公共/商业地点类型的地理位置分类？（用于从邮政地址映射）

发布于 2024-12-26 00:43:48 字数 1792 浏览 3 评论 0原文

市场研究、广告或零售行业是否有标准或广泛使用的分类法来对公共或商业场所进行分类？我们希望将公共或商业地点（例如学校、医院、工厂、办公园区等）的邮政地址映射到位置分类（例如“学校”、“医院”等）。如果可能的话，我们希望使用现有的分类法，而不是发明自己的分类法。

进行地址->分类映射是一个相关但独立的问题。目前，我们只是在寻找一种分类法，并且假设我们将自己进行映射，尽管如果出色的商业地理位置映射服务使用特定的分类法，我们可能会更喜欢该分类法！

以下是有关我们用例的更多详细信息：

我们的客户在办公园区、学校、医院、机场等公共或商业场所内经营小型食品店。我们提供算法驱动的销售建议，这意味着我们帮助零售商选择在其销售区域销售哪些产品商店以及每种产品需要占用多少空间。

我们希望在算法中使用位置类型，因为在医院商店中卖得最好的产品与在高中或工厂中卖得最好的产品是不同的。我们知道每个地点的邮政地址。我们的大部分地点都在美国或加拿大，因此仅限北美的解决方案虽然并不理想，但也可以。

我们希望分类法看起来像下面的列表：

Office
   High Income (e.g. law/medical office)
   Low Income (e.g. Call Center)
Industrial
   Factory
   Repair/Maintenance Shop
Hospital
Hotel
School
   College
   Secondary School
   Middle School
   Primary School
   Other School (e.g. driving school)
Transportation
   Airport
   Bus Station
   Train Station
Stadium
Retail
   Mall
   Large Retail (e.g. department store)
   Small Retail
   Supermarket
   Other Retail
Religious (church, mosque, etc.)
Government (excluding schools)
   Prison
   Courthouse
   Other Government

如果有多个标准正在使用，那么是否有一个标准与广泛使用的、负担得起的服务相关联，用于将美国邮政地址映射到分类法中？

以下是我迄今为止发现的一些选项，尽管我不知道这些选项在地理定位市场研究中的使用量：

NAICS、GICS 和其他行业分类方案：不完全是我们所需要的，因为它们关注的是业务类型，而不一定是业务类型地方。像体育场这样的地方很难用这些分类法来建模。
Google 地方信息：该分类非常符合我们的需求。但我们无法使用谷歌的服务（不允许私人、商业用途），并且快速测试显示谷歌只收录了我们一半的地点。因此，如果对不同的分类法有更好的行业支持，那么使用谷歌的分类法是否有意义尚不清楚。

原文

Is there a standard or widely-used taxonomy in the market-research, advertising, or retail industry to classify public or commercial locations? We want to map from the postal address of a public or commercial location (e.g. school, hospital, factory, office park, etc.) into a taxonomy of locations (e.g. "schools", "hospitals", etc.). Instead of inventing our own taxonomy, we'd like to use an exisitng one if possible.

Doing the address->taxonomy mapping is a related but separate problem. For now, we're just looking for a taxonomy and we're assuming we're going to do the mapping ourselves, although if a great commercial geo-location mapping service uses a particular taxonomy, we'd probably favor that one!

Here's more details about our use case:

Our customers operate small food stores inside public or commercial locations like office parks, schools, hospitals, airports, etc. We provide algorithmically-driven merchandising recommendations, meaning we help retailers select which products to sell in their stores and how much space to devote to each product.

We want to use location type in our algorithms, because the products that sell best in stores in hospitals, for example, are different products from those that sell well in high schools or factories. We know the postal address of every location. Most of our locations are in the US or Canada, so a North-America-only solution is OK although not ideal.

We're hoping a taxonomy would look something like the list below:

Office
   High Income (e.g. law/medical office)
   Low Income (e.g. Call Center)
Industrial
   Factory
   Repair/Maintenance Shop
Hospital
Hotel
School
   College
   Secondary School
   Middle School
   Primary School
   Other School (e.g. driving school)
Transportation
   Airport
   Bus Station
   Train Station
Stadium
Retail
   Mall
   Large Retail (e.g. department store)
   Small Retail
   Supermarket
   Other Retail
Religious (church, mosque, etc.)
Government (excluding schools)
   Prison
   Courthouse
   Other Government

If there are several standards in use, then is there one tied to widely-used, affordable service(s) for mapping US postal addresses into the taxonomy?

Here's a few options I found so far, although I don't know how much these are used in geo-located market research:

NAICS, GICS, and other industry classification schemes: not exactly what we need because they focuses on the type of business, not necessarily a place. Places like stadiums are tough to model with these kinds of taxonomies.
Google Places: The taxonomy meets our needs pretty well. But we can't use Google's service (no private, commercial use allowed) and a quick test showed that Google only had entries half our locations. So it's not clear that using Google's taxonomy makes sense, if there's better industry support for a different one.

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