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Open AccessArticle

Earth Observation for the Implementation of Sustainable Development Goal 11 Indicators at Local Scale: Monitoring of the Migrant Population Distribution

1
Institute of Atmospheric Pollution Research (IIA), National Research Council (CNR), c/o Interateneo Physics Department, Via Amendola 173, 70126 Bari, Italy
2
Department of Civil, Environmental, Land, Building Engineering and Chemistry (DICATECh), Polytechnic University of Bari, Via Orabona 4, 70125 Bari, Italy
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(6), 950; https://doi.org/10.3390/rs12060950
Received: 21 January 2020 / Revised: 21 February 2020 / Accepted: 12 March 2020 / Published: 15 March 2020
(This article belongs to the Special Issue EO Solutions to Support Countries Implementing the SDGs)
This study focused on implementation of the Sustainable Development Goal (SDG) 11 indicators, at local scale, useful in monitoring urban social resilience. For this purpose, the study focused on updating the distribution map of the migrant population regularly residing in Bari and a neighboring town in Southern Italy. The area is exposed to increasing migration fluxes. The method implemented was based on the integration of Sentinel-2 imagery and updated census information dated 1 January 2019. The study explored a vector-based variant of the dasymetric mapping approach previously used by the Joint Research Center (JRC) within the Data for Integration initiative (D4I). The dasymetric variant implemented can disaggregate data from census areas into a uniform spatial grid by preserving the information complexity of each output grid cell and ensure lower computational costs. The spatial distribution map of regular migrant population obtained, along with other updated ancillary data, were used to quantify, at local level, SDG 11 indicators. In particular, the map of regular migrant population living in inadequate housing (SDG 11.1.1) and the ratio of land consumption rate to regular migrant population growth rate (SDG 11.3.1) were implemented as specific categories of SDG 11 in 2018. At the local level, the regular migrant population density map and the SDG 11 indicator values were provided for each 100 × 100 m cell of an output grid. Obtained for 2018, the spatial distribution map revealed in Bari a high increase of regular migrant population in the same two zones of the city already evidenced in 2011. These zones are located in central parts of the city characterized by urban decay and abandoned buildings. In all remaining city zones, only a slight generalized increase was evidenced. Thus, these findings stress the need for adequate policies to reduce the ongoing process of residential urban segregation. The total of disaggregated values of migrant population evidenced an increase of 44.5% in regular migrant population. The indicators obtained could support urban planners and decision makers not only in the increasing migration pressure management, but also in the local level monitoring of Agenda 2030 progress related to SDG 11. View Full-Text
Keywords: EO data; indicators; migrant population; SDG 11.1.1; SDG 11.3.1; Sentinel-2; urban resilience EO data; indicators; migrant population; SDG 11.1.1; SDG 11.3.1; Sentinel-2; urban resilience
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MDPI and ACS Style

Aquilino, M.; Tarantino, C.; Adamo, M.; Barbanente, A.; Blonda, P. Earth Observation for the Implementation of Sustainable Development Goal 11 Indicators at Local Scale: Monitoring of the Migrant Population Distribution. Remote Sens. 2020, 12, 950. https://doi.org/10.3390/rs12060950

AMA Style

Aquilino M, Tarantino C, Adamo M, Barbanente A, Blonda P. Earth Observation for the Implementation of Sustainable Development Goal 11 Indicators at Local Scale: Monitoring of the Migrant Population Distribution. Remote Sensing. 2020; 12(6):950. https://doi.org/10.3390/rs12060950

Chicago/Turabian Style

Aquilino, Mariella; Tarantino, Cristina; Adamo, Maria; Barbanente, Angela; Blonda, Palma. 2020. "Earth Observation for the Implementation of Sustainable Development Goal 11 Indicators at Local Scale: Monitoring of the Migrant Population Distribution" Remote Sens. 12, no. 6: 950. https://doi.org/10.3390/rs12060950

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