How Are Points of Interest Collected and Classified?

What a point of interest is

A point of interest is any place that shapes daily life around a home. The grocery store you would actually walk to. The cafe on the corner. The transit stop, the park, the pharmacy. POIs are the answer to the question every home consumer asks after they like a property: what is around here?

On their own, a pile of business listings is not very useful. The value comes from collecting them comprehensively, organizing them so they can be compared and filtered, and connecting them to how people actually evaluate a place. That is the work behind the data.

How POIs are collected

Local Logic’s data foundation spans more than 250 million addresses across the US and Canada, built on over 500 billion location data points drawn from public, proprietary, and partner sources. POIs are part of that foundation. Rather than relying on a single feed, Local Logic blends multiple sources so coverage stays broad and current across both countries, then normalizes everything into one consistent structure. That normalization is what lets a coffee shop in Denver and a coffee shop in Montreal be treated the same way, so the data behaves predictably wherever a buyer is looking.

How POIs are classified

Every POI is sorted into a category, then labeled with brands and tags for finer detail. That three-part structure is what makes the data filterable and genuinely useful rather than a flat list.

Categories

Local Logic organizes POIs into 28 categories covering the full range of daily needs. Grouped for readability, they look like this:

Brands and tags

Categories answer what kind of place this is. Brands and tags answer which one and what sort. Brand labels identify chain businesses, so a site can surface or filter by a specific brand. Tags capture finer distinctions, such as whether a business is independent or a chain, or the cuisine type of a restaurant. Together they let a website show, for example, independent cafes within walking distance rather than every coffee shop in the area.

How classified POIs power Location Scores

POIs are not just for map pins. They are the raw signal behind several Location Scores. When Local Logic measures access to groceries, restaurants, or transit, it is measuring access to the relevant POIs: finding every one within a travel time and weighting each by distance. Because the POIs are categorized consistently, the scores built on them are consistent too. Clean inputs are what make a walkability or transit score mean the same thing in every market.

Querying POIs: around a point or within an area

POIs can be retrieved two ways: around a coordinate, using a location and a radius, or within a defined geography like a neighborhood. Results come back with their category, brand, and tag labels, so a product team can filter to exactly what matters for a given experience, whether that is a listing page showing nearby amenities or a neighborhood page summarizing what an area offers.

Beyond the raw list of places, you can also get derived metrics on them, such as how many of a category are nearby and how far the closest ones are. That is often the insight people actually want: not every cafe in the area, but how many cafes are within a short distance, or the distance to the nearest grocery store. This is what makes proximity analysis, the what-is-within-reach question, straightforward to build.

Schools: a dedicated dataset

Schools sit in their own dataset rather than among general POIs, with separate coverage for the US and Canada because the two countries publish school information differently. The goal is to answer one practical question at the level of a single address: what are the school options here?

The dataset includes primary and high schools with details like names, websites, educational levels, grade ranges, languages of instruction, and special programs, along with school board information. It also includes proximity metrics, distance and walking time, and catchment-area boundaries, so a site can show which schools serve a given address. School search works by coordinate and radius (a default of five kilometres, extendable to twenty-five) or against custom boundaries.

Local Logic can display school rating data for individual schools where that data is available from approved sources. These ratings may appear alongside other school details, such as name, grade range, language of instruction, programs, distance, walking time, and catchment information. The ratings are sourced from external datasets and are presented as informational attributes about specific schools, not as Local Logic’s own evaluation or endorsement.

One distinction is worth being precise about: when school rating data is displayed, it describes an individual school, not a neighborhood. Local Logic does not grade neighborhoods by school quality, does not use schools as a proxy for the character of an area, and does not independently rank or evaluate schools. It presents sourced facts about specific schools and lets home consumers draw their own conclusions.

Why the method matters

Good POI data is invisible when it works and glaring when it does not. Show a buyer a grocery store that closed two years ago, or miss the park across the street, and the whole experience loses credibility. Comprehensive collection, consistent categories, and accurate brand and tag labels are what make the rest of the platform trustworthy: the scores, the neighborhood profiles, the proximity analysis on a listing page. It is also objective by design. POIs describe what is actually there and how far away it is, measured facts that a real estate company can stand behind.