An Experimental Evaluation of Kernel Density Estimation to Choose Categorical Map Colours

References

• Adobe Color (2023) “Color Wheel, a Color Palette Generator” Available at: https://color.adobe.com/zh/create/color-wheel (Accessed: 10th January 2023).
   Google Scholar
• Andrews, D.W. (1991) “Asymptotic Optimality of Generalized CL, Cross-Validation, and Generalized Cross-Validation in Regression with Heteroskedastic Errors” Journal of Econometrics 47 pp.359–377 DOI: 10.1016/0304-4076(91)90107-O.
   Web of Science ®Google Scholar
• Bergman, L.D., Rogowitz, B.E. and Treinish, L.A. (1995) A Rule-Based Tool for Assisting Colormap Selection Proceedings of Visualization'95, pp. 118–125.
   Google Scholar
• Brewer, C.A., Hatchard, G.W. and Harrower, M.A. (2003) “Colorbrewer in Print: A Catalog of Color Schemes for Maps” Cartography and Geographic Information Science 30 pp.5–32 10.1559152304003100010929.
   Google Scholar
• Brunsdon, C. (1995) “Estimating Probability Surfaces for Geographical Point Data: An Adaptive Kernel Algorithm” Computers & Geosciences 21 pp.877–894 10.10160098-3004(95)00020-9.
   Web of Science ®Google Scholar
• Carter, R. and Huertas, R. (2010) “Ultra-Large Color Difference and Small Subtense” Color Research & Application 35 pp.4–17 DOI: 10.1002/col.20557.
   Web of Science ®Google Scholar
• COLOURlovers (2023) “Share Your Color Ideas & Inspiration” Available at: https://www.colourlovers.com/ (Accessed: 10th January 2023).
   Google Scholar
• Epanechnikov, V.A. (1969) “Non-parametric Estimation of a Multivariate Probability Density” Theory of Probability & Its Applications 14 pp.153–158 10.11371114019.
   Web of Science ®Google Scholar
• European Environment Agency (2023) “Homepage” Available at: https://www.eea.europa.eu/ (Accessed: 10th January 2023).
   Google Scholar
• Field, K. (2018) Cartography. Redlands, CA: Esri Press.
   Google Scholar
• Fotheringham, A.S. and Sachdeva, M. (2022) “Modelling Spatial Processes in Quantitative Human Geography” Annals of GIS 28 pp.5–14 10.108019475683.2021.1903996.
   Web of Science ®Google Scholar
• Gong, X., Lu, Y., Beene, D., Li, Z., Hu, T., Morgan, M. and Lin, Y. (2022) “Understanding Public Perspectives on Fracking in the United States Using Social Media Big Data” Annals of GIS pp.1–15.
   Web of Science ®Google Scholar
• Harrower, M. and Brewer, C.A. (2003) “Colorbrewer. org: An Online Tool for Selecting Colour Schemes for Maps” The Cartographic Journal 40 pp.27–37 10.1179000870403235002042.
   Web of Science ®Google Scholar
• He, Y., Xu, Q., Wan, J. and Yang, S. (2016) “Short-Term Power Load Probability Density Forecasting Based on Quantile Regression Neural Network and Triangle Kernel Function” Energy 114 pp.498–512 10.1016j.energy.2016.08.023.
   Web of Science ®Google Scholar
• Kohavi, R. (1995) “A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection” Ijcai pp.1137–1145.
   Google Scholar
• Kraak, M., Roth, R.E., Ricker, B., Kagawa, A. and Le Sourd, G. (2021) Mapping for a Sustainable World New York: United Nations and International Cartographic Association.
   Google Scholar
• Le, K.G., Liu, P. and Lin, L. (2022) “Traffic Accident Hotspot Identification by Integrating Kernel Density Estimation and Spatial Autocorrelation Analysis: A Case Study” International Journal of Crashworthiness 27 pp.543–553 10.108013588265.2020.1826800.
   Web of Science ®Google Scholar
• Li, T. (2022) “Exploring Spatial Variations of us Rock Music Concerts in Relation to Population Demographics and Leisure and Hospitality Industry” Annals of GIS 28 pp.293–306 10.108019475683.2022.2031293.
   Web of Science ®Google Scholar
• Lin, A., Sun, X., Wu, H., Luo, W., Wang, D., Zhong, D., Wang, Z., Zhao, L. and Zhu, J. (2021) “Identifying Urban Building Function by Integrating Remote Sensing Imagery and Poi Data” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14 pp.8864–8875 10.1109JSTARS.2021.3107543.
   Web of Science ®Google Scholar
• Lin, S., Fortuna, J., Kulkarni, C., Stone, M. and Heer, J. (2013a) “Selecting Semantically-Resonant Colors for Data Visualization” Computer Graphics Forum Oxford, UK: Blackwell Publishing Ltd. 32 pp.401–410.
   Google Scholar
• Lin, S., Ritchie, D., Fisher, M. and Hanrahan, P. (2013b) “Probabilistic Color-by-Numbers: Suggesting Pattern Colorizations Using Factor Graphs” ACM Transactions on Graphics (TOG) 32 pp.1–12.
   Web of Science ®Google Scholar
• Luo, M.R., Cui, G. and Rigg, B. (2001) “The Development of the CIE 2000 Colour-Difference Formula: CIEDE2000” Color Research & Application 26 pp.340–350.
