As evidenced by COVID-19, there is a global need to provide information that can assist the decision-making process and highlight potential risks to the world. Based on this, this project was motivated due to the lack of available information about the local performance in the SDGs (even with huge differences in the social, economic, and environmental aspects within countries). 

To develop the project, the following steps were taken: i) identification of the theme; (ii) Data collection, organization, and analysis, for each SDG based on the SDG Index Score; iii) calculation of the indicator; iv) elaboration of the dashboard.

In the first step, the identification of the theme, two activities were performed: a brainstorming and a literature review. In the brainstorming activity, each member of the group was responsible for suggesting proposals related to the categories and challenges of NASA's Hackathon. With the initial suggestions an analysis of the difficulties in obtaining the data and building the algorithm was carried out, and it was elected the best of the three proposals. Thus, the members unanimously have chosen the "Sustain" category - "Planet, with people" challenge.

To ensure that the project was innovative, a literature review was conducted in the Scopus database with the keywords "index" and "Sustainable Development Goals" or "SGDs" combined by the search string AND and OR. The aim was to find peer-reviewed articles that proposed local indices of sustainable development gap. Based on this, limited to the title of the articles and obtained 23 document results, of which 16 are peer-reviewed articles and the rest are divided by conference paper, book chapter, editorial and review article.

Most of the studies that proposed composite indices were focused on analyzing the level of compliance with sustainable development goals in certain industrial sectors.

Among the studies that discussed the regionalization of SDGs, it is noteworthy to highlight the research performed by Wang et al. (2020) and Oliveira et al. (2020). Wang et al. (2020) have developed a multivariate index of sustainable development based indicators of the United Nations Sustainable Development Goals (SDGs) applied to Fujian Province, China (Wang et al. 2020).  The former authors, however, analyzed the indexes using the entropy weight coefficient method exclusively for Fujian province, investigating the development index for three dimensions (social, environmental, and economic).

On the other hand, the research performed by Oliveira et al. (2020) is more similar to the regionalized index presented in this project. The authors have described the design and application processes of a composite indicator called WeGI to measure sustainability at the municipal level, in Portugal.  

Following, in the stage of data collection, organization and analysis, the main goal of the delimited challenge was initially established. Thus, it developed an indicator of regionalized sustainability gap from the 17 SDDs. Due to the time limitation of hackathon, it was decided to perform a first application for each state of Brazil (and can be later applied to other regions of the world). Based on this, the search and data collection by Brazilian state for each indicator of the SDGs was initiated. Thus, a spreadsheet was created for the organization of the data needed for plotting the maps and indicating the most affected areas. During this stage of development, the greatest difficulties encountered by the team members were obtaining regionalized and updated data. The team, however, proposed solutions to adapt some of the indicators and, finally, it was possible to calculate the score of each Brazilian State.

In addition, the collected data were with different units of measurement, so it was necessary to normalize it, the calculation of the SDG Index Score was made from the equation [1].

[1]

Where:

x is the raw data value;

max/min denote the bounds or best and worst performance, respectively;

x' is the normalized value after rescaling.

The normalization equation was suggested in the Sustainable Development Report 2020 (Sachs et al. 2020, Lafortune et al., 2018) and the results of the equation indicate, in a score from 0 to 100, the quartile in which the score of each region is located. By Sachs et al. (2020) "a country that scores 50 on a variable is halfway towards achieving the optimum value; a country with a score of 75 has covered three-quarters of the distance from worst to best." 

After the normalization of each indicator, an arithmetic mean of the indicators was performed for each region. Thus, it became possible to visualize each SDG and the sustainability gap indicator for each Brazilian state. Moreover, as a way of understanding how the performance of each region affect its population, the SDG Index Score of each region was multiplied by the population of the respective region, which allowed to identify which more populous regions, despite presenting a better performance, presented a greater loss of sustainability, and compared with less populous regions, even with these in many situations presenting a worse performance.

