Location Intelligence // Otherwise



I am focusing my time as a student of Computational Design Practices on studying digital geography and the mapping systems that have entered the everyday. I want to better understand the nature of these maps, what in the world they represent to us and what kind of future they’re engaged in developing.

I am specifically looking at location intelligence platforms to better understand how the products these companies sell are at work in the world. I am developing a critique of their platforms rooted in cultural geography, and I am proposing to develop an alternative platform that is based in humanistic notions of place and landscape, drawing on a large corpus of text sources. I am trying to scale the objectives of critical, participatory map making, using techniques adapted from natural language processing and the digital humanities.

How might developments in language processing and digital cartographic techniques be directed towards different forms of location intelligence, placemaking, and ways of knowing geography?

Explore

I am focused on the maps that travel with us on our phones; they can show us where we are in space; they show us what and, sometimes, who is around us; they show us how to navigate between points in a network of locations that they know about.



What else do they know about geography? [very little]

What else do they know about us? [perhaps a lot more then they let on]

These maps are much more than their interfaces and front end functionalities, they draw on elaborate stacks of web technology, backend systems and internet infrastructures. And while maps have always been the visual outputs of attendant spatial, technological, economic, and political systems, these mobile maps are unprecedented in the way they simultaneously deliver, represent and capture spatial data. The scope of their use is also unprecedented – more people, loading more map views, updated with ever greater frequency by a few corporations with the same or similar cartographic techniques, than ever before. These maps are active and animated sites of global information display, assembly, and collection.

Given the volume of information moving through– and being generated by– these mapping systems, a relatively new kind of platform company has emerged to mind the space between maps, their use, and the spatial data they rely upon. These platforms seemed to have converged around the term “location intelligence” to describe what they produce.

In their own words, location intelligence companies define their product as “the insight gained from visualizing and analyzing geospatial data. Layering location-specific data—such as demographics, traffic, environment, economics, and weather—on a map or dashboard that reveals unique insights. ” This is a very general description – but these companies have developed ways to structure and synthesize vast amounts of spatial data, integrate them within a web-based geographical information system, and deploy spatial analysis tools and models within a mapping environment for display and decision-making. It is often assisted by machine learning and increasingly connected to large language models. It seems there is more value to be extracted in deploying the information gleaned from map use in a perpetual feedback loop, towards further digital product development and restructuring and investing in physical space. This is the profitable and powerful tail end of the data captured and synthesized from everyday map use.




Location intelligence companies have adopted the lexicon of human geography, even if their products have a very thin relationship to the full breadth of geographic inquiry. One prominent product, and the center of my work, are Points or Places of Interest data or POI. At a minimum, POI are locations, mappable to coordinates, that map users can generally visit. They are most often retail stores, restaurants, parks or institutions, but can also represent events. Information about these locations can vary between datasets, but they generally reflect location, contact information, and function or place category (store, restaurant, landmark, atm, commercial or public place).

This is a very limited lens through which to view and represent the complexity and many scales of place. And those limitations are cause for concern given the prevalence, influence, and power that location intelligence products that produce POIs have in structuring the physical world and our relationships to it. The discourse on the differences between space, place, and representations of the two in geography is complex – what I am most interested in is recovering the experiential and difficult-to-map aspects of place within our digital systems, and trying to create alternate systems for relating to place, and representing them, at scale.

There are many excellent examples of artistic and counter cartographic engagement with place, that try to reconcile its relationship with maps. Falling under the category of participatory mapping, these projects operate at scales much smaller than the global purview of location intelligence platforms, for important reasons. They seek engagement and invite authorship – typically allowing users to place a point and log an experience, memory, opinion or vision on a map, which can in turn be viewed alongside others, producing something personal and specific but not always synthetic. These points are also places. Is it possible to look at existing sources, containing vast amounts of information about peoples’ relationship to place, to develop an understanding of place that is larger-scale, collective, but still nuanced? That would exist not as a product but as an entrypoint to developing different ways of relating to places in the digital?

