This paper contributes to the emerging literature on the accurate and consistent definitions of cities by providing, for 53 African countries, a detailed comparison of the delineations of urban areas resulting from the application of two of the frontrunners among the new methods. We provide a detailed comparison of results from the application of the “degree of urbanization” approach (Dijkstra and Poelman 2014) with results from the "dartboard" (de Bellefon et al. 2021) approach for delineating urban areas.

This paper analyses paintings as expressions of their times and contexts. We exploit large repositories of paintings with time and location information and develop an interpretable convolutional network extracting the emotions conveyed through paintings. The geography described by emotions sheds light on economic development and structural changes experienced by those locations. We use the estimated mapping to predict political turmoil and economic change, and provide novel proxies for economic development, inequalities and uncertainty over time and across locations.

Extracting features such as buildings, land-use or transportation networks depicted in historical maps provides novel insights for urban and spatial economics. Using image segmentation techniques and random forest models, these features are extracted automatically using the spectral and spatial information available at the pixel level. Our methodology is successfully applied to a collection of 19th century maps covering mainland France, and proves robust to considerable amount spectral heterogeneity both across maps and within classes. 

This paper analyses the evolution of French urban areas from a historical perspective. Using building footprints extracted from collections of digitised historical maps covering mainland France in the 18th, 19th and 20th centuries, we define consistently urban areas and analyse their trajectories along the urban hierarchy. Our findings highlight increasing urbanisation with fewer and larger urban areas, consistent with agglomerations economies. Disaggregate analysis reveals significant heterogeneity, with the emergence, persistence, disappearance of urban areas.

This paper develops methods to estimate the unobserved knowledge networks from observed similarities in visual artworks. Deep neural auto-encoders have the ability to project high-dimensional data points such as images, into latent spaces where the underlying factors of variation are disentangled and the distances between points are well defined. This property can be exploited to derive meaningful measures of distance across artworks' styles and to quantify accurately similarity and by extension, novelty. Using large datasets of digitised historical paintings, we estimate the impact of artists' mobility pattern on knowledge diffusion across space and time.

This paper studies the economic geography of the North American fur trade between 1670 to 1870. European hat makers used felt made from beaver pelts to produce hats for the elite. With the Eurasian beaver nearly extinct, Europeans turned to North America, trading beaver pelts with Indigenous communities in exchange for iron goods and textiles. We introduce a computational spatial model that integrates a biological beaver growth model with detailed transport cost calculations across North America’s extensive river system, forming a "beaver gravity model". The model illustrates that the over-exploitation of beavers drove the westward expansion of the fur trade, simultaneously shedding light on spatial dynamics of Indigenous populations.

Existing data on building destruction in conflict zones rely on eyewitness reports or manual detection, which makes it scarce, incomplete, and potentially biased. We introduce a new convolutional transformer network architecture measuring destruction in high-resolution satellite images, while explicitly taking into account both the spatial and temporal structure of the data, while relying on comparison with pre-conflict images to increase predictive performance. Our approach allows measuring conflict intensity with unprecedented scope, resolution, and frequency.

With Stephan Heblich and Stephen J. Redding