People and Pixels Linking Remote Sensing and Social Science (1998) / Chapter Skim
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1 Linking Remote Sensing and Social Science: The Need and the Challenges
1 Linking Remote Sensing and Social Science: The Need and the Challenges
Pages 1-27
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From page 1... ...
This confluence of events sets the stage for social scientists to use remotely sensed data and for social scientists and remote sensing experts to collaborate.3 This volume examines the potential for such use. It offers some guidance for researchers and research sponsors in the form of reports of promising research, information on the state of the technology, and reflections on the challenges of linking social science and remotely sensed data.
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For example, while it seems almost self-evident that spatial propinquity must be a factor in the shaping of social networks, it is only recently that the spatial aspects of social networks have been receiving attention (Faust et al., 1997~. Relatively few social scientists outside the field of geography value the spatial explicitness that remotely sensed data provide, nor do the typical social science data sets contain the geographic coordinates that would facilitate linking social science data and remotely sensed data.
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The social utility argument posits that remote sensing becomes even more valuable to the extent that social scientists find it useful, and that efforts should be made to identify and overcome the barriers to making this happen. In addition, the contributions of social scientists might allow remote sensing experts to "see" landscape features in the remotely sensed data not previously apparent.
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We do not believe remote sensing will quickly revolutionize social science; rather, we suggest that some progress can be made by joining social science and remote sensing perspectives, techniques, and data. Hence, the majority of this volume consists of examples of the use of remotely sensed data mainly from space-based platformsin social science research.4 However, we do not want to be overly constrained by the present, so we also speculate about additional, as yet untried, applications of remote sensing to social science questions.
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First of all, remotely sensed data provide an alternative representation of geographical context to that given by maps. Maps always include the mapmaker's selection of what is important to represent, and remotely sensed data, though also imperfect representations of reality, have different biases.
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Even without migration, individuals can act to change their contextsa possibility that may be more easily uncovered when contexts are measured in interviews than when they are measured by remote sensing. Thus, theoretical care is needed when using remotely sensed data to supply contextual data for models of individual or household behavior.
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It may also be possible to study the effects of changes in agricultural commodity prices on cropping patterns and tillage practices by combining price data with remotely sensed data, and to improve understanding further by incorporating additional data on land-tenure systems or agricultural policies. Providing Additional Measures for Social Science Social scientists frequently use aggregated units of analysis: cities or towns, counties or districts, states or provinces, or countries.
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There have been some suggestions that remotely sensed data of fine spatial resolution might be used in statistical models to generate estimates of population counts. If that were possible, there would be numerous uses of such estimates.
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Thus remotely sensed data offer some potential for encouraging social scientists to think across levels of analysis and to develop theories that link these levels. An example is in the work of Moran and Brondizio documented in Chapter 5 which, starting from an anthropological and highly localized perspective, developed ways of examining land use in geographically disparate areas of Amazonia and thereby addressing regional-level questions.
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WHAT CAN SOCIAL SCIENCE DO FOR REMOTE SENSING? As noted earlier, to the extent that remote observations provide uniquely useful information for social research, these social science applications of remote sensing can be used to provide additional justification for the money spent on observational platforms and data management systems.
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As improved technical capabilities, collaboration with social scientists, and especially the linking of remotely sensed data with social data make remotely sensed data increasingly useful, new problems and conflicts may arise over the use of the data. Although there are legal precedents that limit privacy rights with respect to high-resolution aerial photography, the courts have not yet directly addressed questions of privacy and Fourth Amendment rights in the context of space-based remote observation (Uhlir, 1990~.
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The linkages that have occurred, including examples in this volume, involve social science data sets that are not yet in the public domain. Remotely sensed data that are not linked to social data are less likely to pose problems of confidentiality.
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HOW CAN REMOTE SENSING AND SOCIAL SCIENCE IMPROVE UNDERSTANDING OF HUMAN-ENVIRONMENT INTERACTIONS? An additional argument for better collaboration between remote sensing specialists and social scientists is that such collaboration has been necessitated by a new and important set of intellectual and practical problems: those related to understanding and controlling human impacts on the biophysical environment, as well as anticipating and responding to environmental impacts on humanity.
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describes the development of a famine early-warning system for Africa based on remote observations of drought phenomena, combined with an understanding of the social processes by which people adapt to drought. Generally, forecasts and status reports on food crop growth in drought-prone regions, derived mainly from remotely sensed data, are combined with ground-based data on patterns of human response to generate famine warnings.
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Researchers deciding to link social and remotely sensed data must make decisions about the appropriate level of aggregation. On the social side, ignoring temporal issues, the finest grain is an individual.
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Similar considerations apply to shopping patterns, social activities, and religious activities. Routine social science data collection efforts do not provide the capability to georeference these activities; further, tested and accepted methods for collecting such data do not exist.
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Consider, for example, linking firms to remotely sensed data in order to understand the influence of different types of firms on land-cover or land-use patterns. Should one use only the point locations of a firm's places of business, or should one also consider the commuting patterns of the firm's employees and the locations of its suppliers of raw materials?
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To date, there is no scientific association or journal for scholars who are integrating social science and remotely sensed data, and this lack of an institutional base is likely to impede the development of research at the intersection of the two fields. There are certainly examples of sessions at professional meetings that include papers incorporating both social science and remotely sensed data
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For teams working on projects that use both social and remotely sensed data, the most obvious publication outlets are ones that specialize in only one of the two fields. The peer review process in such journals involves the same problems already noted for the review of proposals: the social science reviewers are generally not competent to review the remote sensing components of the paper, and the remote sensing reviewers are generally not competent to review the social science.
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A straightforward but significant problem is to provide georeferencing for social data so as to link them to remotely sensed data, which are normally geocoded. Preexisting social statistics such as those collected by government agencies are typically coded at highly aggregated levels, such as political units.
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Researchers also face the problem of finding appropriate social data to match with remotely sensed data, or vice versa. NASA's support for the SEDAC is intended to address this problem, and an indicator of the SEDAC's success will be the extent to which researchers find it useful for locating the matching data sets they need.
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because this is currently the area of the most intensive research activity involving collaboration between social scientists and remote sensing specialists. Chapters 8 through 10 present applications to urban land-use issues, famine early warning, and public health that illustrate some promising frontiers for social scientific use of remotely sensed data.
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RINDFUSS AND PAUL C STERN 23 also be possible to link remotely sensed data on atmospheric trace gas concentrations to ground-based data on industrial activity in order to improve models that link human activities to their environmental consequences.
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C7 C7 ~C7 Two appendices are intended as resources for social scientists who are relatively unfamiliar with remotely sensed data. Appendix A provides a guide to numerous major sources of remotely sensed data.
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Among the studies we know that use remotely sensed data and social science data together, the vast majority use satellite data rather than aerial photographs. There are probably a number of reasons for this.
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9 Market research firms typically compile social data at much lower levels of aggregation than governments do in the United States, at the level of the postal zip code or even the zip-plus-four, which typically corresponds to a geographic area that encompasses the residences of a few dozen households. Some elements of these privately held data sets, such as data on consumer expenditures, are parallel to data collected by governments but available to clients at finer resolution.
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Wu 1994 Integrating Amazonian vegetation, land use, and satellite data. BioScience 44(5)
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