Explain

[places of interest dataset]

I began to explore the possibilities of expanding notions of place in location intelligence systems, first by working with POIs themselves. Would it be possible to alter them, inflect them with more qualitative information, or suggest alternative ways of relating to them as places? Overture Maps Foundation Points of Interest [describe and sketch] I sourced the Overture Maps Foundation POI dataset, with 60 million places, 780 million buildings, 40 different languages and visualized aspects of it:

[corpus]

I also sought our sources for alternative, difficult or impossible to map places that reflect more ways that people relate to geography and imbue places with meaning. Instead of making a participatory map, I want to mirror the ways in which location intelligence platforms gather massive amounts of data, but will do so by creating a corpus from a vast array of english language sources. This will allow me to work creatively and ethically with different working definitions of place – and explore the geographies latent within texts – works of fiction, wikipedia, messageboards, newspaper and magazine articles, websites more broadly. Then: I am working on geoparsing a sample corpus, which matches text (entities) of mappable places, like cities, place names with coordinates, more conventional points of interest Then: Refining a Named Entity Recognition model for places of experience — e.g. home, fireplace, courtyard, road, hotel, etc. Where experience takes place and is expressed, described, and recounted in text (more aligned with digital humanities work). This work is very challenging and time consuming – but will yield texts tagged for all manner of place types, which in turn can be related to their context, to other places, to mappable locations and visualized. The corpus could be curated, updated to show change over time, or layered with other spatial data, bringing in weather, geological, political, and demographic data over time to explore overlap.

I will bring these data, the Points of Interest that I’ve altered, the corpus and resulting Places of Experience dataset that I will create from it, into an alternative Location Intelligence Platform. I’m going to propose the development of a few interventions that will mirror the structures of Location Intelligence products but alter them in meaningful ways. The result will be a constellation of experiments.

Propose

[Proposal: data structures]

The first is at the level of data structure and schema. Geographical thought in the digital sphere is now shaped by data formats, these have a history or lineage that is expressed through cartographic representation. POIs are represented in a database as discrete locations with fixed coordinates. But what about places that move, that are only places during a certain time of day, that straddle multiple scales and categories? I will experiment with fuzzy topological spaces and graph database formats to accommodate this. And I will borrow from natural language processing to restructure the Places of Experience data, namely around the concept of word embeddings – to see if places might be represented in spatial datasets as similarly embedded.



[Fuzzy graphs and fuzzy topological structures are significant in mathematics because they allow for a more nuanced understanding and representation of complex relationships, where the boundaries and connections between elements are not strictly defined but have degrees of association or relevance. This approach is especially useful in scenarios where traditional binary logic (true/false, yes/no) is insufficient to model the complexity of real-world situations. The use of fuzzy graphs in this context opens up new avenues for research and application in areas where ambiguity and partial truths are commonplace.]

[Embeddings: allows words with similar meaning to have a similar representation. bridges the human understanding of language to that which a machine can process Each word or phrase in the vocabulary is mapped to a vector in a continuous vector space. The position of a word in this multi-dimensional space is learned from the text and is based on the words that surround it. ]

[Proposal: Dissemination]

Location intelligence platforms control access to their data products, making them available in restricted settings like dashboards, or behind licensing, pricing tiers and credit structures. I will create and maintain an API that allows open access to the Places of Experience I am generating, and adopt a transparent approach to documenting the struggles of doing so – it will be expensive and time consuming and might be a messy process, but I hope to demonstrate how it can be done in the open source.

[Proposal: Representation and interaction]

Maps - representing place. What if web maps were data driven (by elements of the corpus) and dynamic?
I want to develop new user experience models for web maps, for instance, a plug-in that alters basemap defaults based on analyses or summaries of the corpus I’ve compiled, or
An interface that offers entrypoints into the underlying, related texts, working with the notion of place as assemblage.
I am also interested in exploring other ways to interact with maps and digital places – which might be ways to dialog with and relate to place differently, in chat for example, or through other ubiquitous web map interactions like search, routing, or geolocation.

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