Three-dimensional visualization of an emotional map with geographical information systems: a case study of historical and cultural heritage in the Yeongsan River Basin,Korea

The objective of this study is the production and visualization of an emotional map to reveal the unique emotions inherent to the areas surrounding the Yeongsan River, which is often referred to as ‘the cradle of civilization’ in Korea. The sites selected for this study are the 11 cities and districts (5667.6 km²) that cut across the vast granary in the southeastern region of Korea, near the Yeongsan River. The emotional map was produced by extracting features of historical and cultural heritage distributed throughout this region and by using a geographic information systems program and its functions for spatial analysis. A database was constructed through interviews with locals and Global Positioning System to index 4318 pieces of cultural heritage to achieve the visualization of emotions. Among the 558 historical relics considered for representing the regional culture, 100 with the largest emotional impact were selected. It was determined that loyalty (), justice (), courtesy (), resentment (), and anger () should be the major emotional elements. Methodologically, a set of regional, periodic, historical, and emotional classification codes were first systematized. After subjecting this data to inverse distance weight interpolation and vertical exaggeration coefficients, the three-dimensional emotional map could be visualized.     

论DEM地形分析中的尺度问题

DEM及其地形分析具有强烈的尺度依赖特征。本文以黄高原地区的研究为例,结合地学建模和地学模拟的需求,重点讨论DEM地形分析中的尺度问题。文中从DEM建立与应用出发,首先建立了DEM地形分析中的尺度概念体系,剖析了各类尺度之间的关系,其次讨论了尺度所引起的各种地形分析效应问题,最后探讨了DEM地形分析中的尺度转换类型和方法。     

地理学视角下土地利用/覆被分类发展探讨

随着对地观测和互联网技术的发展,地理大数据时代正在到来,其多尺度、长时序、多模态等海量“超”覆盖数据为土地利用/覆被（Land Use/Land Cover, 简称LULC）分类及变化检测带来巨大的机遇,支撑着新时代人、地两大系统相互作用关系的认知和实践。然而,多数地理学者认为地理学基本原理与核心思想并未因为大数据的到来而发生本质性变化。所以,从地理学基本原理角度理解LULC分类的发展,尤其在地理大数据时代的发展方向,不失为一条可行的途径。为此,本文从区域、尺度、综合三方面的地理学基本原理视角将LULC分类技术的发展划分为地球观测数据匮乏阶段、人类行为数据融合阶段以及地理大数据“超”覆盖阶段分别探讨分析,以期主动把握LULC分类技术及应用的未来发展趋势。研究结果显示：在地球观测数据匮乏阶段,LULC分类多以类型还不丰富的遥感数据源,在空间分辨率较低的像元尺度上,进行以地表覆被状态为主的分类;发展到人类行为数据融合阶段,LULC分类在城市区域率先出现了对地观测数据和人类行为数据相融合,在街区尺度上进行以空间功能异质性划分、识别为主导的城市功能区分类;在地理大数据“超”覆盖阶段,LULC分类将实现多尺度协同、面向全空间的功能异质性划分,并在主体功能的基础上融合“社会-经济-自然”多维定量属性,本文称之为“空间场景”。希望本文的探讨能够为地理大数据时代LULC分类的新技术发展和新产品应用提供有益启示。     

The use of high-resolution gridded climate data in the development of chironomid-based inference models from remote areas

The development of chironomid-based air temperature inference models in high latitude regions often relies on limited spatial coverage of meteorological data and/or on punctual measurements of water temperature at the time of sampling. The use of simple linear regression to relate air temperature and latitude was until recently the best method to characterize the air temperature gradient along a latitudinal gradient. However, recent studies have used high-resolution gridded climate data to develop new chironomid-based air temperature inference models. This innovative approach has, however, never been further analyzed to test its reliability. This study presents a method using ArcGIS^® to extract air temperatures from a high-resolution global gridded climate data set (New et al. 2002) and to incorporate these new data in a variety of chironomid-based air temperature inference models to test their performance. Results suggest that this method is reliable and produces better estimates of air temperature and will be helpful in the development of further quantitative air temperature inference models in remote areas.     

