A Research Agenda for Spatial Analysis
Levi John Wolf, Alison Heppenstall, and Rich Harris
A Research Agenda for Spatial Analysis (RASA) is a collection (approximately) 5000 word perspectives on the future of spatial analysis published by Edward Elgar Books.
Back to Berry and Marble’s early Spatial Analysis handbook, handbooks in quantitative geography or spatial analysis have provided a venue to take a snapshot of our field as it is. And, while they are produced often, it is both an honor and a rare opportunity to contribute to these Handbooks. More rare than this, however, are opportunities to envision a future for spatial analysis.
In this edited volume, A Research Agenda for Spatial Analysis (RASA), authors stake a claim on the future of their part of spatial analysis, writing a “manifesto” for the future of the area. These short chapters (approximately 5000 words, about as long as a Progress in Human Geography “Progress Report”) are intended to be clear, opinionated, and speculative perspectives on the future of a concept or a domain of practice in spatial analysis.
The edited volume is split into two halves. In the first part, senior researchers reflect on the future of “big ideas” or “concepts” that have been central to their research agenda, as well as any classics that weren’t: concepts, books, or papers that have been particularly influential in your thinking but that are not quite as prominent as the ought to be. The second half of RASA contains essays from a variety of scholars who write about their chosen domain of expertise.
The book is due to publish in Winter, 2022.
Table of Contents
Essentially Contested Concepts in Geography
Geographical analysis involves a wide set of academic domains that all share a set of central concepts, concerns, and practices. Chief among these are space, place, scale, and pattern. More recently, reproducibility has re-emerged as a core concern for geographic analysis. This section of A Research Agenda will present how these concepts are conventionally represented and applied. Further, this section will discuss how these concepts may evolve as the discipline itself changes over time.
Click on the titles below to expand the chapter abstracts.
"Space," David O'Sullivan (Te Hereng Waka | Victoria University of Wellington)Geographers often use “space” and “geography” interchangeably. However, the concept of space is fairly poorly defined. In quantitative application, space is invariably represented through a set of abstractions, chosen largely for computational or representational convenience. This chapter will discuss the main concept for what space in spatial analysis has generally been. It will also discuss possible futures for how representations of spatial processes could be made more useful or powerful or for how the space in may change.
"Place," Tuuli Toivonen (University of Helsinki)For spatial analysis, place is usually the other side of the coin from space. Intimately connected to the concept of “Region” in regional science and critically at issue in many urban applications, place is a critical component of spatial analysis. This chapter will discuss how place has been used in quantitative geography, as well as the new ideas and practices arising in platial study.
"Scale," Stewart Fotheringham (Arizona State)Although geographers are perhaps most familiar with scale as a cartographic concept relating the size of objects in an image or on a map to their real-world dimensions, the notion of scale has much broader interpretations across a vast range of disciplines. Scale is a fundamental concept in spatial analytical research, in whatever discipline such research is promulgated, and has been the cause of much concern ever since the genesis of the field. The notion of scale in spatial analysis incorporates such diverse issues as the geographic frame of reference for the analysis, the definition of the areal units for which spatial data are reported, and the geographic domain over which processes vary. All three conceptual views of scale are also sources of problems for the spatial analyst. This chapter shows how the three problems are related and can be better understood by moving away from the traditional view that they are a product of data properties and recasting them in terms of the properties of processes.
"Pattern," Trisalyn Nelson (UC Santa Barbara)Geographers often describe the “structure” of a geographical process as exhibiting pattern when attempting to represent geographical processes in statistical models. But, what each geographer means, exactly, by pattern has historically been contentious, and some patterns are more frequently analyzed, conceptualized, and problematized than others. This chapter will discuss different notions of pattern in geographical analysis, and take a perspective on how pattern might evolve in new areas of geographical study.
"Reproducibility," Chris Brunsdon (Maynooth University)Reproducibility is a longstanding concern in geographical analysis. In the last half century, though, the increasing complexity and pervasiveness of computers has changed how geographers think of reproducibility. What, if anything, should be the same from context to context? How can we justify geographical knowledge if results are indeed context-dependent? This chapter will discuss the conceptual issues with reproducibility that have affected the domain in the past, and provide ideas on how the concept of reproducibility may change as geographical analysis evolves.