As future improvements of the results, the team aims to perform an additional analysis of the indicators of the most sensitive SDGs for each state of Brazil, as well as the analysis of data quality, for the establishment of weights between the indicators and weighting of the results in each microregion.

Furthermore, it aims with this project to expand the coverage of analysis to microregions of other countries, especially those in development, so maps can provide relevant information to decision makers.

The dashboard was developed on Google Collab and made available as a fast page on GitHub.

It is also worth noting that all communication between team members was made through Skype and WhatsApp and the documents were shared by Google Drive, so that everyone had access and could edit. The calculation was performed through spreadsheet and the preparation of the dashboard using the Python programming language. NASA data was aggregated by state with QGIS version 3.14.16 software.

Finally, the aggregate results of the Brazilian states were compared with the human development index (HDI) of Brazil, which presented a high correlation (R2 = 0.86),demonstrating that the higher the HDI, the greater the action for sustainability.

In relation to the data applied to the creation of the website, the Nasa Earthdata database (Berrick 2020) was used to search for the updated world population (divided by microregions), data on emissions from forest’s fires "Global Fire Emissions Database (20 15-2016)", data on deforestation scenarios"Modeled Deforestation Scenarios (Amazon Basin:2002-2050)" and data on Carbon Flows associated with feed and animal emissions "Global Carbon Fluxes Associated with Livestock Feed and Emissions (CMS - 2000-2013).

For the regionalized data by state of Brazil, related to the indicators of the objectives of sustainable development, the national databases and tables of the Brazilian Institute of Geography and Statistics (IBGE 2006) were used, 2017a, 2017b, 2020), and data from the Unified Health System (SUS) (BRASIL 2020), the National Cancer Institute (INCA 2014), the Energy Research Company, Exame Magazine (Prates 201 3), the International Labor Organization (INSS - SINAN 2018), the National Registry of Conservation Units (CNUC) (Pereira 1999), the Labor Inspection Portal (SIT 2019), the Internal Revenue Service (Federal Revenue 2019), the Brazilian Association of Public Cleaning Companies and Special Resíduos (Abrelpe 2012), the National Forest Information System (SNIF 2018a , 2018b), the Secretariat of Primary Health Care (SAPS) (BRASIL 2019), the Transparency Index(Speck et al. 2014) and the Institute of Applied Economic Research (IPEA) (Rezende 2015).

The regionalized data added to the data available by NASA satellites allow the visualization of more critical regions of Brazil, which should, as a priority, plan corrective actions to improve the performance of indicators.

Video: https://www.youtube.com/watch?v=nLahAcLRSTY&feature=youtu.be

Demo dashboard: https://leo-smi.github.io/data-force/  

Table results: https://docs.google.com/spreadsheets/d/1IGa42fyX4h97dLy_a3XuxlEFhH7mmD-Uj50Pkk_9ssg/edit?usp=sharin

Abrelpe. 2012. "Panorama of Solid Waste In Brazil 2012." Brazilian Association of Public Cleaning and Special Waste Companies, no. 10: 1-116.

Berrick, Stephen. 2020. "And Earthdata Search."  National Aeronautics and Space Administration (NASA). 2020. https://search.earthdata.nasa.gov/search.

Brazil. 2019. "E-Manager - Information and Management Of Primary Care." Ministry of Health. 2019. https://egestorab.saude.gov.br/.

Brazil. 2020. "DATASUS - Health Information." Ministry of Health. 2020. http://www2.datasus.gov.br/DATASUS/index.php?area=02.

Giglio, Louis, James T. Randerson, e Guido R. van der Werf. 2018. “Global Fire Emissions Indicators, Grids: 1997-2015”. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/H400002V.

Ibge. 2006. "Synthesis of Social Indicators (SIS) - 2006." Brazilian Institute of Geography and Statistics. 2006 https://www.ibge.gov.br/estatisticas/sociais/populacao/9221-sintese-de-indicadores-sociais.html?=&t=resultados.