Response to ‘Comments on “Combining spatial transition probabilities for stochastic simulation of categorical fields” with communications on some issues related to Markov chain geostatistics’

Li and Zhang (2012b Li, W. and Zhang, C. 2012b. Comments on ‘Combining spatial transition probabilities for stochastic simulation of categorical fields’ with communications on some issues related to Markov chain geostatistics. International Journal of Geographical Information Science, 26(10): 1725–1739. [Taylor & Francis Online] , [Google Scholar], Comments on ‘Combining spatial transition probabilities for stochastic simulation of categorical fields’ with communications on some issues related to Markov chain geostatics) raised a series of comments on our recent paper (Cao, G., Kyriakidis, P.C., and Goodchild, M.F., 2011 Cao, G., Kyriakidis, P.C. and Goodchild, M.F. 2011. Combining spatial transition probabilities for stochastic simulation of categorical fields. International Journal of Geographical Information Science, 25(11): 1773–1791. [Taylor & Francis Online], [Web of Science ®] , [Google Scholar]. Combining spatial transition probabilities for stochastic simulation of categorical fields. International Journal of Geographical Information Science, 25 (11), 1773–1791), which include a notation error in the model equation provided for the Markov chain random field (MCRF) or spatial Markov chain model (SMC), originally proposed by Li (2007b Li, W. 2007b. Markov chain random fields for estimation of categorical variables. Mathematical Geology, 39(3): 321–335. [Crossref] , [Google Scholar], Markov chain random fields for estimation of categorical variables. Mathematical Geology, 39 (3), 321–335), and followed by Allard et al. (2011 Allard, D., D'Or, D. and Froideveaux, R. 2011. An efficient maximum entropy approach for categorical variable prediction. European Journal of Soil Science, 62: 381–393. [Crossref], [Web of Science ®] , [Google Scholar], An efficient maximum entropy approach for categorical variable prediction. European Journal of Soil Science, 62, 381–393) about the misinterpretation of MCRF (or SMC) as a simplified form of the Bayesian maximum entropy (BME)-based approach, the so-called Markovian-type categorical prediction (MCP) (Allard, D., D'Or, D., and Froideveaux, R., 2009 Allard, D., D'Or, D. and Froidevaux, R. 2009. Estimating and simulating spatial categorical data using an efficient maximum entropy approach, Avignon: Unité Biostatistique et Processus Spatiaux Institut National de la Recherche Agronomique. Technical Report No. 37 [Google Scholar]. Estimating and simulating spatial categorical data using an efficient maximum entropy approach. Avignon: Unite Biostatisque et Processus Spatiaux Institute National de la Recherche Agronomique. Technical Report No. 37; Allard, D., D'Or, D., and Froideveaux, R., 2011 Allard, D., D'Or, D. and Froideveaux, R. 2011. An efficient maximum entropy approach for categorical variable prediction. European Journal of Soil Science, 62: 381–393. [Crossref], [Web of Science ®] , [Google Scholar]. An efficient maximum entropy approach for categorical variable prediction. European Journal of Soil Science, 62, 381–393). Li and Zhang (2012b Li, W. and Zhang, C. 2012b. Comments on ‘Combining spatial transition probabilities for stochastic simulation of categorical fields’ with communications on some issues related to Markov chain geostatistics. International Journal of Geographical Information Science, 26(10): 1725–1739. [Taylor & Francis Online] , [Google Scholar], Comments on ‘Combining spatial transition probabilities for stochastic simulation of categorial fields’ with communication on some issues related to Markov chain geostatistics. International Journal of Geographical Information Science) also raised concerns regarding several statements Cao et al. (2011 Cao, G., Kyriakidis, P.C. and Goodchild, M.F. 2011. Combining spatial transition probabilities for stochastic simulation of categorical fields. International Journal of Geographical Information Science, 25(11): 1773–1791. [Taylor & Francis Online], [Web of Science ®] , [Google Scholar], Combining spatial transition probabilities for stochastic simulation of categorical fields. International Journal of Geographical Information Science, 25 (11), 1773–1791) had made, which mainly include connections between permanence of ratios and conditional independence, connections between MCRF and Bayesian networks and transiograms as spatial continuity measures. In this response, all of the comments and concerns will be addressed, while also communicating with Li and other colleagues on general topics in Markov chain geostatistics.