![]() 2021), PySAL (Rey and Anselin 20), xarray (Hoyer and Hamman 2017), or sf (Pebesma 2018), has enabled highly specialized research applications-including the analysis and modelling of urban spatial systems.Ĭoncerning cities, these new tools are finding fertile ground in the study of functional aspects of urban life, where we now rely on abundant data on, for example, urban populations coming from census records or social networks, or environmental performance, thanks to arrays of various sensors both on the orbit (Drusch et al. ![]() In this field, the quick emergence and maturation of a new generation of spatial data software ecosystems in both Python and R (Rey 2019 Bivand 2020), represented by GeoPandas (Jordahl et al. This trend manifests in the rapid growth of quantitative geography and geographic data science (GDS), fuelled by the development of new computational tools and availability of (big) open data. The latter goes in the opposite one, repeatedly suggested by Kwan and Schwanen ( 2009) or later Derudder and van Meeteren ( 2019) in their call for a “common language” and onboarding the critical insights stemming from quantitative approaches. ![]() 2009 Gahegan 2020), represents one direction of quantitative science. The former, based on the notion that science can be data driven to the point where knowledge and theory can be retrieved from data only (Hey et al. Furthermore, it supports a shift towards quantitative geography, allowing its evolution in the direction of the fourth paradigm of science as well as its closer integration with critical geography. ![]() This is expanding our ability to interrogate and understand the world around us, delivering new evidence-based knowledge to guide action. The wholly reproducible method is encapsulated in computational notebooks, illustrating how modern GDS can be applied to urban morphology research to promote open, collaborative, and transparent science, independent of proprietary or otherwise limited software.Īcross all fields of science, the expanding availability of openly accessible data and constant development of new tools, technologies, and platforms is making possible the generation of new theories and testing of new analytical methods and processes. Results show a trajectory of change in the scale and structure of urban form from pre-industrial development to contemporary neighborhoods, with a peak of highest deviation during the post-World War II era of modernism, confirming previous findings. In this paper, we use the open source Python ecosystem in a workflow to illustrate its capabilities in a case study assessing the evolution of urban patterns over six historical periods on a sample of 42 locations. Simultaneously, the Python ecosystem for GDS is maturing to the point of fully supporting highly specialized morphological analysis. This inherently restricts transparency and reproducibility of research. Although quantitative approaches to morphological research are finding momentum, existing tools for such analyses have limited scope and are predominantly implemented as plug-ins for standalone geographic information system software. ![]() Yet there is limited application in urban morphology-a science of urban form. The recent growth of geographic data science (GDS) fuelled by increasingly available open data and open source tools has influenced urban sciences across a multitude of fields. ![]()
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