The term “Smart City” has become very popular over the past few years. Do people know what it is? Simply put, Smart Cities are data driven, using communication networks, sensor technology, simulation, and human input and interaction to make better decisions about services and infrastructure. Areas where smart cities can utilize data to improve performance include power, water, wastewater, sanitation, traffic, maps and directions, local information, and emergency response plans.
Image courtesy of informationsecuritybuzz.com
Why are Smart Cities Important?
Successful outcomes of making a city smart include:
- Improve the delivery of City services
- Prepare for emergencies
- Support Economic Development
- Enhance the experience of the citizens
Cities can improve not only the communication with citizens, but also ensure visitors have a successful visit with smart maps and information. It is no wonder many cities around the globe are placing resources and budgets on data driven initiatives!
Smart Data Analytics in Infrastructure
Data Analytics is a critical part of infrastructure projects. A cloud-based platform, like Signals, provide users with real-time access to data, in an interactive visual tool. Access to the data aids operational intelligence, eliminating the guesswork often used in making infrastructure decisions. The saying “you can’t act on what you don’t see” applies to city infrastructure data as much as it does to businesses.
One way to use data analytics for Smart Cities is emergency response programs. We have developed a critical infrastructure simulation and analysis system for situationally aware emergency response during natural disasters. Initially prepared for hurricane paths, our simulation scopes also includes the infrastructures of electric power, telecommunications, water, and railroad transportation.
We have also developed event-based algorithms that aggregate and transform the simulation data into geospatial-temporal visualizations to enable the users to detect patterns and build new models effectively using their background knowledge and experience. This analysis is easily shared with other users, and can be analyzed interactively.
Citizen Engagement
Data also helps us understand human behavior so cities can make informed decisions. Cities that foster citizen engagement in infrastructure projects can use this data to improve service levels. When citizens participate in budget prioritization, more funds will be allocated to the citizens’ top priorities. Collaborating with citizens helps cities get to the root of the problems that local businesses and citizens are experiencing. Both of these results lead to higher citizen satisfaction levels with city services.
Case Study — Mobile Engagement
A recent example of citizen involvement was a Mobile Engagement Strategy Workshop held by the City of Charlotte in December 2015. The purpose of the workshop was to generate ideas from citizens on digital services they would like to have. The data is available on Open Charlotte. Data from the Open Data workshop is free to use, so we uploaded it into the Signals platform to see what people were saying and why.
The largest category of citizen ideas was citywide items, including social media. We dug into the data to for more details and found that a core idea was using Social Media as an emergency alert mechanism. Social Media can be used for anything from natural disasters to child abduction to terrorist activities. Along with this positive use of Social Media, there was the counterbalancing idea of the city not becoming too much of a “big brother”. Set a citywide standard that ensures residents’ online social media presence is not surveilled for predictive policing. How long did it take us to spot these insights in the data? Less than a minute. Visualization of the data allows us to easily identify critical items. Simply by clicking on an area of interest, we can zoom in on the ‘why’ behind the data.
It is often useful to pivot the data on a structured field. We decided to take one of the most popular categories, Housing and Neighborhood Development, and see the specific items suggested by citizens. We found that the common issues of concern are gentrification (predicting and mitigating negative impacts), and being able to report issues (potholes, vacant lot problems, etc.) from a phone. Another common idea was providing more neighborhood information, such as bike/running trails maps showing parks, schools, transit, and government offices in the area.
The data collected from the citizens was in the form of unstructured text, and placed into categories. This makes it easy (and fast) to run data analytics against the unstructured data to gain useful insights on what people are saying. Imagine surveying thousands of people, and quickly visualizing the common themes, trends, and issues to identify key insights for improvement. That’s a Smart City!
Data analytics can be used for visualizing the flows of all moving parts in the City. By “listening to the citizens” cities have the potential for adjusting services on a real-time basis, whether it be potholes, cycle routes, or finding a doctor in a medical emergency.
If you are interested in trialing Signals on your data set, or using it to examine one of the Open Data sources, contact us at clientsupport@stratifyd.com.
