16 Februar 2016 User Stories

LotaData Asks, Will Your Neighborhood Shine Red or Blue?

Von Apu Kumar

Our neighborhood rolls down the hills

      Through happy farms to the old mill

Green oaks, red doors, enchanting collage

      The occasional tremor and a foot massage

The old downtown's still smart and fine

      Inclusive, affluent, spiritual, divine

Sumptuous restaurants, boisterous bars

      Drones, hoverboards and electric cars

Kids, teens, families and friends

      Timeless fashion and youthful trends

Our neighborhood shines blue by day

      With bright red specks merged away

Around this place we relax and roam

      Admiring more our neighborhood, our home

The past few years have brought about a renaissance in American neighborhoods with strong economies, healthy workforce and diverse communities. Nextdoor has done well to recognize this trend. The simplicity and elegance of Nextdoor's local community network provides a convenient and useful way for residents to stay in touch with their neighbors.

While most people intuitively understand the colloquial definition of "neighborhood", it is an intriguing exercise to research how neighborhoods manifest in spatial and temporal dimensions, along sociological, philosophical and cultural vectors. What makes a neighborhood? How exactly are neighborhoods defined and who defines them? Do the boundaries change over time? How does one find the information for the thousands of neighborhoods and communities across the US? Is there such a thing as a "neighborhood search engine"? Deep knowledge about neighborhoods can set the tone and context for businesses and brands trying to serve the needs of the local market.

As published in a recent blog post, the data scientists at LotaData have studied the composition, characteristics, trends and correlations across multiple location-based datasets to understand the physical attributes, the social structure and the digital fabric of local communities. The resulting profiles represent the active identity for each neighborhood, constructed from hundreds of geo-temporal variables, including:

  • Crowd-sourced Boundaries 
  • Fluidity of Geometry 
  • Local Places & Businesses 
  • Shops, Malls, Local Services 
  • Hyper-local Events & Activities 
  • Local Deals, Promotions, Classifieds 
  • Town Halls, Libraries, Councils 
  • Local Authorities, Police, Fire 
  • Residents, Age, Gender, Ethnicity 
  • Marital status, Families, Households 
  • Employment, Income & Occupation 
  • Education, Schools, Colleges, Scores 
  • Real Estate, Residential, Commercial 
  • Political Leanings 
  • Spiritual Preferences 
  • Accidents, Crimes, Natural Disasters 
  • Alerts, Advisories, Broadcasts 
  • Weather, and the Environment


Marketers and advertisers look at neighborhoods as a collection of unique people with distinct practices. Neighborhoods provide a sense for the types of audiences one can expect to find in the area, thereby influencing advertising campaign decisions and marketing budget allocations. The increased focus on location-based campaigns in 2016 is starting to put the spotlight back on our neighborhoods.

Neighborhood profiles, structured in the form of APIs, can make it possible for marketers to seamlessly search through unwieldy datasets, to unearth meaningful and actionable intelligence for designing hyper-targeted campaigns. As an example, it would be a marketer's dream to be able to “create and monitor geo-temporal zones, 100 feet in radii, around all café locations in neighborhoods that voted against Prop 8, with median home value above $400K and mean household income over $100K, with an average of 3 members per household, located near venues scheduled to host +5 music events with projected attendance of +1000 per event, with <5% chance of precipitation, over the next 16 weeks”. To enable complex queries like these, LotaData has published the detailed profiles for +6800 neighborhoods and made them accessible and searchable through the Neighborhood Search API and the Visual Explorer.


LotaData's technology platform is powered by Elastic. Our machine learning recipes extract, collect, cleanse, de-dupe, structure, classify and publish geo-temporal data from tens of thousands of sources, across +80 countries, +35,000 cities and towns, +680,000 local deals and promotions, +145,000 performers, musicians, actors, athletes, teams, +1,800,000 venues, +9,000,000 local events, activities, and an ever growing list of businesses and brands. The speed, scalability and performance of Elasticsearch are the foundation for our platform. The massively distributed architecture with cluster resiliency allowed us to scale horizontally. To get the full story, find our Co-founder and CTO Taras Shkvarchuk at Elastic{ON}16, offer to buy him a Cortado and he'll spill the beans faster than you can collect.


Neighborhood insights are of tremendous value to businesses and brands looking to reach the right audience at the right place, the right time and in the right mood. The New York Times recently wrote about a national brand with an effective local campaign that significantly exceeded the industry average for mobile ad engagement. Campari America profiled neighborhoods that have a high density of bars in order to reach liquor consumers aged 21 to 34. Their mobile ad used location targeting to present consumers in bars with a discount for a ride-sharing service, when they performed specific actions on their smartphones. Regional brands and local businesses can also benefit from targeted campaigns, both digital and physical, powered by neighborhood intelligence. Local restaurants and bars could earn more business by staying open past regular hours to serve audiences exiting late evening music, theater, comedy or sporting events in the neighborhood. Music brands like Fender could accelerate the direct-to-consumer strategy by promote their instruments at local guitar meetups, jam sessions and music workshops. Food brands like Pinkberry could monitor neighborhood profiles including youth ratios, ethnicities, income levels and the microclimate to determine the ideal locations to launch their new froyo flavors, Green Tea and Pina Colada.


Many of us might recollect the amusing online survey by ABC News that revealed Democrats trust brands like Starbucks and Jeep, while Republicans prefer Dunkin Donuts and BMW. While most brands will not take a stand on partisan issues, it turns out that political preferences and the hundreds of other neighborhood attributes can provide deep insights into consumer purchase decisions. Marketers, advertisers, brands and businesses know this well. As the country gears up for a long-drawn-out election campaign, you can expect marketers to comb through the cities and towns across the US, zoom into your red, white and blue neighborhood, and allocate their green based on the intensity of red or blue.


Apu, also an acronym for "Accelerated Processing Unit", was born in technology and is rumored to have been conceived in a silicon fab near Springfield, where he went on to found Kwik-E-Cart, an AI-powered sharing economy services company of unicorn stature, that anticipates and predicts your needs and your wants, with antimatter propelled drones delivering to your doorstep prior to even the thought having occurred to you. On a more formal note, Apu Kumar has over 18 years of technology experience in Silicon Valley across software, hardware and cloud services for the mobile, PC and TV ecosystems. Apu's proven record in conceptualizing new products, accelerating adoption and establishing new markets, led him to his newest adventure, LotaData, a company that provides geo-temporal intelligence and location context for businesses and brands. Prior to that, Apu was the SVP and Chief Deal Hacker at BlueStacks (including GamePop), a profitable startup venture-funded by Andreessen-Horowitz, Redpoint, Ignition, Radar, Helion, Presidio, Intel, Samsung and Qualcomm. BlueStacks achieved 1+ billion mobile app downloads with 100+ million MAUs. Apu has also held senior leadership roles at iconic technology brands like Hewlett Packard, Phoenix Technologies (acquired by HP), CNET.com and mySimon (acquired by CNET). Apu is a dynamic and resourceful executive, a motivational leader and manager of cross-functional and cross-geographical, high-performing teams. He is passionate about mobile gaming, wearables, iot, location-based services, health and fitness. While he is not globe-trotting and deal-hacking, you are likely to run into Apu on the trails and tennis courts in the San Francisco Bay Area. Apu has a Master’s degree in Engineering from Stanford University and a Bachelor’s degree in Engineering from the University of Mumbai.

LinkedIn: https://www.linkedin.com/in/apukuma

Twitter: @lotadata @apu_kumar

Email: apu@lotadata.com