Turning Humans Into Sensors to Collect Data
It’s hard not to notice that the parking industry is drifting increasingly into the realms of intelligent transportation systems and “smart cities.” From real-time parking analytics integrating with wayfinding tools to parking payment providers integrating with mass transit and other “smart” payment options, we certainly don’t live in a bubble today.
While we are already using crowd-sourced GPS information to extract data that will show the fastest route to your destination, social media remains a largely untapped source of information that can help smart cities in their efforts to use information and communication technologies to improve livability.
I’m not just talking about social as a community engagement or marketing tool where cities distribute important information or “listen” to the feedback of their constituents. No, I’m talking about using social media conversations the same way we use data collected from sensors in the roads and sidewalks.
Discerning public sentiment, behavior
While some technological challenges have had to be addressed along the way, cities around the world have begun to use social media analyses to discern public sentiment and behavior, ranging from people getting sick from unsanitary restaurant conditions to the illegal distribution of drugs.
In 2014, the NYC Health Department worked in collaboration with Columbia University to take Yelp reviews and turn them into machine-readable data. The data were searched and analyzed for keywords such as “sick,” “vomit,” “diarrhea” or “food poisoning” to identify instances of foodborne illnesses that hadn’t been reported to the health department.
The initial pilot test found three restaurants that were responsible for 16 illnesses. On inspection, these restaurants were found in violation of multiple health codes. NYC has continued this project by reviewing feeds daily and expanding the review of social media posts beyond just Yelp.
Social media is useless, you say?
Social media has real value beyond marketing, customer service and engagement. It turns humans into sensors that can track sentiment.
By using advanced analytics and natural language processing technologies that spot keywords to infer topics of messages and posts, you can analyze large volumes of public social media data to assess and understand public opinion.
Combine this information with geo-tagged location data, and you can further drill down on sentiments by neighborhood. Having such a source of unfiltered citizen attitudes and actions is valuable for making informed decisions.
You can find out what the stressors are in your transportation system. Is it from traffic congestion, accidents, construction, weather, parking, or special events? How do other services such as garbage collection and mail delivery affect a population’s mobility? Having the answers to such questions allows cities to pinpoint and prioritize the actual issues in infrastructure, scheduling or policies (think parking minimum requirements).
This translates into being able to make more targeted investments, improve existing city services, and surface best-practices that worked in one location that can be applied to other city zones.
Listening at its finest
Cities can learn valuable information from social media, but the emphasis needs to shift from talking to listening. Municipal use of it shouldn’t be limited to the communications team talking about city hall’s accomplishments or community events.
As we continue to turn our cities into responsive, living entities that adjust to the needs of the population, integrating information from less obvious sources such as social media posts can make all the difference in using limited resources to target investments effectively.
The stresses that cities will face in the future as urbanization continues will only increase in scope and volume. As we bring greater numbers and proportions of our population into dense locations, we can only alleviate challenges through adaptable and progressive solutions to the most pressing problems based on aggregated data.
And who knew social media could turn humans into sensors to collect it?