Visualizing Geospatial Distribution of Pesticide Residue Pollution Using Cartogram and Heat Map

  • Yi ChenEmail author
  • Yunfang Zhao
  • Xingru Chen
  • Xun Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10092)


Pesticide Residue is one of main sources resulted in food safety problems. It is necessary to analyze the distribution pattern of pesticide residue in order to supervision and management the overuse of pesticide. Thematic map is an effective approach for visualizing data combined with a specific geographic area. The most popular of the thematic maps are Choropleth, in which the values of the attribute are encoded as points or colored regions on the map. However, when the density of attribute-points on an area is different with that region’s area, the data overlap will be produced. In this paper, we present a method for visualizing multidimensional data based on Cartogram. With this method, we first create Cartogram and Choropleth for presenting the geospatial distribution of data at the same time in order to avoid data overlapping, in which the Cartogram is generated by using diffusion algorithm; Second, we create thematic geographic heat map for presenting the pesticide residue pollution index by means of Inverse Distance Weighted interpolation to reckon missing data, in which the pesticide residue pollution index is calculated by using multiple linear regression algorithm; Thirdly, a multi-view spatial-temporal data visualization system, which combines maps, time axis, bar chart, bubble chart, pie chart etc. is presented to help user analyzing the data. A variety of interactive means such as region selection, data filtering, time cursor dragging, are also introduced to the system. The system uses two different ways to combine spatial with time. The results of user evaluation demonstrated that our method and system can effectively help user to analyze geospatial distribution of pesticide residue pollution.


Geospatial visualization Cartogram Heat map Inverse distance weighted interpolation Multiple-linear regression Pesticide residue pollution 



This work is supported by ‘‘Twelfth Five Year Plan’’ National Science and Technology Support Program (No. 2012BAD29B01-2), the open funding project of State Key Laboratory of Virtual Reality Technology and Systems, Beihang University (Grant No. BUAA-VR-14KF-04), and the funding of Funding Project for Innovation on Science, Technology and Graduate Education in Institutions of Higher Learning Under the Jurisdiction of Beijing Municipality (Grand No. PXM2014_014213_000043).


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Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Beijing Key Laboratory of Big Data Technology for Food Safety, School of Computer and Information EngineeringBeijing Technology and Business UniversityBeijingChina

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