources; conducted based on the stakeholder engagement and collaboration. (UNU-EGOV 2016) Source: Transforming cities into smart cities: Opportunities, Challenges and Risks 13

14 UNU-EGOV FRAMEWORK FOR THE SMART CITIES TRANSFORMATION PROCESS INPUTS TRANSFORMATION OUTCOMES Technologies Tools Approaches Stakeholders Governance Maturity Models Innovations Benefits CONTEXT Values Drivers Challenges Risks Region Transforming cities into smart cities: Opportunities, Challenges and Risks 14

15 10 FUNDAMENTAL STEPS FOR THE TRANSFORMATION Transforming cities into smart cities: Opportunities, Challenges and Risks 15

16 Data gaps and opportunities

17 Urban data sources Subjective Data Census Survey Objective Data Administration Data Machine generated Transforming cities into smart cities: Opportunities, Challenges and Risks 17

18 Measurement indicators for data based decision making Offer local administration a flexible tool designed to promote multi-sectoral engagement with the ability to choose indicators that are locally relevant and accessible Provide each sector the opportunity to see how they are achieving outcomes Guide local policy, decision making and resource allocation Allow communities to benchmark themselves against peers and identify best practices Transforming cities into smart cities: Opportunities, Challenges and Risks 18

19 Indicative Measurement Indicators(U.S. Department of Health and Human Services) Domain Subdomain Example Indicators Example Metrics Community Vitality Social Capital Residents who trust their neighbors Governance Neighborhood Resiliency connections Stakeholder engagement for developing regulations Public trust in government Open Data City % of adults who trust their neighbors Density of neighbored acquaintanceships Civic Engagement Registered Voters and percent who vote Public meeting attendance City % registered to vote City % registered voters who voted Social inclusiveness Residential mobility persons 1 year and older living in the same house as one year ago Transforming cities into smart cities: Opportunities, Challenges and Risks 19

20 Indicative Measurement Indicators(U.S. Department of Health and Human Services) Domain Subdomain Example Indicators Example Metrics Economy Income and wealth Persons living in poverty % People living in poverty/ Total community income Employment Unemployment rate % persons years of age formally employed or self-employed and earning a formal income Job Accessibility # of jobs within average commute times by skill level and quality Education Infrastructure & capacity Child care child care locations Teachers per students in public schools Ratio of students to teachers in regular education programs in public schools Environment Natural environment Air and water quality Median daily water consumption in cubic meters Neighbourhood characteristics Broadband cost and speed Transforming cities into smart cities: Opportunities, Challenges and Risks 20

21 Gaps in urban data Limited coverage exclude people who live outside traditional households replicate any biases present in censuses Limited granularity surveys are meant to be nationally representative sample size is often too small to allow the disaggregation of data for local areas administrative data are often incomplete and of poor quality and census data are quickly out of date Limited frequency a lack of regular data makes it difficult to keep track of population changes that could inform the demand for service delivery survey frequency is actually lowest among poor countries time lags in processing the data frequent data can help to track sudden changes in well-being (understand vulnerability and the impacts of shocks, using more frequent data on income/expenditure and employment, health, consumption, access to basic services) Transforming cities into smart cities: Opportunities, Challenges and Risks 21

22 Issues regarding suitability of indicators for urban contexts Monetary Poverty measures based on income/consumption can underestimate the higher costs of living in urban areas non-food expenditure e.g. water, electricity, transport, in some cases access to toilets, housing, education and healthcare can be particularly costly in urban settings. Access to shared services in dense urban areas Access to water and sanitation is often measured as the proportion of people that have access to improved water sources and sanitation facilities. Standards for adequate housing The slum household concept is defined as one lacking one or more of the following: a durable housing structure; access to improved water; access to improved sanitation; sufficient living space; and secure tenure. Difficult to assess given the lack of available data. tenure and affordability are important aspects of housing deprivations that are often missed. Other dimensions insecurity and violence (political violence, clashes between ethnic groups, mugging, gender-based violence) discrimination, not equal treatment, (e.g. stigma is raised as an important issue among slum dwellers in India, who highlighted that they were not treated as citizens, faced discrimination and were stigmatised by society) Transforming cities into smart cities: Opportunities, Challenges and Risks 22

23 Using Technologies in urban data collection to implement SDGs Sensor data systems for weather conditions/air pollution monitoring infer air quality in urban areas provide real-time and fine-grained air quality information to inform people and guide their daily decision-making temperature, humidity, barometer pressure, wind speed, weather cloudy, foggy, rainy, sunny, snowy, pollutant concentrations of CO (carbon monoxide), NO2 (nitrogen dioxide), and O3 (ozone) energy consumption of heating oil Sensor data systems for assistive living mobile healthcare applications alerting caregivers wearable sensors to measure different types of health conditions (e.g. temperature, heart rate, blood pressure, pulse oximetry, electrocardiogram) smartphone with embedded sensors (compass, accelerometer, gyroscope, GPS, microphone, temperature sensor, magnetometer, proximity/light sensor). Sensor data systems for disaster management human as sensor through natural visualization tools to determine using social media (Twitter) as a language via tweets, understand the city's sentiment and opinion time constraint (posts should be within a specific one hour long period) location constraint (GPS coordinates should lie within a 10-km radius circle). Sensor data systems for intelligent transportation (Internet of Vehicles) improve daily routine (e.g. vehicle speed, number of people arriving, vacant parking slots) Transforming cities into smart cities: Opportunities, Challenges and Risks 23

