Valuing Spatio-Temporal Information

The valuation of data is a long time interest, have consulted with large companies on the topic.  Lately about automotive connections.  Technical detail on the topic at the link.

Computing Value of Spatio-temporal Information
By Heba Aly, John Krumm, Gireeja Ranade, Eric Horvitz
Communications of the ACM, September 2020, Vol. 63 No. 9, Pages 85-92
10.1145/3410387

Location data from mobile devices is a sensitive yet valuable commodity for location-based services and advertising. We investigate the intrinsic value of location data in the context of strong privacy, where location information is only available from end users via purchase. We present an algorithm to compute the expected value of location data from a user, without access to the specific coordinates of the location data point. We use decision-theoretic techniques to provide a principled way for a potential buyer to make purchasing decisions about private user location data. We illustrate our approach in three scenarios: the delivery of targeted ads specific to a user's home location, the estimation of traffic speed, and the prediction of location. In all three cases, the methodology leads to quantifiably better purchasing decisions than competing approaches.

1. Introduction
As people carry and interact with their connected devices, they create spatiotemporal data that can be harnessed by them and others to generate a variety of insights. Proposals have been made for creating markets for personal data1 rather than for people either to provide their behavioral data freely or to refuse sharing. Some of these proposals are specific to location data.6 Several studies have explored the price that people would seek for sharing their GPS data.5, 13, 9 However, little has been published on determining the value of location data from a buyer's point of view. For instance, a Wall Street Journal blog says10:

"What groceries you buy, what Facebook posts you 'like' and how you use GPS in your car:
Companies are building their entire businesses around the collection and sale of such data. The problem is that no one really knows what all that information is worth. Data isn't a physical asset like a factory or cash, and there aren't any official guidelines for assessing its value."
We present a principled method for computing the value of spatiotemporal data from the perspective of a buyer. Knowledge of this value could guide pursuit of the most informative data and would provide insights about potential markets for location data.

We consider situations where a buyer is presented with a set of location data points for sale, and we provide estimates of the value of information (VOI) for these points. Because the coordinates of the location data points are unknown, we compute the VOI based on the prior knowledge that is available to the buyer and on side information that a user may provide (e.g., the time of day or location granularity). The VOI computation is customized to the specific goals of the buyer, such as targeting ad delivery for home services, offering efficient driving routes, or predicting a person's location in advance. We account for the fact that location data and user state are both uncertain. Additional data purchases can help reduce this uncertainty, and we quantify this reduction as well.

In the next section, we introduce a decision-making framework with a detailed analysis of geo-targeted advertising. We focus on the buyer's goal of delivering ads to people living within a certain region. We show that our method performs better than alternate approaches in terms of inferential accuracy, data efficiency, and cost. In Section 3, we apply the methodology to a traffic estimation scenario using real and simulated spatiotemporal data. We present our last scenario in Section 4, where we show how to make good data-buying decisions for predicting a person's future location. ... 
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