It might be argued that local search, on-demand services, and other components of the local digital marketplace have long had a neighborhood problem. Facebook, only tangentially a member of that group, nevertheless sums up the problem well. Networks of affinity on Facebook haven’t been inherently local in nature since the company expanded outside the university campus. At its founding, Facebook was about establishing online connections between people who went to the same school, but the company quickly pivoted away from locality as its defining feature. Local is now just one among many filters by which friends can be sorted, and Facebook gives us little in the way of connecting us with our local neighborhoods and communities.
Local search services like Google Maps and Apple Maps have built models that also have little to no relationship with the communities whose locations they depict. Though Google crowdsources data improvements through suggested edits and Mapmaker, the input it gathers from community members is fed like any other source into the Google machine, emerging on the other end as generic data certified by the company. The stance is very unlike that of local mapping services like Open Street Map, Waze (acquired by Google), and Locationary (acquired by Apple), which attempted to create a platform for local experts to curate map content. Due to the coverage and accuracy standards Google and Apple are trying to meet, it makes sense not to leave map curation entirely in users’ hands; yet the companies are likely missing out on an opportunity to build some sense of real community into their services.
A step closer is Foursquare, whose venue content is crowdsourced to a significant degree, and whose focus on mobile location awareness allows it to surface tips and recommendations to those who permit the Foursquare app to track their movements. Still, like Yelp reviews or the Facebook feed of a local restaurant, Foursquare tips are largely about helpful local content provided by strangers.
So what would community-based local services look like? One answer is suggested by Nextdoor, the private social network for neighborhoods that has proven successful in some communites (though troublesome in others). Its fans say the service helps you make personal connections with neighbors you’ve lived next to for years but never learned their names, and certainly that level of community building is welcome in the digital space.
The claims are hard for me to certify, though, because in my community Nextdoor’s active user base is thin, leading me to the conclusion that the service suffers from the familiar “ghost town” dilemma. Platforms that depend on an active user base can tend to seem ineffective if they fail to garner sufficient buy-in from those users. Imagine Yelp with no reviews.
So too, there’s a sense in which Nextdoor’s focus on connections between neighbors – message boards, classifieds, babysitter recommendations, safety alerts – captures one aspect of what makes up a local community but misses out on several others. Local businesses, news services, event coordinators, service organizations, volunteer groups, city councils, and chambers of commerce are as much a part of any community as its residents, and each has a role to play in creating a fully realized community network.
The declining presence of local newspapers has left a void no truly effective digital forum has arisen to fill. So far, digital services, even those focused on local, have done more to atomize local communities than unite them, training us to rely on anonymous resources for the information and recommendations we used to get from our friends and neighbors.
Nextdoor’s closed-circle approach has its place, but I’d love to see a more inclusive and data-rich version of a community network that links up in a meaningful way with the concept of local search – a model where every entity in the local community has a digital representation, with simple tools for communication, information sharing, and interconnection. I suppose what I’m suggesting is a path toward resolving the fragmentation in local services where specialization in local data currently fails to intersect with specializations in social interaction or tangentially related services with a local component, such as Airbnb and Uber, both of which are innovating in useful ways to build trust between strangers.