24 Monitoring poverty with Urban IoT The area: The inhabitants of Red Hook, suffer from asthma at a rate 2.5x the national average, and also have a lower average life expectancy (by ten years). A third of Red Hook residents are living below the federal poverty line. The problem: Little data known about how the built environment and urban environmental conditions impact individual and community well-being and health The solution: Urban IoT sensor systems used to gather high-resolution information in order to create a quantified community Objective: The creation of new models and metrics for urban planning and place making; to create methods that allow citywide assessment across very diverse sections of the local population and economy; and to improve municipal outcomes on health, activity and resource conservation. Example: analysis of intersection and street-level temperatures during periods of extreme heat, allowing the possibility to alert those registered with respiratory illnesses Data collected: Focus on environmental measurements including temperature, air quality, luminosity, pressure and humidity Transforming cities into smart cities: Opportunities, Challenges and Risks 24

25 Benchmarking Examples of benchmarking techniques include: Expert input through focus groups (e.g., preselected groups of individuals with diverse technical backgrounds and interest in the city s sustainability program) or panels (e.g., groups of local, national, or international specialists in urban sustainability planning and implementation) Comparison with cities in the same region, of the same size, or at the same level of development; or against cities that the city aspires to emulate; Comparison with established international standards (where available), such as air and water quality, developed by global or national entities Review of industry guidance documents and relevant standards Transforming cities into smart cities: Opportunities, Challenges and Risks 25

26 What gets measured gets done. And when lots gets measured, lots get done. Only by collecting and using good, measurable, open, accessible and disaggregated data can we leave no one behind Data Matters: The Global Partnership for Development Data, Speech given by Justine Greening 2015

27 Which are the challenges to smart cities transformation?

28 CHALLENGES TO SMART CITIES TRANSFORMATION Technical Governance Financial Institutional CHALLENGES Economic Social Environmental Service Delivery Transforming cities into smart cities: Opportunities, Challenges and Risks 28

29 SMART CITIES TRANSFORMATION CHALLENGES deployment of integrated infrastructure and platforms ensuring systems and data security ensuring the adoption of interoperability standards provision of analytical methods needed to integrate qualitative and quantitative data from heterogeneous sources for improving efficiency of city operations optimizing the use of limited resources managing spectrum utilization having the appropriate technology at the right time contextualizing a solution or a good practice to the local conditions producing and delivering scalable solutions TECHNICAL Transforming cities into smart cities: Opportunities, Challenges and Risks 29

30 SMART CITIES TRANSFORMATION CHALLENGES ensuring availability of financial resources addressing possible lack of capacity for attracting investors ensuring the construction of cost effective buildings and facilities reducing operational costs ensuring long-term sustainability of the delivered solutions FINANCIAL Technical Transforming cities into smart cities: Opportunities, Challenges and Risks 30

31 CHALLENGES TO SMART CITIES TRANSFORMATION budget cuts affecting local governments obtaining funding for Smart City initiatives improving local competitiveness against regional and international markets diversifying economic activities reducing capital and operational expenditures ECONOMIC overcoming pressures to the resource base due to growth of urban populations Financial Technical Transforming cities into smart cities: Opportunities, Challenges and Risks 31

32 SMART CITIES TRANSFORMATION CHALLENGES protecting natural resources and green areas addressing the scarcity of natural resources reducing dependency on gas and oil reducing emissions generated by transport systems ENVIRONMENTAL Economic Financial reducing air pollution reducing energy consumption or using renewable energy addressing environmental degradation caused by urbanization adopting green practices Technical Transforming cities into smart cities: Opportunities, Challenges and Risks 32

33 SMART CITIES TRANSFORMATION CHALLENGES SERVICE DELIVERY Environmental Economic Financial increased demand for energy, water and sanitation increased waste generation and shortfalls in municipal budgets to collect and proper dispose of waste increased pressure on housing and transport systems improving public safety by reducing crime and emergency response time reducing traffic congestions ensuring the construction of comfortable city facilities and buildings improving quality of services by delivering innovative services and streamlining and tailoring services to address citizens needs ensuring the right levels of security and resilience across delivery models updating new releases of public services without major disruptions to ongoing service delivery ensuring 24*7 service availability ensuring customers satisfaction by maintaining data and information up to date Technical Transforming cities into smart cities: Opportunities, Challenges and Risks 33

34 SMART CITIES TRANSFORMATION CHALLENGES ensuring social inclusion SOCIAL Service delivery Environmental Economic reinforcement of social and territorial cohesion ensuring equity and fairness addressing political and ethnic tensions ensuring the availability of services for all the different communities in the city leveraging human capital Financial Technical Transforming cities into smart cities: Opportunities, Challenges and Risks 34

35 SMART CITIES TRANSFORMATION CHALLENGES INSTITUTIONAL Social Service delivery Environmental ensuring departmental coordination and alignment overcoming bureaucracy in government agencies attracting qualified IT professionals and relevant IT players having qualified human resources for service delivery Economic Financial Technical Transforming cities into smart cities: Opportunities, Challenges and Risks 35

36 SMART CITIES TRANSFORMATION CHALLENGES GOVERNANCE Institutional Social Service delivery Environmental Economic engaging private sector in testing solutions adopting decisions and proposals made by citizens defining the proper role for private sector actor interventions defining where, when, how they should be engaged attracting talent enabling distributed implementation and execution by different stakeholders supported by central coordination establishing a governance committee with broad representation of government levels and societal sectors Financial Technical Transforming cities into smart cities: Opportunities, Challenges and Risks 36

37 Demetrios Sarantis egov.unu.edu