Multi‐scale approach to mining significant spatial co‐location patterns

Spatial co‐location pattern mining aims to discover a collection of Boolean spatial features, which are frequently located in close geographic proximity to each other. Existing methods for identifying spatial co‐location patterns usually require users to specify two thresholds, i.e. the prevalence threshold for measuring the prevalence of candidate co‐location patterns and distance threshold to search the spatial co‐location patterns. However, these two thresholds are difficult to determine in practice, and improper thresholds may lead to the misidentification of useful patterns and the incorrect reporting of meaningless patterns. The multi‐scale approach proposed in this study overcomes this limitation. Initially, the prevalence of candidate co‐location patterns is measured statistically by using a significance test, and a non‐parametric model is developed to construct the null distribution of features with the consideration of spatial auto‐correlation. Next, the spatial co‐location patterns are explored at multi‐scales instead of single scale (or distance threshold) discovery. The validity of the co‐location patterns is evaluated based on the concept of lifetime. Experiments on both synthetic and ecological datasets show that spatial co‐location patterns are discovered correctly and completely by using the proposed method; on the other hand, the subjectivity in discovery of spatial co‐location patterns is reduced significantly.     

Monitoring canopy growth and grain yield of paddy rice in South Korea by using the GRAMI model and high spatial resolution imagery

Monitoring crop conditions and forecasting crop yields are both important for assessing crop production and for determining appropriate agricultural management practices; however, remote sensing is limited by the resolution, timing, and coverage of satellite images, and crop modeling is limited in its application at regional scales. To resolve these issues, the Gramineae (GRAMI)-rice model, which utilizes remote sensing data, was used in an effort to combine the complementary techniques of remote sensing and crop modeling. The model was then investigated for its capability to monitor canopy growth and estimate the grain yield of rice (Oryza sativa), at both the field and the regional scales, by using remote sensing images with high spatial resolution. The field scale investigation was performed using unmanned aerial vehicle (UAV) images, and the regional-scale investigation was performed using RapidEye satellite images. Simulated grain yields at the field scale were not significantly different (p = 0.45, p = 0.27, and p = 0.52) from the corresponding measured grain yields according to paired t-tests (α = 0.05). The model’s projections of grain yield at the regional scale represented the spatial grain yield variation of the corresponding field conditions to within ±1 standard deviation. Therefore, based on mapping the growth and grain yield of rice at both field and regional scales of interest within coverages of a UAV or the RapidEye satellite, our results demonstrate the applicability of the GRAMI-rice model to the monitoring and prediction of rice growth and grain yield at different spatial scales. In addition, the GRAMI-rice model is capable of reproducing seasonal variations in rice growth and grain yield at different spatial scales.     

Android平台下的地形图图幅信息检索

针对当前移动端缺少快速、实时检索地形图图幅信息的系统,本文依据国家基本比例尺地形图分幅与编号标准,提出了基于Andriod平台的地形图图幅信息检索方法并编程实现了该系统。该系统实现的功能有:点所在图幅编号检索、图幅范围检索、地形图新旧编号的转换及区域范围内图幅编号集检索。此系统可应用于Andriod智能手机和平板电脑等移动设备。实验表明,本文的方法和系统执行效率能够基本满足移动用户实时检索地形图图幅信息的需求。     

警用地理信息系统平台下仿三维地图实现

针对警用地理信息系统建设中地图风格过于陈旧的问题,该文以四川省公安厅相关系统为例,在考虑互联网主流电子地图的制图配图风格与PGIS平台用户体感的基础上,结合ArcGIS Engine二次开发技术设计并实现了警用地理信息系统支持下的仿三维电子地图算法。实际应用结果表明,该算法与百度、高德等电子地图实现的仿三维立体效果趋于一致,且电子地图的配图风格和制图精细化均有较大幅度的提升,为警用地理信息系统平台下仿三维地图的制图技术应用提供了一种新的思路。     

A Knowledge-Based Computerized Approach to the Development of a Sinkhole Database

This study proposes a knowledge-based, computerized method to develop an initial sinkhole database as a basis for subsequent field investigation to improve on the established database. The knowledge-based data analysis represents sinks (i.e., topographic depressions) as isolines and associates them with contextual land use, land cover, and hydrologic information relevant to potential sinkhole hazard. Sinks of high priority for subsequent field investigation are identified based on combinations of contextual attributes. The proposed methodology for knowledge-based development of a preliminary sinkhole database is valuable for hazard planning and management.     

Exploring the Spatiotemporal Pattern of Cyberbullying with Yik Yak

Cyberbullying is an emerging social issue along with the prevalence of social media. Previous studies have used extensive surveys or firsthand data primarily from conventional social networks¹1 Social media whose users primarily use their real name and provide identifiable information, although some might use pseudonyms (e.g., Facebook and Twitter).View all notes (e.g., Twitter) to study cyberbullying, which often ignores the factor of anonymity and location. Considering the sensitive nature and contagious effect of cyberbullying, a better understanding of the spatiotemporal pattern in cyberbullying is sorely needed to develop effective policies to combat this toxic social behavior. Grounded in the dramaturgy theory and the emerging literature on technoself, this study aims to compare cyberbullying in the anonymous social media (Yik Yak) with the conventional social media (Twitter) and explore its spatiotemporal patterns. A support vector machine is used to help identify records with bullying content. Average nearest neighbor, kernel density, and Ripley's K-function are used to explore the spatiotemporal patterns of cyberbullying behavior. We have found that cyberbullying is more likely to occur in anonymous than conventional social media. We also detected a clustering pattern corresponding to the student population, which can be explained by the dramaturgy theory and recent studies on technoself. In addition to making suggestions to help reduce cyberbullying in the future, this article also sheds light on the need for future studies.     

