Syn-SC: Gerando Dados Pontuais Sintéticos de Alto Volume com Continuidade e Suavidade Especificadas
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Quando os dados pontuais são agregados e representados em mapas, a escolha da técnica de mapeamento temático depende da habilidade do cartógrafo e da estrutura espacial dos dados. MacEachren & DiBiase (1991) argumentaram que duas variáveis estruturais merecem atenção explícita: continuidade (a proporção do espaço ocupado pelos eventos) e suavidade (o grau de variação entre locais vizinhos). No entanto, estudos empíricos raramente isolam explicitamente essas variáveis, pois os conjuntos de dados do mundo real raramente abrangem uma faixa conveniente de valores. O Syn-SC preenche essa lacuna; é um plug-in de processamento QGIS 3 independente que sintetiza conjuntos de dados pontuais de alto volume, e a continuidade e suavidade desses dados podem ser definidas independentemente. A ferramenta Scale Assistant (Assistente de escala) divide qualquer área de interesse em hexágonos adaptáveis ao tamanho e relata uma janela de suavidade de dois valores — piso e teto — definida pelos níveis extremos binários 1 e 100. Um distribuidor baseado em regras seleciona então uma das três soluções generativos: uma solução exaustiva de força bruta para pequenas grades de até 16 células, uma heurística de tabuleiro de xadrez para grades grandes e um otimizador iterativo que converge rapidamente sempre que a suavidade solicitada está dentro da janela relatada ou abaixo dela. Benchmarks demonstram que o Syn-SC corresponde precisamente à continuidade solicitada, atinge as metas de suavidade dentro de ±1 ponto percentual e gera conjuntos que se aproximam de um milhão de pontos em segundos. O Syn-SC, portanto, fornece aos cartógrafos, pesquisadores de usabilidade e desenvolvedores de IA conjuntos de dados de pontos compartilháveis e parametrizados perceptualmente que antes não estavam disponíveis.
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References
Arnold, N. D., Jenny, B., & White, D. (2017). Automation and evaluation of graduated dot maps. International Journal of Geographical Information Science, 31(12), 2524–2542. https://doi.org/10.1080/13658816.2017.1359747
Campos, R. G. de, Paiva, C., Bravo, J. V. M., & Camboim, S. P. (2021). A proposition to define boundaries based on the smoothness and the continuity of voluminous point data phenomena. Abstracts of the ICA, 3, 1–2. https://doi.org/10.5194/ica-abs-3-42-2021
Carr, D. B., Olsen, A. R., & White, D. (1992). Hexagon Mosaic Maps for Display of Univariate and Bivariate Geographical Data. Cartography and Geographic Information Systems, 19(4), 228–236. https://doi.org/10.1559/152304092783721231
Dirrler, M., Dörr, C., & Schlather, M. (2020). A generalization of Matérn hard-core processes with applications to max-stable processes. Journal of Applied Probability, 57(4), 1298–1312. https://doi.org/10.1017/jpr.2020.66
Elzakker, C. P. J. M. van, & Griffin, A. L. (2013). Focus on geoinformation users: Cognitive and use/user issues in contemporary cartography. GIM International, 27(8), 20–23.
