How Cities Are Using Big Data

Tim Jonas our consultant managing the role
Author: Tim Jonas
Posting date: 1/9/2019 2:20 PM
High speed trains in Florida. Driverless cars in Arizona. National grid union agreements. All these and more are working to create a more smoothly operating system of infrastructure. While privacy laws and transparency vie for attention at every level of government in the US, cities have taken the onus of using data to make decisions.  

The functionality a critical infrastructure society is built on – railroad tracks, flare stacks, power lines – has been brought together by robotics and AI. The decentralization of intelligence, cloud systems which remotely control Industrial IoT, and AI are just a few of the ways in which 2019 will be a breakout year for Distributed AI.

New York City Uses Data to Alleviate Damage Risk to Buildings
In their race to stay ahead of Big Data, they may also find ways to improve they might never have discovered without it. New York City has limited staff who can analyze its million properties and incorporate analytics to discern fire risk considering past risk and building traits. City coding has therefore become more important than ever to alleviate potential risk.  

Philadelphia Focuses on City Interaction with its Residents
Evidence-based decision making has debuted in Philadelphia’s GovLabPHL, a multi-agency collaboration. Together, they are centralizing and digitizing records making information easier to share among agencies that historically kept information to themselves. With everything in one place, they can provide city services to their residents much more effectively and efficiently.

Florida’s First High Speed TGV Train
Rolled out late last year, this high-speed train travels from Miami to West Palm Beach with plans to branch into Orlando and Tampa soon. America’s first high speed passenger train in years will help alleviate road traffic, noise pollution, and more. Data collected may include best safety measures, business practices, and economic value to the city and its residents as money shifts from car buying to rail ticket purchases.

The Ethics of Data and Potential Risk of Bias

Gaining insights into human behaviors, ease of transportation, and predictive information to curb damage to buildings and other city properties are all important to a smart city’s infrastructure. But, data is, after all, input by humans and isn’t infallible; falling prey to natural biases. Researches and analysts caution decision-making from computer-based algorithms isn’t perfect and should be considered with discretion.

For example, the rise in AI, face recognition software, traffic cams, and statistics currently on file may hold a prejudice against certain ethnicities based upon their developer’s biases. This is especially glaring in criminal behavior predictions and as such, policymakers need to think critically and to not take technology at face value. After all, those inputting the data are human, and our biases have a way of seeping into our information.

In 2019, AI systems are no longer the robotic machines once shown in movies as something to fear. Today, vendors who build these systems must not only focus on the value provided, but also consider moral foundation of their service. It’s important to understand exactly why and how data will be collected and with whom it will be shared. As cities and businesses continue to catch up, this knowledge is necessary for long-term viability, credibility, and transparency. Trust is a crucial element of data strategy. 

See Through Cities – Transparency is Key

City governments and researchers are working to lessen discriminatory outcomes by turning to transparency. Major cities such as Philadelphia and New York have opened up their websites and invited the public to examine information and their methods of interpreting the data. New York implemented a task force to study how the city uses data and its goal is to present in December of this year ways the city should assess its automated decision-making for transparency, equity, and opportunity.

This is a pivotal year for cities to understand their urban ecosystems. Understanding challenges such as traffic, pollution, parking and inefficiency of movement in urban areas may help realize how, when, and where people are moving. With core infrastructures in place, movement may be reduced. In addition, mobility will become efficient and lessen people’s need to move around for better jobs and/or housing. AI is the tool to help cities gain visibility into this type of data. It will enable not only visibility, but also foster prediction capabilities, and provide actionable insights to improve our understanding of why, how, and the way we move.

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