   Web of Science ®Google Scholar
• MacEachren, A.M. (2004) How Maps Work: Representation, Visualization, and Design New York: Guilford Press.
   Google Scholar
• Mahy, M., Van Eycken, L. and Oosterlinck, A. (1994) “Evaluation of Uniform Color Spaces Developed After the Adoption of CIELAB and CIELUV” Color Research & Application 19 pp.105–121. DOI: 10.1111/j.1520-6378.1994.tb00070.x.
   Web of Science ®Google Scholar
• Mcdonald, R. and Smith, K.J. (1995) “CIE94 - A New Colour-Difference Formula” Journal of the Society of Dyers and Colourists 111 pp.376–379 10.1111j.1478-4408.1995.tb01688.x.
   Google Scholar
• Mellado, N., Vanderhaeghe, D., Hoarau, C., Christophe, S., Brédif, M. and Barthe, L. (2017) “Constrained Palette-Space Exploration” ACM Transactions on Graphics (TOG) 36 pp.1–14 10.11453072959.3073650.
   Web of Science ®Google Scholar
• Mittelstädt, S., Jäckle, D., Stoffel, F. and Keim, D.A. (2015) “Colorcat: Guided Design of Colormaps for Combined Analysis Tasks” Conference on Visualization (EuroVis), pp.115–119.
   Google Scholar
• Open Canada (2023) “Open Government” Available at: DOI: https://open.canada.ca/en (Accessed: 10th January 2023).
   Google Scholar
• Polończyk, A. and Leśniak, A. (2020) “Mapping Public Order Offenses: A Study of the Spatial Distribution of Perceived Risk Intensity in the City of Krakow, Poland” Cartography and Geographic Information Science 47 pp.171–191 10.108015230406.2019.1682677.
   Web of Science ®Google Scholar
• Robertson, A.R. (1977) “The CIE 1976 Color-Difference Formulae” Color Research & Application 2 pp.7–11 10.1002j.1520-6378.1977.tb00104.x.
   Google Scholar
• SciencesPo MediaLab (2013) “I Want Hue: Colors for Data Scientists” Available at: https://medialab.github.io/iwanthue/ (Accessed: 10th January 2023).
   Google Scholar
• Shen, C., Kim, J. and Wang, H. (2009) “Generalized Kernel-Based Visual Tracking” IEEE Transactions on Circuits and Systems for Video Technology 20 pp.119–130 10.1109TCSVT.2009.2031393.
   Web of Science ®Google Scholar
• Silverman, B.W. (1986) Density Estimation for Statistics and Data Analysis London: Chapman and Hall.
   Google Scholar
• Terrell, G.R. and Scott, D.W. (1992) “Variable Kernel Density Estimation” The Annals of Statistics 20 pp.1236–1265.
   Web of Science ®Google Scholar
• United States Geological Survey (2023) “Homepage” Available at: www.usgs.gov (Accessed: 10th January 2023).
   Google Scholar
• University of Pennsylvania Department of Geography (2023) “Homepage” Available at: https://guides.library.upenn.edu/mapping (Accessed: 10th January 2023).
   Google Scholar
• Valerio, R. and Vilalta, R. (2014) “A Data Complexity Approach to Kernel Selection for Support Vector Machines” Proceedings of the AAAI Conference on Artificial Intelligence pp.3138–3139.
   Google Scholar
• Wu, M., Chen, T., Lv, G., Chen, M., Wang, H. and Sun, H. (2018) “Identification and Formalization of Knowledge for Coloring Qualitative Geospatial Data” Color Research & Application 43 pp.198–208 10.1002col.22183.
   Web of Science ®Google Scholar
• Wu, M., Chen, T., Wang, H., Lv, G., Li, H., Jin, Z. and Tao, Y. (2019) “An Artificial Bee Colony-Based Algorithm to Automatically Create Colour Schemes for Geovisualizations” The Cartographic Journal 56 pp.161–174 10.108000087041.2018.1507182.
   Web of Science ®Google Scholar
• Wu, M., Sun, Y. and Li, Y. (2022) “Adaptive Transfer of Color from Images to Maps and Visualizations” Cartography and Geographic Information Science 49 pp.289–312 10.108015230406.2021.1982009.
   Web of Science ®Google Scholar
• Xie, Z. and Yan, J. (2008) “Kernel Density Estimation of Traffic Accidents in a Network Space” Computers, Environment and Urban Systems 32 pp.396–406 10.1016j.compenvurbsys.2008.05.001.
   Web of Science ®Google Scholar
• Yuan, K., Cheng, X., Gui, Z., Li, F. and Wu, H. (2019) “A Quad-Tree-Based Fast and Adaptive Kernel Density Estimation Algorithm for Heat-map Generation” International Journal of Geographical Information Science 33 pp.2455–2476 10.108013658816.2018.1555831.
   Web of Science ®Google Scholar
• Zhang, G., Zhu, A. and Huang, Q. (2017) “A GPU-Accelerated Adaptive Kernel Density Estimation Approach for Efficient Point Pattern Analysis on Spatial Big Data” International Journal of Geographical Information Science 31 pp.2068–2097 10.108013658816.2017.1324975.
   Web of Science ®Google Scholar