地理学中的大数据：新范式，新研究

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"《Thinking Big Data in Geography New Regimes, New Research》是关于地理学领域大数据研究的一本书，由Jim Thatcher, Josef Eckert和Andrew Shears编辑。这本书探讨了大数据如何改变了地理学的研究范式和方法。" 在当今信息化社会，大数据已经渗透到各个学科，包括地理学。这本书的核心内容可能涵盖了以下几个重要的知识点： 1. **大数据的定义与特征**：大数据通常指的是那些在体积、速度和多样性方面超出传统数据处理能力的数据集合。在地理学中，这可能包括卫星图像、GPS轨迹、社交媒体数据等。 2. **地理大数据的来源**：书中可能会讨论各种地理大数据的来源，如遥感卫星、GIS（地理信息系统）数据、移动设备定位数据、交通流量数据等。 3. **新的研究范式**：随着大数据的引入，地理学的研究方法发生了变革。例如，空间分析、时空挖掘、模式识别等技术的应用，使得地理学家能够以更全面、动态的方式理解地球表面的现象。 4. **大数据的挑战与机遇**：可能涉及数据获取、存储、处理和分析的复杂性，以及隐私和伦理问题。同时，大数据也提供了前所未有的机会，如预测自然灾害、城市规划优化、环境监测等。 5. **案例研究**：书中可能包含实际案例，展示如何利用大数据解决具体的地理问题，比如气候变化模型的构建、城市热岛效应的分析、人口流动模式的研究等。 6. **数据可视化与决策支持**：大数据在地理学中的一个重要应用是数据可视化，它可以帮助研究人员和决策者更好地理解复杂的空间关系。此外，大数据分析的结果可以为政策制定提供有力的支持。 7. **技术工具与平台**：可能会介绍用于处理和分析地理大数据的各种工具和平台，如QGIS、ArcGIS、Google Earth Engine等。 8. **未来趋势**：作者们可能还会讨论地理大数据研究的未来方向，包括技术进步、跨学科合作以及数据驱动的科学方法的持续发展。 9. **学术伦理与版权**：由于数据来源多样，书中可能会涉及数据所有权、版权和使用许可的问题，这是大数据研究中必须面对的法律和道德议题。通过这本书，读者可以深入理解大数据在地理学中的作用和影响，同时也能够了解到如何利用这些数据进行创新性的科学研究。

Introduction xv

user license agreement (). Despite empirical evidence that these

oen lengthy and legally framed documents are not read, they remain a

key site at which individuals become dispossessed from the data they cre-

ate.



One common example of this moment is found in the iTunes terms

and conditions statement, upon which agreement is required by Apple

iPhone owners before they can access the iTunes interface necessary for

the device’s online use at least, without hacking or “jailbreaking” the

device, a process requiring additional knowledge and skills to complete.

e latest form of the iTunes terms and conditions statement comprises

some , words; by other measures it is nearly six times the length of

the Magna Carta and consists of nearly ve times as many words as the

U.S. Constitution. Consent to this document, and to participating in the

big data project, becomes the price of entry for most persons.

Even beyond basic use of mobile and digital technologies, many activ-

ities that were previously beyond the purview of data collection have

become sites for the production of not only “big” but also “small” forms

of data (see chapter  for an exploration of the dierences). Commercial

outlets, such as supermarkets and pharmacies, increasingly have mandatory

loyalty card memberships, which track and correlate purchasing habits,

while many public spaces have become sites for algorithmically monitored

video recording.



In such systems it becomes questionable as to whether

individuals can opt out of data collection, with their options reduced to

boycoing whole swaths of everyday life or to participating in regimes

of data collection.



With these circumstances in mind it is worth asking

again to what degree any given piece of data was knowingly and willingly

contributed. Many of the chapters in this volume address this question

in some way, from David Retchless’s look at how informed, volunteered

visualizations may inuence climate science to Mahew Kelley’s look

at the new forms the digital divide has taken in recent years. Questions

of hidden bias and how to address it appear in many chapters, such as

Fekete’s and Ricker’s. Together these chapters illustrate how the possibility

of avoiding the seemingly ever- expanding reach of big data, small data,

and other new mapping technologies has become increasingly tenuous

as issues of consent and participation blur.

xvi atcher, Shears, and Eckert

Hope and Fear in Data

e ambiguity in the question of consent signals the more crucial, wider-

reaching consequences of big and spatial data projects. Framing tech-

nology’s role in the world as a double- edged sword runs through writing

on the topic. Technology enables and constrains actions in the world: it

destabilizes labor relations while opening new sites for surplus capital

absorption, and in this way technology is “both friend and enemy.”



Kingsbury and Jones suggest that the Frankfurt school’s view on tech-

nology can be read as a broad dialectic between hope for technology’s

role as liberator and fear of its domination of everyday life, and we wish

to extend this heuristic for this book.



We do not make a claim upon the

theoretical orientation of any individual author nor reduce their claims to

some structural box in which they can be placed. Rather it is to suggest

that if we are to take seriously big data as a specic instantiation of tech-

nology in the world, then it is only natural to see outcomes that leave us

both hopeful and fearful.

As such, the chapters in this book engage these topics from a variety of

perspectives, probing the ways in which data functions in the world, while

avoiding falling into hard technological determinism. Lingel (chapter )

describes “watching big data happen” and how in moments of hubris

alternative voices and visions can be swept away by powerful normative

forces. Retchless (chapter ) explores the distinct potential for these

self- same technologies to improve the public’s understanding of climate

change. Our point here is not to oer an either/or proposition, wherein

big and spatial data projects will either change the world for the beer or

enroll us within oppressive regimes of quantication; rather we seek to

oer these and other possibilities as a not only... but also dynamic that,

while proving more dicult to resolve conceptually, oers space for a

wide diversity of approaches.



