基于GIS的泸水县易地扶贫搬迁安置地选择

易地扶贫搬迁是对生活在恶劣环境中的贫困人口采取的扶贫举措。泸水县尚有大量人口需要易地搬迁解决贫困问题,泸水县特殊的峡谷地理环境,使安置地的选择成为棘手的问题。将GIS技术应用于扶贫易地搬迁地选择中,可以方便地找出适宜的搬迁居住地,为当地政府易地安置提供决策参考依据。安置区的选择,主要考虑以下因子：交通便利、土地资源丰富、水源丰富、坡度较为平缓、海拔较低,避开地质灾害隐患点和自然保护区等。通过GIS技术,将上述因子图层叠加后找到适合的安置地。结果表明：泸水县难以找到成片的面积较大的安置区,安置区呈零星状分布在各个乡镇。     

GIS技术在果树适宜种植区域选择中的应用

以永泰县枇杷果树适宜种植区域选择为例,通过对数字高程模型进行地形分析,提取坡度、坡向、坡位信息,以及计算出避冻因子,采用GIS技术进行空间叠置分析,筛选出适宜种植枇杷的生态区域,研究GIS在果树种植中的应用,指导农业生产。     

基于GIS的平顶山市建设用地生态适宜性评价

以平顶山市为例,探讨了煤炭资源型城市建设用地生态适宜性评价的指标体系、评价模型,以及在GIS和RS技术支撑下定量分析土地生态适宜性的方法。选取自然因素、社会经济因素、生态安全因素3个因素层12个因子层构成生态适宜性评价指标体系,采用层次分析法计算相关影响因子权重。在ENVI4.5支持下对平顶山市TM遥感影像进行解译,提取林地、耕地、水体、建设用地多因子图层,在Arc GIS10.0支持下建立数字高程模型,提取高度等地形因子。最后通过Arc GIS10.0空间分析模块对相关因子进行叠加分析,得到结果为:平顶山市最适宜用地占29.01%,较适宜用地占13.53%,基本适宜用地占14.71%,不适宜用地占16.05%,不可用地占26.70%。最后,结合评价结果,针对平顶山建设用地的特征提出合理化建议,以期为生态环境保护和城市规划建设提供科学依据。     

基于GIS的东营市土地适宜性评价研究

依据土地适宜性评价原则,以山东省东营市土地适宜性评价为例,探讨了运用GIS技术对用地的自然、社会属性进行综合评价的数量化方法,结合当地实际情况,选取了土壤类型、地下水位、全氮、全磷、土壤盐分五个评价因子,各因子的适宜度的确定和各单元因素适宜性的评价,对评价结果进行了分析,提出了土地改良应用的合理化建议。将GIS引入到土地适宜性的空间评价中评价方法更科学,使评价结果能更好地为土地利用和规划服务。     

粗集理论和层次分析法在GIS空间分析中的应用研究

随着地理信息理论与技术的不断发展,地理信息系统(GIS)已在各行各业广泛应用开来。GIS的专题应用常常涉及对数据进行多因子分析,而因子分析中权重的确定是一个常见且重要的问题。层次分析法能够从大量因子中选取出对评价或决策影响很大的因子,但是这些因子所占的权重具有很大的主观性,而粗集理论在确定因子的权重时更好地消除了人为因素的影响,这两者结合应用在GIS空间分析中,能够使得空间分析更客观、有效。     

老挝沙湾水经济区土地利用适宜性评价

基于老挝沙湾水经济区的自然、社会、经济发展状况和土地利用现状,将层次分析法、特尔菲法和Arc GIS地理空间分析技术相结合,对研究区农业用地、林业用地和建设用地的适宜性进行评价。通过单因子适宜性和综合适宜性评价,提出不同区域的土地利用空间优化配置方向。研究结果表明,农业用地主要适宜分布在研究区西侧湄公河沿岸土壤较肥沃的地区;林业用地适宜分布在研究区北部及东南山区;建设用地适宜分布在沙湾水经济区西南部基础设施条件较好且地形较平坦的地区。本研究为老挝沙湾水经济区土地利用提供了一定的数据支持,同时能够为东南亚同类地区的相关研究提供借鉴和参考。     

土地利用空间分布的多尺度分析

土地利用在不同的规模尺度上具有不同的特征,某个研究尺度上的影响因子可能在其他尺度上并不发生作用。论文运用统计方法与GIS技术从自然环境、社会经济以及基础设施条件各方面分析汉王镇土地利用空间分布影响因素及其尺度效应。综合考虑9种主要的土地利用方式和6个候选影响因子,其中土地利用数据来自土地利用详查数据,并根据其空间分布与...     

