Cities are becoming products with the help of location data

The cities of tomorrow will increasingly have to exist in a constant state of change to suit changing needs from their residents, as well as from the planet itself (cities need to become greener). Cities are also increasingly fighting for the attention of its current and future residents in competition with other cities. In short, cities are becoming products.‍

October 30, 2018

Share this article

In general, modern product development has become heavily data-oriented, in that agile methodology and access to real-time(ish) data helps to better understand users and quicker understand how those users are changing - so that the product(s) can change with them.

For cities, that has been hard, even impossible, due to limited (if any) access to data. That is now changing with the rise of human mobility products providing the missing piece of the data puzzle: Insights on how we as “city users” are reacting to changes in our cities.

By understanding human mobility data, cities too can better understand users and quicker understand how those users are changing - so that the product can change with them.

This is still a new field within data analytics, and therefore I wanted to show you some of the exiting stuff and products we are working on at Unacast at the moment to empower cities around the globe.

All of these data products are based on anonymous and aggregated data that is scrambled each 24 hours.

City Commute Overview

Understand the commuting habits to and from a city. On a city to city basis get an overview of where people frequent to and from, how long their commutes are, their weekly patterns, how they trend and their seasonality. Allowing you to answer questions such as:

  • Where do the inhabitants of a city work and how long is their commute?
  • What is the influx and outflux of people during the weekends in a city? Is a city losing or gaining traction in terms of visitations by non-residents (popularity)
  • Where are the international tourist visiting a city traveling from, how long do they stay, and where do they go afterwards?
  • What share of people passing through a city on any given day makes a visit in town (e.g. shopping, eating, cinema etc.)

Further, discover how a city ranks comparing to other cities in terms of share of commuters, share of non-resident workers, weekend visitation popularity, time arriving work, time spent at work, distance commuted, time spent commuting and more.

Heatmaps show the popularity of home vs work in two neighbouring cities

City Centre Activity Levels

Discover through data insights, the hour by hour, day by day activity levels of a city centre and how it stacks up to its neighbouring cities or any other city. Evaluate what areas within a city that's visited the most and longest by local inhabitants, people from nearby cities and tourists. Enabling you to answer questions such as:

  • Has the new 4D Super MAX Cinema in a neighbouring city impacted Saturday night activity in the other city?
  • What was the actual activity increase the last music festival had on the city centre, where did they travel in from and how does this event compare to the last five events?
  • How are the number of people visiting the shopping district affected by the latest removal or addition of parking spaces?
  • Did the refurbishment of a downtown area, actually cause people to (1) stay in the area for longer, (2) visit the area more frequently?

The City Centre Activity Levels dataset will allow a city to spot trends, one-time events and changes to a city centre usage early, and provides the materials to make amends or attribute where praise is due!

City centre dwell times lets cities understand inner-city usage in detail

Trips Graph for e.g. bike sharing

Understand how people move through cities by accessing the full graph of trips revealing commute choke points, desired paths, and incident ramifications. The insight is broken down to which areas that are passed by on routes from A to B, giving answers to questions such as:

  • How many people passed by the areas covered by a bike rack throughout the day? And how does it change over time?
  • What's the top 5 most common places people travel to that reaches beyond the current bike rack coverage?
  • What are the biggest hubs not covered by bike racks at the moment?
  • How many of the people traveling from bike rack A's area to bike rack B' area did not use use the bikes?
  • What mode of transportation would be best to capture a specific underserved trip?
Bike sharing companies can understand their underserved trips and therefore the share of the available traffic that they serve

These are just some of the ways we are starting to bring data and insights into the hands of city officials - so that we can all have cleaner, better “products” to live in.

Get in touch if you want to learn more about the cities of the future and how location data empowers them.