As Graham illustrates in the nal chapter, while the specic terms may

change, the underlying processes of neither big data nor new web- mapping

technologies are going to disappear. Unless we want to ignore the project

in its totality, we must ask important critical questions about this new

paradigm, not only who is being le out and who is being exploited but

Introduction xvii

also how new sources of data “help us to answer the big questions that

we need to ask.” It is impossible, or at least irresponsible, to be blind to

for- prot motivations behind many new spatial data and big data rms;

however, it is similarly irresponsible to not consider, propose, and practice

alternatives that take up the banner of justice, equity, and social good

as their core objective. at innate tension runs, by design, through the

chapters of this book.

Seeing the Qualitative

Further epistemological tensions within big and spatial data arise from

the nature of data and its analysis. Contemporary big data practices have

oen been undergirded by a resurgent pseudopositivism that accepts

quantication uncritically.



With respect to social media and geode-

mographic data, big data comes to represent the individual who cre-

ated it, reducing the complexity of human experience to a limited set

of purportedly quantitative variables.



As illustrated above, this desire

to reduce the world to numbers has its roots in much older tendencies

toward statistical analysis within capitalist modernity.



However, that is

not to suggest quantitative analysis and methodologies have no place in

knowledge production. rough this book we instead want to suggest a

need to see the qualitative nature within quantitative data. Even where

the rigor of statistical analysis has produced empirical, robust results

working with new, large, heterogeneous data sets, we want to suggest a

moment of reection on the construction of code, data, and algorithms.

e chapters of this text oer dierent insights into how to question the

qualitative within the quantitative. Ricker argues in chapter  that the anal-

ysis and visualizations of big data are always inevitably “inuenced by...

epistemolog[ies]” of the researchers involved. By seeing the qualitative

within the quantitative, Ricker demonstrates how the rigor of qualitative

methodologies can strengthen datacentric analysis. Chapters like Fekete’s

exploration of Foursquare check- ins in Kansas City (chapter ) and Jung

and Shin’s work on Washington State election tweets (chapter ) aempt

to directly bridge the supposed gap between quantitative and qualitative,

exploring the limits at which grounded theory, qualitative methods, and

xviii atcher, Shears, and Eckert

quantitative big data meet. Ultimately there is no single answer here, or

elsewhere, as to the exact limits of qualitative and quantitative methods.

In this book the authors grapple with the limits of big data and the impor-

tance of understanding where the qualitative, aective moments of human

life are constrained by moments of classication of digital information.

Organization of the Volume

Chapters in this volume have been organized into ve sections, the divi-

sions of which are based loosely upon how the author(s) approach big

data and geography and how the chapters engage with the themes extrap-

olated above.

What Is Big Data and What Does It Mean to Study It?

In chapter  Kitchin and Lauriault explore a new vision for critical data

studies () in geography and how such an epistemology would provide

signicant insight into a myriad of signicant questions critical research-

ers should be asking about the provenance of big data. Building from the

work of Dalton and atcher, Kitchin and Lauriault forward the data

assemblage an agglomeration of factors that contribute to data’s creation,

circulation, and application, including technological, social, economic, and

political contexts crucial to framing the data at hand as a unit for critical

analysis.



e authors draw on Foucault and on Hacking as theoretical

guideposts for unpacking these assemblages as a starting point for ,

providing illustrations of how such assemblages have impacts far greater

than the sum of their parts.

Recognizing the wide- scale adoption of big data as an important data

source for computational studies within the social sciences, O’Sullivan

(chapter ) calls for an adjustment in the epistemology used to under-

stand these data from examining the novelty of the data themselves to

a beer use of computational frameworks when leveraging such data to

explain the world. Citing the ascendancy of certain big data methodolo-

gies that value data volume over all else, the author demonstrates how a

specic form of computational social science has accompanied this rise,

one based on identication of variables and the establishment of math-

Introduction xix

ematical relationships between them. Demonstrating the inadequacy of

such approaches, O’Sullivan explores approaches that beer recognize

and represent processes. He concludes by arguing for the geographic

application of approaches taken from complexity science, a eld that has

been largely ignored in geography since the s and s.

Methods and Praxis in Big Data Research

Citing several concerns with the big data paradigm, chapter  authors

Sieber and Tenney forward a counterargument to the notion that bigger

big data is always beer by exploring the problematic binary used to

dierentiate big data from “small data.” While remaining “agnostic about

the value of big data and data- science methodologies,” the authors urge

caution about couching all data studies within the buzzy and evolving

big data epistemology. Moving through various potential denitions of

big and small, the authors explore how the very constitution of data as

an object of research shis across scales. To Sieber and Tenney some of

the shortcomings of a perspective prioritizing the size of big data can be

solved by continuing to acknowledge the legitimacy of small data and

small data– driven studies, even within the big data paradigm.

Another proposal for rening the big data paradigm comes from the

author of chapter . In her chapter, Ricker convincingly argues that big

data, and especially spatial data, is mostly qualitative in nature. Despite

the tendency of many big data researchers and practitioners, driven by

the intimidating size of such data sets, to focus exclusively on quanti-

tative readings and analyses, Ricker suggests that aspects of qualitative

methodologies, including acknowledgment of subjective approaches to

issues of reexivity and positionality, can provide a rigor largely missing

from current big data projects.

Empirical Interventions

Recognizing the limited focus of many spatial data studies in terms of the

acquisition of massive data sets for quantitative analysis, chapter  authors

Jung and Shin argue that a hybrid qualitative- quantitative approach to such

work allows for researchers to minimize inherent issues with such data sets

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