Spatial patterns of lost and remaining trees in the Iberian wooded rangelands

Spanish dehesas and Portuguese montados are similar Iberian silvopastoral systems that provide important environmental services and hold a high biodiversity. However, there is a raising concern for the sustainability of these cultural landscapes, as they suffer from tree recruitment failure. Consequently, trees in those systems are lessening due to tree ageing, diseases and other causes. A key to developing successful management strategies in dehesas is to understand the spatiotemporal behaviour of this process. We analysed the spatial pattern of lost and remaining trees in five dehesas in Extremadura (SW Spain) between 1956 and 2009 based on aerial images. We studied the possible spatial dependency (clustering or segregation) of the lost and remaining trees in areas with low or high land use intensity at different spatial distances. Analyses were performed under the random labelling hypothesis and the critical values were obtained by using 199 permutations of a Monte Carlo test. A linear mixed model was used to investigate the possible factors affecting tree loss. The results indicate a clear segregation between remaining and lost trees in areas of high intensity land use, while in areas with low intensity this pattern was less evident. Also, lost trees were more clustered than the remaining ones. Higher tree loss density occurred at lower slopes, the areas initially occupied by high or medium-density shrubs being less prone to undergo tree loss than those that appeared in 1956 like ploughed lands, grasslands or low-density shrubs. These results show a concentration of the risk of tree loss in certain areas where conservation efforts should be focused.     

Exploring spatially varying and scale-dependent relationships between soil contamination and landscape patterns using geographically weighted regression

Landscape pattern is an important determinant of soil contamination at multiple scales, and a proper understanding of their relationship is essential for alleviating soil contamination and making decisions for land planners. Both soil contamination and landscape patterns are heterogeneous across spaces and scale-dependent, but most studies were carried out on a single scale and used the conventional multivariate analyses (e.g. correlation analysis, ordinary least squared regression-OLS) that ignored the issue of spatial autocorrelation. To move forward, this paper examined spatially varying relationships between agricultural soil trace metal contamination and landscape patterns at three block scales (i.e. 5 km × 5  km, 10 km × 10 km, 15 km × 15 km) in the Pearl River Delta (PRD), south China, using geographically weighted regression (GWR). This paper found that GWR performed better than OLS in terms of increasing R square of the model, lowering Akaike Information Criterion values and reducing spatial autocorrelation. GWR results revealed great spatial variations in the relationships across scales, with an increasing explanatory power of the model from small to large block scales. Despite a few negative correlations, more positive correlations were found between soil contamination and different aspects of landscape patterns of water, urban land and the whole landscape (i.e. the proportion, mean patch area, the degree of landscape fragmentation, landscape-level structural complexity, aggregation/connectivity, road density and river density). Similarly, more negative correlations were found between soil contamination and landscape patterns of forest and the distance to the river and industry land (p < 0.05). Furthermore, most significant correlations between soil contamination and landscape variables occurred in the western PRD across scales, which could be explained by the prevailing wind, the distribution of pollutant sources and the pathway of trace metal inputs.     

哈大巨型城市带要素集聚程度与生态环境水平关系演变

基于耦合模型和象限图法,从整体和内部分异2个角度对2000~2012年哈大巨型城市带要素集聚程度与生态环境水平演变及其关系进行分析,结果表明：哈大巨型城市带要素集聚程度呈波动上升的演变特征,生态环境则“先波动上升,后缓慢下降”,城市带整体要素集聚程度与生态环境水平间关系趋于协调,产业结构演变、资源消耗水平、经济密度是导致城市带要素集聚程度与生态环境水平协调度提高的主要驱动因子,其中产业结构演变的影响作用更加明显。城市带内部要素集聚的“四城市”格局已经形成,呈现出“核心-边缘”结构,要素集聚程度与生态环境水平可分为高级协调、要素集聚滞后、低级协调、生态环境滞后4种类型。4个区域中心城市要素集聚程度与生态环境水平关系趋向不协调,中心城市周边地区主要向要素集聚滞后型转变,资源型城市和重工业城市要素集聚程度与生态环境水平多表现为低级协调。     

贵州省乡村贫困空间格局与形成机制分析

以贫困态势严峻、区域内部贫困差异大的贵州省为研究区,分析了贵州省区县层面乡村贫困的空间异质性和空间依赖性格局,定量测度了乡村贫困空间差异的影响因素和因素效应的空间差异性,进而归纳了贵州省乡村贫困的形成机制。结果发现,贵州省区县乡村贫困具有时空稳定性,呈现出东、南、西部高而中、北部低的“马蹄”形空间异质性格局。区县贫困存在较强的空间依赖性,“高-高”型贫困地域即空间贫困陷阱区域,集聚分布在贵州省的东南部、南部。定量模型发现,坡度、到所在市中心的距离、青少年人口占比、少数民族人口占比是导致贵州区县层面乡村贫困空间差异的显著因素,且这些因素的效应水平呈现出不同的空间模式。产业发展受限、劳动力流动性差、金融和人力资本积累不足是贵州贫困空间形成的主导机制。最后建议扶贫政策层面应将基于地方和基于人的政策相结合。