Smart Cities

The concept of a city has changed over the course of human history, from self-sustained, small, and farming-intensive settlements to urban metropolitan centers where cultures intermingle and industries flourish. However, throughout this timeline, cities have collected information about their residents and operations as estimates, and on a community level, not an individual level. This makes decisions made on the city inaccurate and the overall knowledge of a city’s residents very blurry and unclear, causing a city’s economic output to never reach near its full capacity and slowly but steadily, deteriorate in its efficiency and optimization of production. To overcome these problems, a relatively new ideology started to materialize in the last decade, known as ‘smart cities’. In simple terms, a smart city can be defined as one where the collection of resident data is the prime goal and is of utmost importance, which is then used to manage assets, resources, and services efficiently and overall, improve city operations. The data collection application system is implemented using technological methods such as electronic, voice activation, and sensor methods. Some examples of data collection methods include CCTV, smart devices, and digital libraries. Data collection is part of 3 broader forms of intelligence that are demonstrated in a smart city, known as instrumental intelligence, where data is collected, analyzed, and then used to train machines to make intelligent and accurate predictions. The other 2 forms of intelligence that we take for granted are orchestration - institutions are established for problem-solving real-world problems - and empowerment intelligence, where cities provide platforms and resources to nourish innovation spurs. After obtaining data and demonstrating instrumental intelligence with that data, a city would make appropriate decisions with the data that is turned into information. Although this may be a bit counter-intuitive, even after using prediction modeling, there is still a need for human intervention to innovate and problem-solve with the predicted data. They can then apply solutions that would reduce the amount of undesired data after further predictions are made using machine models. An example of this would be, let’s say, after collecting data about air quality via sensors and using a machine/deep learning model, a group of specialized people

come together and start presenting ideas such as electric car production, bicycle stands that can be accessed using digital cards, etc. The best of these ideas is then used to estimate effectiveness, cost-benefit, externalities, etc., after which the most suitable idea based on the results, is implemented.

As the concept of smart cities further solidified and developed, the creation, adoption, and integration of a smart city required a specific set of frameworks based on the city, its objectives, and its residents. These frameworks can be categorized into 5 categories; technology, human, institutional, energy, and data management. After implementing and materializing these frameworks, the residents and people are encouraged to engage in them. This is done by events, challenges, and commercialization of certain initiatives. Some examples which are well-known are LEAP, the AI City Challenge, and Apollo.

Let’s take the example of the city of Lahore in Pakistan. Lahore, known as the breadbasket and fat-cat of Pakistan, over the years, has made its way to becoming the most polluted city in the world, where a 3 km distance could take 2 hours, 6300 people live per km2 and crime rates are at an all-time high. However, the local authorities have been fairly responsive to these problems and have implemented smart city solutions in some aspects. They have set up a relatively small and simple data collection application system, consisting of CO2 and infrared sensors, CCTV cameras that are trained to identify a robbery or crime scene and instantiate an emergency at the nearest police station, as well as a complex system of cameras that identify how long it would take a car to get from one place to another. Along with this, they implemented an intricate form of data collection related to energy usage, known as an energy data management system, in the more urbanized areas of Lahore. From this valuable data gathered, the authorities gathered IT companies, consultancy groups, and construction companies, who were used to a relatively simple solution. The solution consisted of initiatives to produce electric cars, construction of roads that were predicted to reduce traffic during rush hours (an ITS), and funding online payment and banking and fintech companies to encourage residents to shift from the hard-cash norm. Furthermore, a metro system was built, where, to keep with the cashless goal, digital pods are used, along with a scattered smart grid, as data results showed there was an excessive amount of CO2-releasing fuels being used. The smart grid consisted of using a range of renewable energy sources, such as solar panels and biofuel, which was already commonly adopted in the rural areas of Lahore. With regards to health, telehealth was encouraged to reduce patients whose situation would get worse due to traffic and air quality.

These measures which were taken are starting to return the city’s situation to normal, where,all-in-all, the city’s operations are starting to become more efficient!

Now, even though this was quite a bit of information to digest, we could still go on about how cities have prospered from this ideology, with improved lifestyle, industry, and service provision!