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Benefits of crowd-sourced GPS information for modelling the recreation ecosystem service.

ABSTRACT: Modelling cultural ecosystem services is an enduring challenge, raising issues about the integration and spatialization of immaterial values and benefits, and their contingency on local preferences. Building on the Recreation Opportunity Spectrum framework, we present a novel methodology for assessing the recreation service using GPS tracks downloaded from crowd-sourced websites: the Grelou model (Georeferencing REcreation in Local OUtdoors), here applied to the Grenoble living area (French Alps). GPS tracks revealed the complete spatial extent of visitor presence and enabled modelling visitation networks for ten recreation activities with great spatial accuracy, thus providing a spatial estimate of recreational multifunctionality-expressed as the sum of networks. After coupling track networks with landscape preference and proximity factors, Grelou assessed the recreation service as a combination of opportunity and preferences, and identified recreation hotspots of different profiles such as aroundoor leisure or outdoor sport. We performed an online survey among local sports associations using an interactive map to select districts visited by respondents (~1000 people). The declared visitor presence for recreation purposes was highly spatially congruent with Grelou outputs (R2 = 0.89). Detailed analysis of responses on selection criteria for recreationists validates our choice of critical factors underlying both the recreation opportunity potential and the expected visitation frequency over the whole study area. We also analyzed outputs of the InVESt recreation model against the same visitation explanatory factors. Differences between the two models allowed us to pinpoint biases and weaknesses in the InVESt recreation modelling framework based on crowd-sourced photographs. By making use of an increasingly available data source (GPS tracks), Grelou offers a standardized and flexible way to assess the recreation service associated with multiple recreation practices. Its high spatial accuracy supports the analysis of spatial relationships with other ecoystems services and the integration of recreation into environmental assessments, land management and planning.

SUBMITTER: Byczek C

PROVIDER: S-EPMC6188625 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Benefits of crowd-sourced GPS information for modelling the recreation ecosystem service.

Byczek Coline C Longaretti Pierre-Yves PY Renaud Julien J Lavorel Sandra S

PloS one 20181015 10

Modelling cultural ecosystem services is an enduring challenge, raising issues about the integration and spatialization of immaterial values and benefits, and their contingency on local preferences. Building on the Recreation Opportunity Spectrum framework, we present a novel methodology for assessing the recreation service using GPS tracks downloaded from crowd-sourced websites: the Grelou model (Georeferencing REcreation in Local OUtdoors), here applied to the Grenoble living area (French Alps ...[more]

PMID: 30321173

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Project description:IntroductionTo mitigate the COVID-19 pandemic and prevent overwhelming the healthcare system, social-distancing policies such as school closure, stay-at-home orders, and indoor dining closure have been utilized worldwide. These policies function by reducing the rate of close contact within populations and result in decreased human mobility. Adherence to social distancing can substantially reduce disease spread. Thus, quantifying human mobility and social-distancing compliance, especially at high temporal resolution, can provide great insight into the impact of social distancing policies.MethodsWe used the movement of individuals around New York City (NYC), measured via traffic levels, as a proxy for human mobility and the impact of social-distancing policies (i.e., work from home policies, school closure, indoor dining closure etc.). By data mining Google traffic in real-time, and applying image processing, we derived high resolution time series of traffic in NYC. We used time series decomposition and generalized additive models to quantify changes in rush hour/non-rush hour, and weekday/weekend traffic, pre-pandemic and following the roll-out of multiple social distancing interventions.ResultsMobility decreased sharply on March 14, 2020 following declaration of the pandemic. However, levels began rebounding by approximately April 13, almost 2 months before stay-at-home orders were lifted, indicating premature increase in mobility, which we term social-distancing fatigue. We also observed large impacts on diurnal traffic congestion, such that the pre-pandemic bi-modal weekday congestion representing morning and evening rush hour was dramatically altered. By September, traffic congestion rebounded to approximately 75% of pre-pandemic levels.ConclusionUsing crowd-sourced traffic congestion data, we described changes in mobility in Manhattan, NYC, during the COVID-19 pandemic. These data can be used to inform human mobility changes during the current pandemic, in planning for responses to future pandemics, and in understanding the potential impact of large-scale traffic interventions such as congestion pricing policies.

Project description:The importance of ecosystems for supporting human well-being is increasingly recognized by both the conservation and development sectors. Our ability to conserve ecosystems that people rely on is often limited by a lack of spatially explicit data on the location and distribution of ecosystem services (ES), the benefits provided by nature to people. Thus there is a need to map ES to guide conservation investments, to ensure these co-benefits are maintained. To target conservation investments most effectively, ES assessments must be rigorous enough to support conservation planning, rapid enough to respond to decision-making timelines, and often must rely on existing data. We developed a framework for rapid spatial assessment of ES that relies on expert and stakeholder consultation, available data, and spatial analyses in order to rapidly identify sites providing multiple benefits. We applied the framework in Madagascar, a country with globally significant biodiversity and a high level of human dependence on ecosystems. Our objective was to identify the ES co-benefits of biodiversity priority areas in order to guide the investment strategy of a global conservation fund. We assessed key provisioning (fisheries, hunting and non-timber forest products, and water for domestic use, agriculture, and hydropower), regulating (climate mitigation, flood risk reduction and coastal protection), and cultural (nature tourism) ES. We also conducted multi-criteria analyses to identify sites providing multiple benefits. While our approach has limitations, including the reliance on proximity-based indicators for several ES, the results were useful for targeting conservation investments by the Critical Ecosystem Partnership Fund (CEPF). Because our approach relies on available data, standardized methods for linking ES provision to ES use, and expert validation, it has the potential to quickly guide conservation planning and investment decisions in other data-poor regions.

Dataset Information

Benefits of crowd-sourced GPS information for modelling the recreation ecosystem service.

Publications

Benefits of crowd-sourced GPS information for modelling the recreation ecosystem service.

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