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English Information

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Authors
# Name
1 Lucas Silva(lucaslsilva@alu.ufc.br)
2 Marta Gonzalez(martag@berkeley.edu)
3 Lucas Babadopulos(babadopulos@ufc.br)
4 Jorge Soares(jsoares@det.ufc.br)
5 Lara Furtado(lara.furtado@insightlab.ufc.br)

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Reference
# Reference
1 Bai, Y., Yang, Z., Yu, J., Ju, R.-Y., Yang, B., Mas, E., & Koshimura, S. (2024). Flood data analysis on SpaceNet 8 using Apache Sedona. arXiv preprint arXiv:2404.18235. https://arxiv.org/abs/2404.18235
2 Croeser, T., Sharma, R., Weisser, W. W., & Bekessy, S. A. (2024). Acute canopy deficits in global cities exposed by the 3-30-300 benchmark for urban nature. Nature Communications, 15(1), 9333. https://doi.org/10.1038/s41467-024-49383-1
3 Forrest, M. (2025). Geospatial tools compared: When to use GeoPandas, PostGIS, DuckDB, Apache Sedona, and Wherobots. Medium. Retrieved June 19, 2025, from https://towardsdatascience.com/geospatial-tools-compared
4 García-García, F., Corral, A., Iribarne, L., & Vassilakopoulos, M. (2023). Efficient distributed algorithms for distance join queries in Spark-based spatial analytics systems. International Journal of General Systems, 52(3), 206–250. https://doi.org/10.1080/03081079.2023.2174732
5 Konijnendijk, C. C. (2023). Evidence-based guidelines for greener, healthier, more resilient neighbourhoods: Introducing the 3–30–300 rule. Journal of Forestry Research, 34(3), 821–830. https://doi.org/10.1007/s11676-022-01551-4
6 Lyon, W., Yu, J., & Sarwat, M. (2025). Cloud native geospatial analytics with Apache Sedona (1st ed.). O’Reilly Media.
7 Moussa, R. (2021). Scalable analytics of air quality batches with Apache Spark and Apache Sedona. In Proceedings of the 15th ACM International Conference on Distributed and Event-Based Systems (DEBS ’21) (pp. 154–159). Association for Computing Machinery. https://doi.org/10.1145/3465480.3466932
8 Nieuwenhuijsen, M. J., Dadvand, P., Márquez, S., Bartoll, X., Barboza, E. P., Cirach, M., Borrell, C., & Zijlema, W. L. (2022). The evaluation of the 3-30-300 green space rule and mental health. Environmental Research, 215, 114387. https://doi.org/10.1016/j.envres.2022.114387
9 Wyrzykowski, B., & Mościcka, A. (2024). Implementation of the 3-30-300 green city concept: Warsaw case study. Applied Sciences, 14(22), 10566. https://doi.org/10.3390/app142210566
10 Yu, J., Wu, J., & Sarwat, M. (2015). GeoSpark: A cluster computing framework for processing large-scale spatial data. In Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems (pp. 1–4). Association for Computing Machinery. https://doi.org/10.1145/2820783.2820860
11 Zheng, Y., Lin, T., Hamm, N. A., Liu, J., Zhou, T., Geng, H., Zhang, J., Ye, H., Zhang, G., Wang, X., et al. (2024). Quantitative evaluation of urban green exposure and its impact on human health: A case study on the 3–30-300 green space rule. Science of the Total Environment, 924, 171461. https://doi.org/10.1016/j.scitotenv.2024.171461