Mapping Problems

I have been interested in location

And I have made maps that attempt to share:

But the more I learn about location

The less satisfied I am with how we have come to map;

And the more focused I become on how we might transform maps.

My work outside of CDP has centered on mapping and spatial analysis — engaging web maps mostly in projects that span scales of the body, to the city, to the continent. These projects have worked through methods of digital humanities, forensics, and spatial data science. And over the last year, my coursework at GSAPP has been focused both on expanding the tools, techniques, and methods I know how to use; and on importantly on engaging with theory, history, and the aesthetics of spatial practice as a designer.

Location Intelligence

The tools, data, use cases, and places digital maps describe, and the companies and actors that develop them, are all elements that constitute a kind of platform, often describing itself as engaged in “location intelligence”. I’m interested in describing contemporary, digital location intelligence platforms through a systems lens.

How and what do we know about location, place, and space? What is location intelligence?

A chatbot with a knowledge base rooted in cultural geography:

A chatbot with a knowledge base rooted in a location intelligence company's API documentation:

"Place" is understood as a center of meaning constructed by experience. It can be known not only through the eyes and mind but also through the more passive and direct modes of experience, which resist objectification. To know a place fully means both to understand it in an abstract way and to know it as one person knows another. At a high theoretical level, places are points in a spatial system, while at the opposite extreme, they are associated with strong visceral feelings. In the middle range of experience, places are constructed out of elements like distinctive odors, textural and visual qualities in the environment, seasonal changes, and more. Places can range in scale from small entities like a fireplace or home to larger constructs like a city or nation. They are all centers of meaning to individuals and groups.

Venue Name: The name of the venue or place
Venue ID: A unique identifier for the venue.
Latitude and Longitude: The geographical coordinates of the venue.
Address: The physical address of the venue.
City, State, Postal Code, and Country: Location details to further pinpoint the venue.
Categories: The type or category of the venue (e.g., restaurant, museum, park).
Verified: Indicates if the venue's details have been verified.
Stats: Data about the venue such as check-ins count, users count, and tip count.
URL: The website associated with the venue.
Price: Information about the price range of the venue or its services.
Rating: The rating given to the venue by users.
Hours: The operating hours of the venue.
Photos: Links to photos associated with the venue.
Tips: User-generated tips or reviews about the venue.
Menu: If applicable, a link to the venue's menu.

There are many possible functions of location intelligence platforms, but they primarily fall under the directive of selling information about places, their specific locations, characteristics, how they are used, and how they relate to other places. The corporations involved in developing these platforms also tend to use language that dips into the humanistic and experiential, but it is not clear that “unlocking the power of places and movement, etc etc” has objectives beyond producing and circulating money across certain geographies.

location, a unit within a hierarchy of units in space

A specific place or position in space

I do not want to take prevailing ideas about what constitutes “location intelligence” and its attendant systems or stakeholders as given. I’m excited about re-imagining them and shifting the relationship between stakeholders in the system, and the relationship between those stakeholders and the places (landscapes, locales, etc) that are the substrate and subject of location intelligence platforms.

Location intelligence refers to the process of deriving meaningful insights from geospatial data. It involves analyzing locations and spatial data to solve specific problems, make informed decisions, or gain a strategic advantage. This can be applied in various industries, from retail (determining the best location for a new store) to transportation (optimizing delivery routes) to urban planning (analyzing population growth and infrastructure needs). Location intelligence tools often utilize Geographic Information Systems (GIS) and other data visualization platforms to present data in a comprehensible and actionable manner.

People come to know about places and locations through a combination of direct sensory experiences, emotional connections, and abstract understanding.

Places of Interest

The proliferation of location data has changed place and people’s experience of it. Commonly referred to as Points of Interest (POI), these data work through web maps and spatial media to become deeply embedded within the tools people use to navigate, understand, and situate themselves within the world around them.

And while the sense and experience of places that comes along with being a body is rich, complicated, subjective, and impossible to fully reduce or capture into quantified terms alone, POI and their prevalence work to collapse notions of place, the experiences people have of them, the way people move through and between them into something that can be commodified, measured and analyzed and “ground truthed” computationally, with ever increasing frequency, remoteness, and automation. This is a form of “location intelligence”: the measurement and valuation of location and locale in terms of how someone may spend time and money there. This intelligence is the stock, or product, of platforms.

