Startups and the Future of Spatial Analysis

Mark Altaweel


With increased data availability and ever more sophisticated analyses available for researchers and analysts alike, it might be no surprise that many new startup companies have emerged in the last few years that are beginning to take advantage of massive volumes of data and the increase sophistication required for analysis.

One recent company is Descartes Labs, located in New Mexico, which specializes in integrating various sensor data, including weather, satellite, and high resolution imagery from sources such as unmanned vehicles. The company focuses on bringing not only the data to customers but also allowing quick analysis of large volumes of data. Effectively the company tries to bring ‘Big Data’ analytical capabilities to its customers in simple way. Analysis such as crop yield forecasting, hazard monitoring, and urban development planning are among the areas where the company could help customers focus. The co-founder of the company, Mark Johnson, recently stated something like 100 terabytes of new data are made accessible each day that their customized tools provide analytical access to.[1] What this highlights is that there are both opportunities but challenges for companies to develop more rapid ways to effectively use such constantly growing data.

Given the explosion of social media data and hand-held devices, a large focus is on now bringing that data to common retailers, where spatial analysis includes using spatial statistical and artificial intelligence (AI) techniques so that estimating potential crowd volumes or even the type of community surrounding retail outlets are possible. Taking advantage of social media data is among the largest areas where spatial analysis companies have focused. For instance, this has been the focus for companies such as Spatial AI.[2]

Mapping geosocial data. Source:
Mapping geosocial data. Source:

Other companies have been more focused in niche industries, such as construction and engineering. Autodesk is one company where it has mostly focused on structural engineering, architectural design, and other engineering applications. Software includes analysis of structures, such as weight distribution, that compare to required building standards and indicate where violations could be evident from a proposed, hypothetical design.[3] Given the hype about autonomous driving, companies such as Here are beginning to take the lead in creating AI-based analysis and application of navigation that is applicable for vehicles. This includes using sensors to precisely calculate and position a vehicle to a high standard that push the limits of sensor accuracy and rapid AI techniques. [4]

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HERE's work focuses on detecting 3D road features by applying deep convolutional networks to its HERE True LiDAR in order to capture 3D geometry and surface reflectivity to produce centimeter accurate 3D features.
HERE’s work focuses on detecting 3D road features by applying deep convolutional networks to its HERE True LiDAR in order to capture 3D geometry and surface reflectivity to produce centimeter accurate 3D features. Image: HERE

From these brief examples, it is evident that spatial analysis will grow in integrating ever larger and more diverse datasets in a variety of fields and application areas. Researchers have stated that the growth of the Internet of Things (IoT) will be the biggest challenge for ‘Big Data’ application that also includes spatial-temporal analysis, where devices and data acquisition from these devices have to account for volume, velocity, and variety of data The inclusion of hand-held or micro devices as data acquisition platforms makes the challenge harder because data growth often outfaces storage and analytical capabilities. In fact, the only way to continue to deal with this exponential growth is machine-learning solutions, such as that used for spatial analysis for autonomous driving. Analysis has to be able to quickly integrate new data as they emerge, as the rapidity of change also affects the utility of single-time analysis and time sensitive solutions become critical for different applications.[5]

The other main challenge is security, where the record has been mixed at best for spatial analytical software companies and other Big Data firms. With high volumes of data, it becomes easier to acquire information that could be particularly sensitive for analysis. Security for clients analysis is also important in competitive markets. Identity-based broadcast encryption (IBBE) has been one proposed solution that forces users to have specific-set encryption details that can be tagged to individual identities not easily transferable to different devices. While this could be a potential solution, security for data will likely need continuous evolution as new ways are found to exploit discovered weaknesses in data protection.[6]

What the spatial analysis startups and new companies highlight is the growing need for advanced spatial analysis for everyday solutions that ranges to different fields. These startups will face increased challenges as they deal with the velocity, volume, and variety of data, with security and dynamism of data being among the biggest challenges. Nevertheless, solutions, particularly involving AI, are being developed and continue to demonstrate there will likely continue to be greater industry growth in advanced spatial analytics.


[1]    For more on Descartes Lab, see:

[2]    For more on Spatial AI, see:

[3]    For more on Autodesk, see:

[4]    For more on Here, see:

[5]    For more on the challenges faced by spatial analysis and the Big Data problem, see:  Bariki Leelavathy, A. T. (2017). Big Data  Analytics: Challenges, Research Issues, Tools   And   Applications A Survey.

[6]    For more on identity-based encryption used for securing large or Big Data, see:  Huang, Q., Yang, Y., & Fu, J. (2018). PRECISE: Identity-based private data sharing with conditional proxy re-encryption in online social networks. Future Generation Computer Systems, 86, 1523–1533.

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About the author
Mark Altaweel
Mark Altaweel is a Reader in Near Eastern Archaeology at the Institute of Archaeology, University College London, having held previous appointments and joint appointments at the University of Chicago, University of Alaska, and Argonne National Laboratory. Mark has an undergraduate degree in Anthropology and Masters and PhD degrees from the University of Chicago’s Department of Near Eastern Languages and Civilizations.