Potential Uses of Urban Analytics to Increase Economic Productivity
How smart city technology can generate insights on various drivers of productivity
In today’s world where the tech industry continues to rise, data is the new gold. The digital transformation of everyday activities such as shopping, commuting and even watching TV has enabled tech giants and startups to determine its strategic direction and improve operations based on real-time analytics. Companies can quickly understand and predict what product is most appealing to an individual customer, foods they would regularly order, current places of interests and ads they would like to see. This data would be the basis for product development, pricing & promotion strategies and partnership building. It also gives a stronger lens to see the business performance from a national level to a single neighborhood. We can easily identify which areas currently have high demand and calculate the cost to serve. These are all just a few examples of the limitless potential of data analytics.
Being a professional in data analytics and an urbanist, I tend to wonder, how can we optimize the use of data to improve the livelihood in our cities? Several countries are now adapting the internet of things (IoT), where physical objects are embedded with sensors, software and other technologies to connect and transfer data to online platforms. The data collected includes pedestrian movement, traffic congestion and air quality. This has led to the rise of smart cities, cities that use data insights from technology to solve its current and potential problems. Governments of large cities like New York, London and Jakarta have established special institutions dedicated for developing urban analytics to improve public service. Such practices include placing sensors in traffic lights to detect mobile signals and in buildings to measure energy efficiency. Having this access to this real-time data, how can we use it to boost economic productivity? There are 5 areas that come to my mind: transport efficiency, land valuation and usage, affordable housing, green space availability and small business empowerment.
Traffic congestion has been a long ongoing issue for large metropolitans, especially for cities like Jakarta where public transportation is still being developed. Current smart city technology can now capture the amount of congestion in every road and hour through sensors and satellite imagery. Having this data, urban analytics can give in-depth visibility on the root causes of the congestion; whether it is traffic light malfunctions, inadequate public transport availability/capacity, or simply just too many cars with a low passenger per vehicle ratio. From here, we can calculate the socioeconomic cost-benefits and provide potential mitigation options. Such costs to capture are increased carbon footprint and loss of employee productive hours from commuting. We can also calculate several benefits from congestion reduction, such as increased number of food deliveries or taxi rides. Having reliable calculations and clear planning brings stronger cases for generating public investment. In Hangzhou, local authorities found that simple adjustments in traffic lights could reduce a significant amount of travel time using this technology. The city of Liverpool could improve its bus service by measuring the density and average waiting time in each stop. It also gave insights for creating new bus routes based on passenger needs.
Transport infrastructure and urban regeneration projects can generate value uplifts for the surrounding land. Current methods are mostly based on historical archives and qualitative data from surveys and focus group discussions. Retrieving such data would require timely manual work and elements of subjectivity as public data is often scattered and research respondents may not be representative. With urban analytics, local authorities can identify the economic activity patterns in areas within proximity of those projects. By digitizing the historical archives, we can calculate and visualize the correlation between land and housing prices with the existence of the transport infrastructure or regeneration project. We can also compare the productivity of businesses within and outside the radius based on the human traffic data generated from sensors. This can become a basis for predicting land value uplifts from planned projects. Another thing urban analytics can do is compare the economic use of a property against the potential land value. This visibility can be provided by a map that shows the economic activity of a property in a selected area. If the property use does not meet the potential, local authorities can design incentive/disincentive schemes for the owners to develop their property into productive use. Having this valuation model can also give ideas for urban redevelopment projects. The IoT technology can detect underdeveloped areas with low values to be subjects of investment in transport infrastructure and commercial estates that would increase employment opportunities.
With visualization on mobility patterns, urban analytics can also help improve housing policies. Let’s start with an example of an office building in South Jakarta and the everyday commute of its employees. Most employees would take around 45 to 90 minutes from home to work. While reaching their workplace, they would pass several landed houses that are beyond the affordability limit. What if the data shows that these houses have low to zero occupancy rate, when based on realistic calculations it can accommodate 2–3 families? It means that there is a productive waste caused by the owners. Instead of having unused space, the government can incentivise the owners to provide shared living opportunities for young families as a means of affordable housing. The employee will no longer need to spend much time commuting, increasing his/her productive hours and reducing the amount of vehicles on the street.
Green spaces, such as parks and recreational grounds, are vital parts of the city. It fosters economic activity in the surrounding areas, a place for community activities, increases tourism, uplift land values, and most importantly, improves air quality. However, many local governments have failed to realize these economic attributes. Most government officials would prefer real estate development over green spaces due to direct short-term revenue. Urban analytics tools can improve the cost-benefit analysis by showing the economic and environmental impact of green spaces. With proper data visualization, we can compare the level of economic activity in areas within proximity of a park and those without. We can also see the air quality and calculate the environmental cost from areas outside the range of a park. Buildings with lower air quality are less likely to attract high economic activity. Local governments can also conduct experiments, such as converting a vacant government-owned property in one neighborhood into a park and another into a rental home and compare the economic outcomes. Of course this would not be as easy as it sounds, but it gives the idea on the potential use of urban analytics for improving green space.
From the data generated by urban analytics, we can have an in depth analysis of the shopping habits. There might be cases of residents in the outskirts spending weekly one hour drives by car to purchase goods at a shopping mall or a chain supermarket. Meanwhile, the data shows that there are several Mom and Pop shops within walking distance that provide the same product categories. Seeing this, we can find the patterns of these shops that affect residents’ preference, whether it is the shopping experience, product completeness or pricing. From here, we can identify the right support policy these SMEs need, e.g. feasible financial loans to build their business, proper training or access to better suppliers. Small business owners could then grow their business to further support their local community.
A concern many would have regarding smart cities is data privacy. Some people would feel unease with the idea of the government watching our movements. Seeing from a different perspective, all the data collected is used for a greater good of the citizens. Urban analytics does not track a specific individual, rather see the overall patterns in an area. Moreover, there would be little value for the government to monitor common citizens unless one has committed a felony. So there should be less to worry and more to hope for.
In conclusion, there is much to explore on the potential of urban analytics for boosting economic productivity. The real-time data generated gives local governments visibility on the citizen needs without having to spend hours doing research and fieldwork. It can also help urban planners to predict future outcomes of their development strategies. Let’s get excited to see how smarter our cities will be!