Corporations trade in these data, towards this intelligence, to influence and inform land use decision-making, design, strategy. These points are ingested by algorithms to fix rent prices and parameterize master plans. Now there is no place that escapes this capitalized lens on landscape. Place is consumed for consumers. This process is a kind of reinforcing feedback loop – places become quantified and understood through location intelligence platforms and then in turn change, as subjects of the logics of the systems capturing and disseminating information about them in very particular ways. If using the data and using the places they describe are goals of location intelligence platforms, then use is a key factor in frequency, delays, and resilience of these platforms and their inputs and outputs.

Positions, Relations, Layers

Most of the design features and elements of location intelligence platforms that are locked-in trace their roots in the long history of European cartography. Which synthesizes and abstracts information in such a way that flattens place, controlling for risk, uncertainty, time. These maps, charts, and cadastres make space navigable and legible as a site for investment, extraction, ownership, “improvement” or “use”. Location intelligence platforms follow these logics, which also flow from centuries-old mapping practices through GIS technologies to their contemporary embeddedness within nearly all of the digital devices and networked tools in wide use today.

There is a deep body of critique within the discourses of critical and counter cartography that looks at the locked-in features of maps, and proposes creative alternatives that inflect cartographic output with different value systems, politics, and standpoints. From questioning the default projections, units and visual, representational systems of maps; to critiquing who makes them, uses them, and their motives. Authorship has been a site of intervention for critical cartographers, who have created many mapping projects that center user participation and input over default, top-down data creation or dissemination practices. These maps ask, who gets to say and share experience of a place? And thereby take part in (re)creating it?

It follows that for location intelligence corporations, and the stakeholders that work for and are invested in their platforms, lock-in is essential. Place needs to be rendered into data, with standards and set features; which are in turn categorized, related, and databased. This pipeline of measurement and quantification of place increasingly relies on user input, or data derived from personal/individual use. But while products are built on massive collections of check-ins, reviews, geocodable traces, and pings, users are not given autonomy, credit, or financial remuneration for their contributions to a platform.

Much like the questions being asked of authorship and AI, which scoops up and re-represents massive amounts of information created under different authorship paradigms, the question of user authorship in location intelligence systems is complex and could be reimagined towards different economic and political outcomes (how credit, money, power, and place making capacity are distributed across stakeholders). Cultural geography would also say that landscapes are authored — what does this discourse on place making have to offer a critique of AI and location intelligence systems?

Projection

When is it necessary to craft data, to make or remake them, alongside the process of collecting, downloading, and gathering? I’m interested in ways of intervening in and expanding notions of “location intelligence”, in a digitized sense, and that will involve intentionally shifting what is considered default in spatial data. It will involve appropriating existing and widely utilized datasets, reading and analyzing them against the grain. It will involve looking to difficult- and /or impossible-to-map collections of information and regarding them as data, to test, explore, and learn from what they can and cannot measure, quantify and represent. It will involve making data and systems to archive and disseminate them that engender new political, aesthetic, and spatial possibilities. What if spatial data and the maps that represent them reflected the experiential, qualitative, and humanistic dimensions of place and space?

I’m inflecting my approach to data gathering with the work of Yani Loukissas and Johanna Drucker (among other data and media scholars, and historians of science and technology) who emphasize the importance of considering “data settings” (Loukissas) and that data are always taken or captured from a particular context, never given (Drucker). Data are only ever situated, partial, and constructed bits of information and are never whole, representative, or containing innate knowledge or factual bearing on their own. An approach to data gathering should reflect that.

Exploratory visualization resonates with my practice, and the goal of developing alternatives to location intelligence platforms and systems. To me, this means not attempting to explain or predict using these data, but to question the underlying defaults of the data themselves, and explore towards alternatives. It means first learning about how the data are made, their limitations, and then developing arguments with them, against them, around them. Questioning the default settings with which these datasets are often visualized should also be a goal of my practice, and that’s where pushing the limits of the tools and methods used will be helpful, breaking down the rigidity of their particular data models to reveal something else. This process deploys exploration as a form of analysis — something more inductive.

I am also asking: can you learn through creative synthesis of seemingly disparate collections of information? By bringing these data together with others, there will be even more to explore and reveal about what constitutes digitized location intelligence.