基于GIS的移民搬迁信息化管理系统的设计与实现

为了更好地实现移民搬迁信息的数字化管理和共享,本文将GIS与计算机网络技术和数据库有效地结合起来,建立了基于GIS的移民搬迁信息化管理系统。该系统实现了移民人员管理、建设项目管理、资金管理、三维显示分析、电子档案管理、领导查询,以及统计分析等功能,较大地提高了移民工作效率和移民精准度。     

测绘地理信息服务吴起县移民搬迁

<正>吴起县移民(脱贫)搬迁信息管理平台由陕西测绘地理信息局与吴起县移民管理办公室共同设计建设,主要定位于以丰富的信息化手段支撑吴起县移民搬迁工作更好、更快地开展,着力提升工作效率、辅助科学决策、直观展示搬迁成效。吴起县移民(脱贫)搬迁工程是该县"十三五"期间的重点工作之一,是坚决打赢脱贫攻坚战、确保全省同步建成小康社会的重要举措。2016年11月,吴起县移民(脱贫)搬迁信息管理平台组织框架建设正式开展。     

GIS在采煤沉陷区生态价值评价中的应用

以淮南市在采煤沉陷区生态治理规划中对规划区的生态价值评价为例,探讨了运用GIS技术和多源信息复合原理对规划区进行生态要素单因子评价和生态价值综合评价,选取评价因子包括地形因子、生态基质因子、植被因子及水环境保护因子。采用遥感与地理信息系统相结合的技术,建立与评价相关的基于GIS的基础数据平台,运用GIS的空间分析功能对规划区的生态价值进行分析和评估。通过对规划区各类生态因子的评价,深入了解规划区内生态价值的特征与空间分布,提取重要生态节点;为挖掘规划区生态建设潜力,实施生态治理和建设提供依据。     

基于GIS的工程移民DSS研究

工程移民指工程建设引起的非自愿的搬迁安置活动,工程移民决策包括规划决策和管理决策,这在国内外都是一个新课题,基于ＧＩＳ的工程移民ＤＳＳ是实施科学决策的有效手段,本文对工程移民决策支持的理论、方法进行了探讨,并结合万家寨工程移民决策支持进行了试验。     

征地补偿安置的问题、模式与对策研究

随着城镇化的加快发展,征地农民补偿安置问题成为全社会持续关注的焦点。本文梳理了长期以来征地补偿安置存在的问题,总结了各地征地补偿安置的主要模式,最后提出了未来改善征地补偿安置的对策建议。     

深圳市征地拆迁信息系统的设计与实现

提出了征地拆迁信息管理系统的总体目标和建设内容,陈述了系统的结构设计和数据库设计,分析了系统的主要技术特点。     

无人机实景三维建模在水利BIM中的应用

为满足水利工程全生命周期管理需求,提出了利用无人机单镜头大重叠摄影与多角度倾斜摄影联合作业模式,对河南汝阳前坪水库水利工程进行实景三维建模,建立了一套水库粗模和精模快速三维生成的技术流程,研究成果可为水利工程投资分析、工程设计、施工与安全质量管理、库区移民等基于BIM技术的水利工程全生命周期管理提供技术支持。     

Similarity Measurements on Multi‐Scale Qualitative Locations

Qualitative representation of spatial locations and their similarity measurements are essential for the analysis of linguistic term‐based data. Existing methods have focused on the similarities of spatial relations and spatial scenes but have not considered the variations in geometrical representations and relations over scales. This study developed some new measures to assess the similarities of both single‐ and multi‐scale qualitative locations. Region‐ and cell‐based models were used to formalize qualitative locations of spatial objects with respect to multi‐scale frames of reference. The similarities were assessed by integrating the similarities of frames and qualitative relations. The frame similarity measures how two objects are compared considering the common elements that they occupy in the reference frames. Moreover, the similarity of qualitative relation measures how two relations relate two objects to the corresponding elements in the frames. The location similarities at a single level integrate the similarities of the frames and qualitative relations, whereas the location similarities at multiple scales incorporate the variations in qualitative locations over scales. These methods were used to assess location similarities concerning residential areas, roads, and lakes. The results indicated that the location‐based measurements can disclose the distributions of the similarities and that the cell‐based model is more accurate than the region‐based model.     