Agendas for Domains of Practice
Geographical analysis, as a domain in itself, involves many different modes of inquiry. Often, these quantitative analyses are distinguished by their methods, but also sometimes by their aims, purposes, and focus of study. In this section of A Research Agenda, authors will discuss the current state-of-the-art of their field. They will also give a perspective on the future of these areas.
Click on the titles below to expand the abstracts.
"Geographic Data Science," Daniel Arribas-Bel (Liverpool) & Anita Graser (AIT)Abstract Coming Soon!
"Generative Modelling," Clementine Cottineau (TU Delft)
Generative modelling has entered the field of geography and spatial analysis some 35 years ago, under three main forms: CA (cellular automata), LUTI (land-use transport interaction models), and ABM (agent-based models). Besides allowing spatial analysts and geographers to build operational models for transportation and planning, it has represented the opportunity to take causal inference from the traditional (statistical) analysis of empirical models to the design of causal mechanisms simulated in virtual environments. It is a major epistemological shift, whose scientific contribution still needs advocacy in the wider community of geographers and spatial analysts. In parallel, the expansion of both computing power and available data have made it easier for isolated teams and individuals to build ad-hoc models fit for their specific research questions, leading to a cacophonous development of generative models unrelated to one another.
Since the introduction of generative modelling in geography and spatial analysis, hundreds of models have been developed: all may have been wrong, but some were surely useful. However, most models built over the years have been abandoned, their program either inoperable with today’s technology or lost entirely. Apart from a few classics (such as MatSim or SLEUTH), dozens of models of urban growth, of transport networks, of negotiation between actors, etc. have been developed separately, been exploited for a few years, published and then forgotten or reinvented, leading to the YAAWN syndrome (O’Sullivan et al., 2016).
In 35 years, this should strike us as vastly wasteful of ideas, time and energy. By that time, my hope for geosimulation is that we have found a convincing way to build on each other’s work so as to demonstrate the benefits of generative modelling for geography and spatial sciences: 1/ by ensuring the sustainability of models and the traceability of contributions, 2/ by relying on modular combinations of theoretically-grounded mechanisms (or “building blocks”) and 3/ by making model building and model evaluation transparent and painless.
In this chapter, I will lay out possible paths towards this goal and the expected impacts in terms of geographical theory development and scientific reproducibility. Reproducibility and reusability of model building blocks are especially important for us geographers and spatial analysts since we are concerned with generic processes (segregation, polarisation, urbanisation, etc.) embedded in singular spatiotemporal contexts (e.g. region x during period p, street y at time t). Knowing the variation of model structures associated with varying spatiotemporal contexts can contribute to the identification of both universal properties of social phenomena and the peculiarities of places. In that way, generative modelling done right can both contribute to a new form of causal inference and to the larger programme of social sciences: the simultaneous search for generalised explanations of social phenomena and recognition of the uniqueness of historical events.
"Visualization," James Cheshire (UCL)Abstract Coming Soon!
"Integrated Science of Movement," Urska Demsar (St. Andrews)Recent years have brought unprecedented advances in movement data acquisition and movement is now being analysed in such disparate disciplines as human geography, computer science, transportation research and movement ecology. While mathematical concepts for movement analysis are the same across all disciplines, there still remains a barrier for sharing methods, despite similar research objectives. Recently attempts have been made to bridge this gap and establish an overarching interdisciplinary science, the Integrated Science of Movement. This essay introduces this initiatve. In the first part, I provide an interdisciplinary overview of contemporary movement analytics. In the second part, I discuss challenges arising from both scientists’ views on interdisciplinary work and from new developments, including new sensors and types of data. I further outline how spatial analysis, as part of Geographic Information Science (GIScience) and with its focus on space-time, could play an integral part in this exciting new interdisciplinary science.
"Inequalities and Segregations," TBCAbstract Coming Soon!
"Spatial Optimization," Qunshan Zhao (University of Glasgow)Abstract Coming Soon!
