Éditeur(s) : HAL CCSD Wiley
Résumé : International audience
1. The Before-After Control-Impact Paired Series (BACIPS) design distinguishes natural spatial and temporalvariability from variation induced by an environmental impact (or intervention) of interest. BACIPS is a powerfultool to derive inferences about interventions when classic experimental approaches (e.g. which rely on spatialreplicates and random assignment of treatments) are not feasible or desirable. Previously applied BACIPSdesigns generally assume that effects are sudden, constant and long-lived: that is, that systems exhibit ‘stepchanges’in response to interventions. However, complex ecological interactions or gradual interventions maycreate delayed and/or progressive responses, potentially impeding the reliability of classic (step-change) analyses.2. We propose a novel approach, the Progressive-Change BACIPS, which generalizes and expands the scope ofBACIPS analyses.We evaluate the relative performance of this approach using both simulated and real data thatexhibit step-change, linear, asymptotic and sigmoid responses following an intervention.We quantify the statisticalpower and accuracy of the Progressive-Change BACIPS under varying initial population densities, intensityof spatial sampling, effect sizes and number of sampling dates After the intervention.3. We show that Progressive-Change BACIPS identified the correct model among the set of candidate modelsunder most conditions and led to accurate estimates of the parameters that were used to generate the simulateddata. When data were sparse, and the dynamics complex, simpler (more parsimonious) models were favouredover the more complex models that actually generated the simulated data. Application of the Progressive-Change BACIPS to existing data sets from the literature led to strong support for specific models (over alternatives)and led to more specific inferences than possible under the classic BACIPS approach.4. The Progressive-Change BACIPS proposed here is more flexible than the original BACIPS formulationbecause the data are used to inform the form of the final model, rather than having the form of the modelimposed on the data. This leads to better estimates of the effects of environmental impacts and the time-scalesover which they operate. As a result, the Progressive-Change BACIPS should be applicable to a wide range ofstudies and should help improve investigation of time-dependent effects. R code to perform Progressive-ChangeBACIPS analysis is provided.
ISSN: 2041-210X

hal-01416668
https://hal-univ-perp.archives-ouvertes.fr/hal-01416668
DOI : 10.1111/2041-210X.12655

Éditeur(s) : HAL CCSD
Résumé : For a decade, distance-based methods have been largely employed and improved in the field of spatial economics. Such tools are very powerful to evaluate accurately the spatial distribution of plants or retail stores for example (Duranton and Overman, 2008; Jensen and Michel, 2011). In the present paper, we introduce a new statistic measure based on distances to evaluate the spatial concentration of economic activities. As far as we know, the m function is the first relative density function proposed in the economic literature. This tool completes the typology of distance-based methods recently drawn up by Marcon and Puech (2014). By working on several theoretical and empirical examples, we prove the advantages and the limits of the m function to gauge the spatial structures in spatial economics.
https://hal.archives-ouvertes.fr/hal-01082178

hal-01082178
https://hal.archives-ouvertes.fr/hal-01082178
https://hal.archives-ouvertes.fr/hal-01082178/document