共查询到20条相似文献，搜索用时 140 毫秒
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鲁东南新石器遗址时空格局与自然环境的关系 总被引：1，自引：0，他引：1
针对史前文化遗址的时空间分布进行研究以进一步分析环境变化对人类聚居地带来的影响,对发现和保护历史文化遗址具有重要的意义。该文运用GIS空间分析方法对鲁东南地区史前遗址的时空分布特征进行分析,并结合区域地形地貌等条件分析遗址与自然环境条件的关系。结果表明:遗址点主要分布在高程50～200m、坡度0～6°、坡向朝南且距离河流200～2 100m的范围内;且遗址点的分布呈现一定的聚集性,不同时期人类活动聚集中心存在迁移现象。由此得出古代人类倾向于选择海拔高度低,坡度小,向阳且靠近水源的地方聚居,且不同时期农业经济类型和社会的发展程度一定程度的影响了人类活动中心的位置分布。探究了鲁东南地区3个时期遗址时空分布和自然环境之间的关系,分析了不同时期人类活动中心的聚散特征。 相似文献
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《北京测绘》2020,(4):F0003-F0003
聚落的形态研究是综合反映社会组织结构和文明发展研究中的重要内容之一,是理解聚落聚集、文明发展、时空演化的重要手段,也是考古研究领域重视的问题之一。随着空间分析技术的发展,考古学者开始应用空间分析方法研究聚落,空间技术和多因子加权评价方法减少了依靠专业知识经验带来的主观性。本文以中华文明探源工程项目为背景,以聚落形态研究为目标,采用空间信息技术、分形理论、群体智能方法对临汾地区四个时期(7kaB.P.~2.7 kaB.P.期间,仰韶时期、庙底沟二期、龙山时期、夏商周时期)的聚落形态进行综合分析,形成了适合于黄河流域先秦时期聚落形态研究的方法,并以黄河流域文明发展的重要区域-临汾为示范区开展了聚落形态及其演化分析研究,同时结合气候、地貌、水系流域等环境因素对四个文化时期的聚落遗址发展演化情况作了详细的论述。论文主要创新点如下:(1)构建了宏观-微观尺度的聚落形态研究体系。宏观上采用空间聚类、分形及出行空间聚落集群分析方法进行聚落形态研究,微观上从每个聚落遗址的出行交流出发,模拟出行路线以重建聚落出行交流主干线,建立了适合于黄河流域聚落形态研究的多方法综合研究体系,可用于流域内不同文化类型聚落遗址的聚落形态研究。(2)创新性地提出了基于地形约束空间距离算法的聚落形态研究方法,用于解决聚落聚类过程中对地形考虑不足的问题,使得聚落群划分结果更加客观合理,更适合聚落形态研究的目的。(3)提出了基于梯度下降的变邻域蚁群算法出行模拟方法,结合地形和人类出行预判经验,提高了古人类出行模拟的真实性。采用方向约束和全局及局部优化规则以提高算法效率,应用于临汾地区先秦聚落的出行模拟中,以重建的古人类出行交流主干线为基础,分析聚落形态。(4)从聚落遗址出行空间角度,以地形、生物机能等多特征出行网络为基础,提出了基于出行空间的聚落集群分析方法,实现了日常出行局部范围内的聚落集群分析。 相似文献
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顾及空间自相关的地理国情信息统计格网尺度选择——以植被覆盖信息统计为例 总被引：1，自引：0，他引：1
统计格网尺度的不同会带来统计结果的差异,如何选择统计格网是地理国情信息统计的重要工作。本文提出了一种顾及空间自相关的地理国情信息统计格网尺度选择方法。采用地理国情普查数据,在50 m、60 m、70 m、80 m、90 m、100 m、250 m、500 m和1000 m几个尺度下,以植被覆盖信息统计为例,利用面积占优法和中心点归属法两种方法分别进行格网化,得到了不同尺度的植被格网数据;计算植被覆盖面积统计误差,分析不同尺度下植被覆盖信息的空间自相关的变化特征,并利用Moran’s I系数差值进行尺度选择,得到了植被覆盖信息统计格网的适宜尺度。以龙沙区和清涧县作为研究区域,结果表明,在地理国情植被覆盖信息统计时,不同地区的格网统计适宜尺度是不一样的,植被覆盖度中低的龙沙区的适宜尺度为100 m,而植被覆盖度高的清涧县的适宜尺度为250 m。 相似文献
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空间数据库是分析和挖掘区域复杂时空体系中的聚落分布和传承规律的基础。选取了对研究中原文明乃至中华文明起源具有代表意义的环嵩山地区,从该区域聚落空间数据特点、研究需求入手,分析了环嵩山地区史前聚落数据库的建设思路和数据库的主要功能,提出了聚落空间数据库的设计和实现方案,为该区域聚落考古研究提供数据和技术支持。 相似文献
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针对榆林东北部地区新石器时代的环境宜居性分布规律进行研究,通过SOFM神经网络模型对研究区聚落等级进行划分,结合地形高程、坡度、坡向、距水系距离、植被覆盖度等因子,构建指数模型。研究结果表明,研究区遗址大都分布在海拔1 000~1 200m、坡度3~9°、距水系距离为0~800m、坡向为阳坡以及植被覆盖度较好的区域,一级聚落均分布在古代环境宜居性较高的区域。与仅使用地形因子建立的指数模型相比,加入植被覆盖度和聚落等级因子的模型对不宜居的沙漠和遗址分布空白区域划分的宜居性等级低,对遗址分布密集的宜居区域划分的宜居性等级高,宜居性等级划分结果与各等级遗址密度分布的客观事实更为吻合,综合因子模型对区域宜居性等级划分更为精确。 相似文献
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《测绘科学》2017,(9)
针对榆林东北部地区新石器时代的环境宜居性分布规律进行研究,通过SOFM神经网络模型对研究区聚落等级进行划分,结合地形高程、坡度、坡向、距水系距离、植被覆盖度等因子,利用指数变异法客观确定各因子的相对重要性并构建指数模型。研究结果表明,研究区遗址大都分布在海拔1 000~1 200 m、坡度3~9°、距水系距离为0~800 m、坡向为阳坡以及植被覆盖度较好的区域,一级聚落均分布在古代环境宜居性较高的区域。与仅使用地形因子建立的指数模型相比,加入植被覆盖度和聚落等级因子的模型对不宜居的沙漠和遗址分布空白区域划分的宜居性等级低,对遗址分布密集的宜居区域划分的宜居性等级高,宜居性等级划分结果与各等级遗址密度分布的客观事实更为吻合,综合因子模型对区域宜居性等级划分更为精确。 相似文献
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Daniel A. Griffith 《Journal of Geographical Systems》2002,4(1):43-51
As either the spatial resolution or the spatial scale for a geographic landscape increases, both latent spatial dependence
and spatial heterogeneity also will tend to increase. In addition, the amount of georeferenced data that results becomes massively
large. These features of high spatial resolution hyperspectral data present several impediments to conducting a spatial statistical
analysis of such data. Foremost is the requirement of popular spatial autoregressive models to compute eigenvalues for a row-standardized
geographic weights matrix that depicts the geographic configuration of an image's pixels. A second drawback arises from a
need to account for increased spatial heterogeneity. And a third concern stems from the usefulness of marrying geostatistical
and spatial autoregressive models in order to employ their combined power in a spatial analysis. Research reported in this
paper addresses all three of these topics, proposing successful ways to prevent them from hindering a spatial statistical
analysis. For illustrative purposes, the proposed techniques are employed in a spatial analysis of a high spatial resolution
