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In this chapter, the authors contribute toward building a better understanding of farmers’ responses to behavioral drivers of land-use decision by establishing an alternative analytical procedure, which can overcome various drawbacks suffered by methods currently used in existing studies. Firstly, our procedure makes use of spatially high-resolution data, so that idiosyncratic effects of physical environment drivers, e.g., soil textures, can be explicitly modeled. Secondly, we address the well-known censored data problem, which often hinders a successful analysis of land-use shares. Thirdly, we incorporate spatial error dependence (SED) and heterogeneity in order to obtain efficiency gain and a more accurate formulation of variances for the parameter estimates. Finally, the authors reduce the computational burden and improve estimation accuracy by introducing an alternative generalized method of moments (GMM)–quasi maximum likelihood (QML) hybrid estimation procedure. The authors apply the newly proposed procedure to spatially high-resolution data in England and found that, by taking these features into consideration, the authors are able to formulate conclusions about causal effects of climatic and physical environment, and environmental policy on land-use shares that differ significantly from those made based on methods that are currently used in the literature. Moreover, the authors show that our method enables derivation of a more effective predictor of the land-use shares, which is utterly useful from the policy-making point of view.

To understand the importance of coordination and collaboration for ROV teams, let us examine some of the typical tasks that ROV operators might be required to perform (Cooke & Shope, 2004; Gugerty, DeBoom, Walker, & Burns, 1999). To do so, we will use the members of a U.S. Air Force Predator crew as an example. The team consists of three members: an Air Vehicle Operator (AVO) who pilots the aircraft, a Payload Operator (PLO) who operates the surveillance equipment, and a Data Exploitation, Mission Planning, and Communications Operator (DEMPC) who is responsible for mission planning. In the course of a mission, the AVO is responsible for the take off and landing of the aircraft. Because they fly the aircraft from a remote location, AVOs are generally required to use visual input from a camera mounted on the nose of the aircraft to guide their flight. Once in the air, the PLO can operate cameras and sensors mounted on the belly of the plane to gather information. The DEMPC, who is in contact with the upper echelons of the organization, provides the AVO with the desired heading and the PLO with target coordinates.

DoD accidents are classified according to the severity of injury, occupational illness, and vehicle and/or property damage costs (Department of Defense, 2000). All branches of the military have similar accident classification schemes, with Class A being the most severe. Table 1 shows the accident classes for the Army. The Air Force and Navy definitions of Class A–C accidents are very similar to the Army's definition. However, they do not have a Class D. As the total costs of some Army UAVs are below the Class A criteria ($325,000 per Shadow aircraft; Schaefer, 2003), reviewers have begun to add Class D data into their analyses (Manning, Rash, LeDuc, Noback, & McKeon, 2004; Williams, 2004).

Satellite images can be a powerful source of data for analyses of conflict dynamics and social movements, but sociology has been slow to develop methods and metadata standards for transforming those images into data. We ask: How can satellite images become useful data? What are the key methodological and ethical considerations for incorporating high-resolution satellite images into conflict research? Why are metadata important in this work? We begin with a review of recent developments in satellite-based social scientific work on conflict, then discuss the technical and epistemological issues raised by machine processing of satellite information into user-ready images. We argue that high-resolution images can be useful analytical tools provided they are used with full awareness of their ethical and technical parameters. To support our analysis, we draw on two novel studies of satellite data research practices during the Syrian war. We conclude with a discussion of specific methodological procedures tried and tested in our ongoing work.

Do social movement organizations increase the supply of a public good? We address this question by investigating the role of generalist social movement organizations and technology-focused organizations for the development of the electric vehicle (EV) charging infrastructure in California from 1995 until 2012. We find that increases in the membership of Electric Auto Association (EAA) chapters in the cities of California enhanced the number of EV charging stations set up in each city. Our analyses also show that the organizational diversity of the environmental movement spurred the growth of EAA membership but did not directly increase the establishment of charging stations.