关键词: 智能制图工具 , 大数据 , 农业 , 环境

Abstract: 【Objective】In agricultural and environment research area, restrained by financial and technical limitations, in fact, most studies have to focus on specific topics and mechanisms based on point or parcel observations and confined period. As a result, large number of scattered observation data was produced. Different types of observations and evidences previously obtained in separate studies can be related through associated coordinates. Such connections may induce new scientific acquaintance from former observations and evidences. Massive geo-data analysis methods improve the comprehending of process or object complex through multi- channels and with multi-point of view. It is a useful tool to examine and amend the assumptions to the world by taking advantage of the new data and evidence. The main difficulty of the big geo-data analysis comes from not only the treating of large data volume, but also the extracting, integrating and expressing information based on heterogeneous data resources. Since there has been no mainstream software tool available for big geo-data analysis, purpose of the study is to develop a professional tool for extracting and integrating heterologous massive geo-information from different resources as well as for making thematic maps with high resolution on large-scales. The intelligent software package should finish data processing and mapping automatically or human-computer interactively.【Method】Principles and rules of methodology for massive geo-data analysis and software design were applied for IMAT (Intelligent mapping tools) design. The whole design consisted of three parts, the system architecture, the system data supporting platform, and the design for system modules and models. For IMAT software development, C# was used as the programming language, NET Framework 4 Extended was applied as development environment, functions and components from software packages of ArcGIS, Access and DotNet Bar were called. 【Result】With 38 independent functional modules, IMAT provides the main functions for analyzing massive spatial information and cartographic representing, which are required in agriculture and the environment research and working area. Each module can be used to independently conduct certain data analysis and processing, for example, the big data loading and storing, the statistical analysis, classification and coding of space elements, the data selecting, integration and mapping, etc. It can also be used as a combination of several modules to complete a more complex task of data extraction and expression. IMAT has made up the lack in functions to deal with massive spatial data provided by mainstream database package and GIS software package. The objects for IMAT data analysis are the massive spatial database. When doing data analysis with IMAT, rules for data extracting can be set down based on thematic objectives. The integration of massive spatial data within logical data structure as well as storage data structure can be processed automatically or in batch processing way. When making cartographic representations with IMAT, rules and parameters for the whole map composed of a series map sheets can be set, through which differentiated data extracting and mapping for map sheets can be carried out. The data analysis and mapping procedures can be operated by intelligent and automatic way as well as human-computer interactive approach.【Conclusion】For agricultural and environmental topics, IMAT can be applied to analyze and express thematic elements both for soil types and soil texture that expressed by classification, and for soil nutrient and contaminant concentration or non-point source pollution index that expressed by quantity. In addition, it can be used for extracting multi thematic elements and for mapping with different map scales or map sheets. In IMAT design, the whole system was assembled by separated modules and models, so that both the designing and the programming of the software packages could be completed efficiently. By using a four component expressions as interface files among different massive geo-data aggregate from different modules, each module of IMAT is able to receive and process geo-data aggregate of different heterogeneity at different data processing steps. According to data processing target, whether for data extracting, integrating or mapping, required functional modules can be chosen and assembled with high flexibility.

Key words: intelligent mapping tools , big data , agriculture , environment