———. 2017a. "Innovation Research - PINTEC." Brazilian Institute of Geography and Statistics. 2017. https://www.ibge.gov.br/estatisticas/multidominio/ciencia-tecnologia-e-inovacao/9141-pesquisa-de-inovacao.html?=&t=resultados.

———. 2017b. "National Basic Sanitation Survey - PNSB." 2017. https://www.ibge.gov.br/estatisticas/multidominio/meio-ambiente/9073-pesquisa-nacional-de-saneamento-basico.html?=&t=resultados.

———. 2020. "SIDRA - Statistical Table Bank." Brazilian Institute of Geography and Statistics. 2020. https://sidra.ibge.gov.br/home/pimpfbr/brasil.

Inca. 2014. "Online Atlas of Mortality." National Cancer Institute. 2014. https://mortalidade.inca.gov.br/MortalidadeWeb/.

INSS - SINAN. 2018."Smartlab - Observatory of Safety and Health At Work." International Labour Organisation (ILO). 2018. https://smartlabbr.org/sst/smartmap.

Lafortune, G., G. Fuller, J. Moreno, G. Schmidt-Traub, and C. Kroll (2018). "SDG Index and Dashboards: detailed methodological paper.," Bertelsmann Stiftung and Sustainable Development Solutions Network, Paris.

Oliveira, G M, D G Vidal, L M F Viterbo, and R L Maia. 2020."Measuring the Implementation of Sustainable Development Goals at a Local Level:The WeGIx Index." World Sustainability Series, 215–45. https://doi.org/10.1007/978-3-030-30306-8_13.

Pereira, Paula Moraes. 1999. "Systematization of Information Related to The Conservation Units Of Coastal Zones and Brazilian Navy." National Registry of Conservation Units (CNUC), 1–57.

Prates, Marco. 2013. "ES Has Better Education In Brazil, according to Pisa." Exame Magazine. 2013. https://exame.com/ciencia/es-tem-melhor-educacao-do-brasil-second-step-see-list/.

Irs. 2019. "UF Collection Jan-Dec 2019." Ministry of Economy. 2019. https://receita.economia.gov.br/.

Rezende, Daniela Leandro( 2015. "Woman In Power and Decision-Making." Aplicada EconomicResearch Institute (IPEA), 1–73. https://www.ipea.gov.br/retrato/.

Sit. 2019. "Statistics and Information Dashboard of Labor Inspection in Brazil." Ministry of Economy. 2019. https://sit.trabalho.gov.br/radar/.

Snif. 2018a. "Deforestation - Legal Amazon - States - PRODES - 2012-2015." Ministry of the Environment. 2018. https://dados.gov.br/dataset/sistema-nacional-de-informacoes-florestais-snif/.

———. 2018b. "Deforestation - Biome - States - PMDBBS - 2002-2011. " Ministry of the Environment. 2018. https://dados.gov.br/dataset/sistema-nacional-de-informacoes-florestais-snif/.

Speck, Bruno, Ciro Biderman, David Fleischer, Ernesto Saboia, Gil Castello Branco, José Roberto de Toledo, Ricardo Caldas, and Vânia Vieira. 2014. "Transparency Index 2014 - States." Transparency Index. 2014. https://indicedetransparencia.com.

Sachs, Jeffrey, Guido Schmidt-Traub, Christian Kroll, Guillaume ‬ Lafortune‬, ‬ Grayson‬ Fuller‬,Finn ‬ Woelm‬. 2020. The ‬ Sustainable‬ Development‬ Goa‬ls‬ and‬ COVID-19.‬ Sustainable Development Report 2020. Cambridge: Cambridge University Press.‬

Wang, X, Gao P, Song C, and Cheng C. 2020. "Use of Entropy in Developing SDG-Based Indices for Assessing Regional Sustainable Development: A ProvincialCase Study of China." Entropy 22 (4). https://doi.org/10.3390/E22040406.