URBAN4M has released a beta version of its aboutPLACE API, bringing a new approach to serving location analytics within apps and Web-based products. Founder and CEO Hillit Meidar-Alfi spoke with ProgrammableWeb about how URBAN4M's delivery of urban data will transform hyperlocal products and services.
The aboutPLACE API is a RESTful API designed to help developers integrate URBAN4M's way of categorizing place data in their apps and solutions. URBAN4M provides four main ways to organize place-based data:
- PLACE is an interactive map that can be created to highlight specific data sets as required (for example, the location of specific amenities or census data shading).
- Pulse is a tool to evaluate and compare different PLACEs within URBAN4M's database.
- Quality of Life Index is a measure of walkability, safety, schools and public transit accessibility.
- Vibe aims to "capture the area's personality" by aggregating data from a number of sources into URBAN4M's proprietary system of characterizing areas.
Developers integrate the API by identifying locations using a place identification marker that aboutPLACE recognizes. From there, specific data sets can be returned or data can be displayed as PLACE, Pulse, Quality of Life or Vibe. A sandbox environment is available in the aboutPLACE API documentation to allow developers to test API calls, see what gets returned and fine-tune search queries.
For now, aboutPLACE is in beta release with data for Austin, Texas; Boston; and Miami. URBAN4M is taking a wait-and-see approach to its ecosystem business model, meaning access to the API is free for all users for now, with monetization expected when there is more data on how developers are using the API and how the API is leveraging commercial opportunities across the platform.
Initial Hyperlocal Market: Real Estate
Meidar-Alfi confirms that URBAN4M is targeting the real estate industry as the primary vertical, but sees opportunities across the board for integrating the data into hyperlocal services. For real estate, Meidar-Alfi believes the aboutPLACE API can help developers turn property searches on their head, potentially disrupting how real estate functions with the availability of hyperlocal data.
Using aboutPLACE's data streams, for example, a real estate property search can begin with a searcher identifying some household characteristics — family needs for schooling, any recreational interests of household members, whether the family has a car or prefers to bicycle, for example — as well as the usual demographic data such as ages and income range.
Once aboutPLACE understands the family dynamics, a multiple listing service or realtor's host site using the API can return a property list that shows available properties that are based on the location needs, including properties that are near appropriate schools, with parking or good bike lanes, close to preferred recreational opportunities or whatever else has been indicated as matching the lifestyles of the household members. This is a 180-degree difference from searching for a three-bedroom property first with or without a garage and then trying to identify if the property is near the amenities that match the family's lifestyle.
Real estate web platform Zillow already makes use of some open data sources to supplement its property searches. It can weave in data from amenity-mapping sources and things like crime rates, but at present, this is done post-property search: Location analytics are shown after the property search is done to provide contextual information for the searcher. What Meidar-Alfi is proposing is about turning property searching completely around by matching personal characteristics with context first, and then surfacing relevant data on individual properties.
The Growth of Hyperlocal
Hyperlocal market opportunities are just beginning to grow after what feels like several years of hype around context and personalization. To date, these have been poorly integrated in a way that is not really creating a killer user experience. This is changing: The growing availability of predictive analytics APIs, for example, enables more nuanced contextual and personalized data to surface in mobile apps, and the creative potential of what will be possible using hyperlocal data is transforming.
This year has seen the U.K.'s public transit data platform Transport API, for example, help a new range of hyperlocal market products to emerge: Toothpick uses public transport data alongside National Health Service data to help users search for dentists that provide their insurance coverage and are accessible via public transport. ScreachTV feeds in public transport data to its venue broadcasting so that punters can stay in the pub until their bus arrives at the closest stop. And services like Yelp and JUST EAT are investing in ecosystem APIs to enable a more transactional, hyperlocal platform, starting with food delivery services.
URBAN4M believes it has created a new approach to urban analytics that can further the hyperlocal cause. It uses its own techniques to draw in open and proprietary data from multiple sources, clean and analyze the data, and enable it to be included in its database. The difficulty will be in scaling this approach. Crime data, for example, is notoriously difficult to compare across areas as it is often collected using different methodologies in different areas. Meidar-Alfi's Ph.D. in city and regional planning will no doubt help her keep her team on track, but the lack of civic data standards may become more obvious as URBAN4M tries to grow and scale.
Developers can sign up for a free account on the URBAN4M's aboutPLACE Web pages.