Flannery, J. J. (1971). The relative effectiveness of some common graduated point symbols in the presentation of quantitative data. Cartographica, 8(2), 96–109. https://doi.org/10.3138/J647-1776-745H-3667
Gorry, P., & Mooney, P. (2025). RADIAN – A tool for generating synthetic spatial data for use in teaching and learning. Cartography and Geographic Information Science, 52(3), 314–330. https://doi.org/10.1080/15230406.2024.2377981
Hermes, T. B., & Poulsen, C. N. (2012). Spatial microsimulation for the generation of synthetic populations: A review. Journal of Geographical Systems, 14(4), 437–467. https://doi.org/10.1007/s10109-012-0164-8
Kawakami, Y., Yuniar, S., & Ma, K.-L. (2024). HexTiles and Semantic Icons for MAUP-Aware Multivariate Geospatial Visualizations (arXiv:2407.16897). arXiv. https://doi.org/10.48550/arXiv.2407.16897
MacEachren, A. M. (1992). Visualizing Uncertain Information. Cartographic Perspectives, 13, Artigo 13. https://doi.org/10.14714/CP13.1000
MacEachren, A. M., & DiBiase, D. (1991). Animated Maps of Aggregate Data: Conceptual and Practical Problems. Cartography and Geographic Information Systems, 18(4), 221–229. https://doi.org/10.1559/152304091783786790
Mannino, M., & Abouzied, A. (2019). Is this Real? Generating Synthetic Data that Looks Real. Proceedings of the 32nd Annual ACM Symposium on User Interface Software and Technology, 549–561. https://doi.org/10.1145/3332165.3347866
Montello, D. R. (2002). Cognitive research in GIScience: The role of user-centered design and user studies. Transactions in GIS, 6(1), 1–11. https://doi.org/10.1111/1467-9671.00091
Provin, R. W. (1977). The Perception of Numerousness on Dot Maps. The American Cartographer, 4(2), 111–125. https://doi.org/10.1559/152304077784080374
Quick, H., & Waller, L. A. (2018). Using spatiotemporal models to generate synthetic data for public use. Spatial and Spatio-temporal Epidemiology, 27, 37–45. https://doi.org/10.1016/j.sste.2018.08.004
Raghunathan, T. E., Reiter, J. P., & Rubin, D. B. (2003). Multiple imputation for statistical disclosure limitation. Journal of official statistics, 19(1), 1.
Robinson, A. C., Demšar, U., Moore, A. B., Buckley, A., Jiang, B., Field, K., Kraak, M. J., Camboim, S. P., & Sluter, C. R. (2017). Geospatial big data and cartography: Research challenges and opportunities for making maps that matter. International Journal of Cartography, 3(sup1), 32–60. https://doi.org/10.1080/23729333.2016.1278151
Robinson, A. C., Kettunen, P., Delazari, L., & Çöltekin, A. (2023). New directions for the state of the art and science in Cartography. International Journal of Cartography, 9(2), 143–149. https://doi.org/10.1080/23729333.2023.2216334
Roth, R. E., Çöltekin, A., Delazari, L., Filho, H. F., Griffin, A., Hall, A., Korpi, J., Lokka, I., Mendonça, A., Ooms, K., & Elzakker, C. P. J. M. van. (2017). User studies in cartography: Opportunities for empirical research on interactive maps and visualizations. International Journal of Cartography, 3(sup1), 61–89. https://doi.org/10.1080/23729333.2017.1288534
Roth, R. E., Kelly, M., Underwood, N., Lally, N., Vincent, K., & Sack, C. (2019). Interactive & Multiscale Thematic Maps: A Preliminary Study. Abstracts of the ICA, 1, 1–2. https://doi.org/10.5194/ica-abs-1-315-2019
Roth, R. E., Ross, K. S., & MacEachren, A. M. (2015). User-Centered Design for Interactive Maps: A Case Study in Crime Analysis. ISPRS International Journal of Geo-Information, 4(1), Artigo 1. https://doi.org/10.3390/ijgi4010262
Slocum, T. A. (1983). Predicting Visual Clusters on Graduated Circle Maps. The American Cartographer, 10(1), 59–72. https://doi.org/10.1559/152304083783948168
Slocum, T. A., McMaster, R. B., Kessler, F. C., & Howard, H. H. (2022). Thematic Cartography and Geovisualization (4ª ed.). CRC Press.
Słomska-Przech, K., & Gołębiowska, I. M. (2021). Do Different Map Types Support Map Reading Equally? Comparing Choropleth, Graduated Symbols, and Isoline Maps for Map Use Tasks. ISPRS International Journal of Geo-Information, 10(2), Artigo 2. https://doi.org/10.3390/ijgi10020069
Vu, T., Migliorini, S., Eldawy, A., & Belussi, A. (2022). Spatial Data Generators. In: Spatial Gems, Volume 1 (1ª ed., Vol. 46, p. 13–24). Association for Computing Machinery. https://doi.org/10.1145/3548732.3548736
Wallner, G., & Kriglstein, S. (2020). Multivariate Visualization of Game Metrics: An Evaluation of Hexbin Maps. Proceedings of the Annual Symposium on Computer-Human Interaction in Play, 572–584. https://doi.org/10.1145/3410404.3414233