Abstract
Geospatial data inform decision makers. An economic model that involves application of spatial and temporal scientific, technical, and economic data in decision making is described. The value of information (VOI) contained in geospatial data is the difference between the net benefits (in present value terms) of a decision with and without the information. A range of technologies is used to collect and distribute geospatial data. These technical activities are linked to examples that show how the data can be applied in decision making, which is a cultural activity. The economic model for assessing the VOI in geospatial data for decision making is applied to three examples: (1) a retrospective model about environmental regulation of agrochemicals; (2) a prospective model about the impact and mitigation of earthquakes in urban areas; and (3) a prospective model about developing private–public geospatial information for an ecosystem services market. Each example demonstrates the potential value of geospatial information in a decision with uncertain information.
Highlights
Geospatial information is a form of infrastructure for decision-making that supports many societal activities
Starting with examination and analysis of available data, we propose a model of economic change as a result of technological innovation
moderate resolution land imagery (MRLI) contributes to decision making as a revision of regional land use by reassigning crops to parcels by changing the corn/soybean distribution in space and time that will maximize the value of agricultural production and preserve potable groundwater resources
Summary
Geospatial information is a form of infrastructure for decision-making that supports many societal activities. An Inductive Retrospective Model—Environmental Regulation of Agrochemicals: Geospatial Data Provide Information for Regional Environmental and Health Policy Decisions This example is a retrospective model for estimating the benefits of moderate resolution land imagery (MRLI) as open access geospatial data. Combining the MRLI and a groundwater vulnerability model is used to forecast critical levels of NO3− concentration in an aquifer over time These data can inform decisions to regulate land use for mitigating a potentially loss of drinking water by imposing constraints on the farmer’s practices that might reduce their output. The baseline in this example is for the farmer and regulator to rely on the current and historical groundwater well records and inspections of ad hoc and variable data of agricultural land uses
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