Vineyard site suitability analysis by use of multicriteria approach applied on geo-spatial data

Abstract

In this research, multicriteria decision analysis with pairwise comparison weighting method was utilized to determine the suitable locations for vineyard plantation in Sarkoy region of Turkey. Soil maps, meteorological measurements, slope, aspect and elevation maps were used as input to conduct spatial analysis. Different methods were compared and pairwise comparison method was identified as the most appropriate method of weighting for this spatial analysis. Current vineyard areas were determined using Worldview-2 imagery and their spatial distribution compared with the resulting suitability map to determine the current suitability. Comparisons showed current vineyards were mostly established in locations where suitability map expresses low capability. Further inspection unveiled that, these low capability lands are closer to the transportation networks and city/county centres that tend to be in sea level elevations as opposed to vine grapes thriving in higher altitudes. Results also enabled providing suggestions on alternative sites for new vineyard plantation.     

青海省地震灾后地物变化的快速识别

地震灾后救援时间宝贵,有效利用灾后数据,快速对救援路线及居民情况进行准确的评估,能有效避免地震带来的进一步损害。地震并不是一次即停止,灾后余震同样威胁灾区群众的生命和财产安全。因此,根据灾区地质及居民分布制定后续可持续救援方案,也是灾后救援的重点,这不仅能使救援稳步进行,也可进一步规划安置居民。本文通过比较神经网络和传统机器学习方法,提出了自适应方法的地物分割算法,根据航拍影像的复杂程度使用神经网络和传统算法相结合的方式,通过比较识别出地物与原有测量数据的差别变化,判断灾后道路扭曲和地质变化,为救援工作提供准确的科学依据。     

Street‐level Spatial Interpolation Using Network‐based IDW and Ordinary Kriging

This study proposes network‐based spatial interpolation methods to help predict unknown spatial values along networks more accurately. It expands on two of the commonly used spatial interpolation methods, IDW (inverse distance weighting) and OK (ordinary kriging), and applies them to analyze spatial data observed on a network. The study first provides the methodological framework, and it then examines the validity of the proposed methods by cross‐validating elevations from two contrasting patterns of street network and comparing the MSEs (Mean Squared Errors) of the predicted values measured with the two proposed network‐based methods and their conventional counterparts. The study suggests that both network‐based IDW and network‐based OK are generally more accurate than their existing counterparts, with network‐based OK constantly outperforming the other methods. The network‐based methods also turn out to be more sensitive to the edge effect, and their performance improves after edge correction. Furthermore, the MSEs of standard OK and network‐based OK improve as more sample locations are used, whereas those of standard IDW and network‐based IDW remain stable regardless of the number of sample locations. The two network‐based methods use a similar set of sample locations, and their performance is inherently affected by the difference in their weight distribution among sample locations.     

A deep learning approach for rooftop geocoding

Geocoding has become a routine task for many research investigations to conduct spatial analysis. However, the output quality of geocoding systems is found to impact the conclusions of subsequent studies that employ this workflow. The published development of geocoding systems has been limited to the same set of interpolation methods and reference data sets for quite some time. We introduce a novel geocoding approach utilizing object detection on remotely sensed imagery based on a deep learning framework to generate rooftop geocoding output. This allows geocoding systems to use and output exact building locations without employing typical geocoding interpolation methods or being completely limited by the availability of reference data sets. The utility of the proposed approach is demonstrated over a sample of 22,481 addresses resulting in significant spatial error reduction and match rates comparable to typical geocoding methods. For different land‐use types, our approach performs better on low‐density residential and commercial addresses than on high‐density residential addresses. With appropriate model setup and training, the proposed approach can be extended to search different object locations and to generate new address and point‐of‐interest reference data sets.