"Economic Systems & Program Evaluation," Max Nathan (UCL)Abstract Coming Soon!
"Earth Observation," Michelle Stuhlmacher (DePaul)Earth Observation—the gathering of information about the planet via in-situ and remote sensing technologies—has allowed us to look at the Earth in new ways. The view from above provided by aerial and satellite imagery has been an essential source of spatial information in areas where we have very little data, such as out in the ocean or remote reaches of the rainforest. With the advent of big data, however, there has been a shift toward utilizing Earth Observation (EO) technologies in areas where data are abundant. Urban areas, in particular, have large quantities of spatial data that often remain unleveraged or underutilized. This chapter focuses on the future of EO in urban areas: a technological and analytical frontier that has the potential to improve the lives of over half of the world’s population and the environment we all rely on.
"Machine Learning," Stephen Law, Shen Yao, and Zhong Chen (UCL)The field of artificial intelligence have expanded rapidly in recent years permeating to many application domains including medical science, climate science, finance, and geography. In Geography, these advances have culminated in the new subdomain of GeoAI which was driven by advances in deep learning, optimised computational tools and the availability of large scale spatially embedded data. In this chapter, we will describe a couple of techniques in deep learning for analysing image, text, graph and point data. We will then provide some projections on the near future for the topic, including increasing application of deep learning on traditional geographical problems, open data practices as well as cross disciplinary engagements and teaching. We envisage the use of deep learning in geography will continue to grow leading to hopefully new spatial insight, knowledge and methods to be discovered in the future.
"Causal Inference," Gareth Griffith (University of Bristol)"
The most interesting and useful questions in spatial science are often truly about cause and effect: if we do X what will happen to Y. If spatial data scientists care about better understanding society and designing policies to improve it, we must concern ourselves with why spatial processes function as they do, not simply how. We propose doing so requires greater focus on research design, not ever more arcane spatial methods or datasets.
There is no doubt that ongoing developments in spatial data analysis and computational capacity have enabled a breathtakingly interdisciplinary scope of spatial enquiry. However, the causal questions they tend to answer are very limited, perhaps just shy of being deemed “mostly pointless”. Rather than continuing to lament this, we outline a manifesto for why spatial analysts should care about causal inference, and provide guidelines for the converted.
In essence, understanding causal relationships requires us to create, assume or discover random variation. It is not usually practical or ethical for spatial analysts to create random spatial assignment, so we commonly rely on assuming randomness (via exchangeability) using statistical adjustment. Better yet, we may search for so-called natural or quasi-experiments to remove the need to rely on statistical assumptions. We do so by identifying situations where someone (sometimes ‘God’; but more often a bureaucrat) has, without realising, cast geographically informed dice which allow us to investigate spatial processes.
We demonstrate the benefits of explicitly considering causal research questions, rather than avoiding the “C-word” (and furtively hoping readers infer it regardless). Throughout, we provide examples of studies which have explicitly exploited such designs, from considering the impact of neighbourhood homicides on exam results, to the impact of localised restrictions on COVID-related mortality. We use these studies as a roadmap to identify the most fruitful future research avenues for spatial analysts.
Information for Authors
Information on Elgar’s “Author Hub” is comprehensive and clear, but a few things to note are below.
What is the production schedule?
We would like first drafts of chapters by 31 January, 2022. Review & edits would be provided in spring of 2022, with revisions due by early summer 2022.
Can I use figures/tables?
Yes! Greyscale images are allowed (and count as 500 words). Submit high-resolution figures at 300DPI alongside the manuscript as separate files, and do not include the figures in the manuscript directly.
Tables should be set within the text where they ought to appear in the print, and include their own title and caption.
Should I have an “example” of the future?
This is not necessary! Your chapter can contain any mixture of theoretical arguments, empirical demonstrations, or literature-based conclusions. However, it is useful to stress: your chapter is not a miniature literature review or a proof of concept: it is a 5000-word manifesto!
Is there any example chapter to pattern my submission on?
No, as we are interested in ensuring that submissions’ creativity is both fostered and supported. However, past entries in the Elgar Research Agendas series may be informative for authors seeking this kind of inspiration: