Economic Indicators

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The Experts below are selected from a list of 295536 Experts worldwide ranked by ideXlab platform

Laura Syms - One of the best experts on this subject based on the ideXlab platform.

Timothy A. Duy - One of the best experts on this subject based on the ideXlab platform.

Jeffrey A. Krautkraemer - One of the best experts on this subject based on the ideXlab platform.

  • Economic Indicators of resource scarcity: Comment☆
    Journal of Environmental Economics and Management, 1991
    Co-Authors: Scott Farrow, Jeffrey A. Krautkraemer
    Abstract:

    Abstract A recent article in this journal by Richard Norgaard [ J. Environ. Econom. Management 19 , 19–25 (1990)] concludes that the literature on Economic Indicators of resource scarcity is not a scientific endeavor. We argue the contrary by surveying two research literatures omitted by Norgaard but inspired by the resource Indicators literature and by investigating Norgaard's claim to have found a logical flaw. We conclude that the research on Economic Indicators of resource scarcity is a scientific endeavor and that it is a distinguishing feature of the sub-discipline of environmental and resource Economics.

Tanya Raterman - One of the best experts on this subject based on the ideXlab platform.

Joern Birkmann - One of the best experts on this subject based on the ideXlab platform.

  • Using OpenStreetMap Data and Machine Learning to Generate Socio-Economic Indicators
    ISPRS International Journal of Geo-Information, 2020
    Co-Authors: Daniel Feldmeyer, Claude Meisch, Holger Sauter, Joern Birkmann
    Abstract:

    Socio-Economic Indicators are key to understanding societal challenges. They disassemble complex phenomena to gain insights and deepen understanding. Specific subsets of Indicators have been developed to describe sustainability, human development, vulnerability, risk, resilience and climate change adaptation. Nonetheless, insufficient quality and availability of data often limit their explanatory power. Spatial and temporal resolution are often not at a scale appropriate for monitoring. Socio-Economic Indicators are mostly provided by governmental institutions and are therefore limited to administrative boundaries. Furthermore, different methodological computation approaches for the same indicator impair comparability between countries and regions. OpenStreetMap (OSM) provides an unparalleled standardized global database with a high spatiotemporal resolution. Surprisingly, the potential of OSM seems largely unexplored in this context. In this study, we used machine learning to predict four exemplary socio-Economic Indicators for municipalities based on OSM. By comparing the predictive power of neural networks to statistical regression models, we evaluated the unhinged resources of OSM for indicator development. OSM provides prospects for monitoring across administrative boundaries, interdisciplinary topics, and semi-quantitative factors like social cohesion. Further research is still required to, for example, determine the impact of regional and international differences in user contributions on the outputs. Nonetheless, this database can provide meaningful insight into otherwise unknown spatial differences in social, environmental or Economic inequalities.