hyperspectral image collected during research on riparian habitats in the Yellowstone ecosystem.
Received: 25 February 2001 / Accepted: 2 August 2001 相似文献
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Peter Rogerson 《Journal of Geographical Systems》2005,7(1):101-114
When assessing maps consisting of comparable regional values, it is of interest to know whether the peak, or maximum value, is higher than it would likely be by chance alone. Peaks on maps of crime or disease might be attributable to random fluctuation, or they might be due to an important deviation from the baseline process that produces the regional values. This paper addresses the situation where a series of such maps are observed over time, and it is of interest to detect statistically significant deviations between the observed and expected peaks as quickly as possible. The Gumbel distribution is used as a model for the statistical distribution of extreme values; this distribution does not require the underlying distributions of regional values to be either normal, known, or identical. Cumulative sum surveillance methods are used to monitor these Gumbel variates, and these methods are also extended for use when monitoring smoothed regional values (where the quantity monitored is a weighted sum of values in the immediate geographical neighborhood). The new methods are illustrated by using data on breast cancer mortality for the 217 counties of the northeastern United States, and prostate cancer mortality for the entire United States, during the period 1968-1998.The research assistance of Ikuho Yamada is gratefully acknowledged. I also am grateful for the support of Grant 1R01 ES09816-01 from the National Institutes of Health, the support of National Cancer Institute Grant R01 CA92693-0, and the helpful comments made by the referees 相似文献
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针对农村居民点用地布局优化研究未考虑规划影响的问题,该文提出了集成空间相互作用理论与规划影响的方法。首先参照城镇发展规划对农村居民点类型进行划分,然后依据新农村建设的要求和研究区的实际情况,从发展基础、生产条件、生活条件和生态环境4个方面建立了农村居民点潜能评价指标体系,根据修正后的潜能模型计算各农村居民点的潜能值,将孟河镇农村居民点划分为城镇化型、中心村、基层村、拆并村4种类型。在对农村居民点类型微调后,针对不同的类型提出相应的布局优化方案,对推进农村居民点整理具有一定的指导意义。 相似文献
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To design retrieval algorithm of spatial relations for spatial objects with randomness in GIS, this paper builds up the membership functions based on set theory idea, used for determination of topological spatial relations between random objects, such as between point and point, point and line or polygon, which provides theoretical basis for retrieving spatial relations between certain and random objects. Finally, this paper interprets detailed methods and steps of realizing them by means of some simple examples under the GIS's environment. 相似文献
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DENG MinLI ChengmingLIU Wenbao 《地球空间信息科学学报》2001,4(4):43-48
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Dewayany Sutrisno Suzan Novtalia Gill Suseno Suseno 《International Journal of Digital Earth》2018,11(9):863-879
A major problem associated with marine spatial planning (MSP) involves the difficult and time-consuming practice of creating a scenario that encompasses complex datasets in near real time via the use of a simple spatial analysis method. Moreover, decision-makers require a reliable, user-friendly system to quickly and accessibly acquire accurate spatial planning information. The development of national spatial data infrastructure (NSDI), which links the spatial data of a nation’s many diverse institutions, may pave the way for the development of a tool that can better utilize spatial datasets, such as a spatial decision support system (SDSS). Thus, this project aimed to develop an SDSS for MSP and to evaluate the feasibility of its integration within the NSDI framework. The seaweed culture was selected as an example due to its economic and technological acceptance by traditional fishers. Additionally, a multi-criteria analysis was used to develop the tool. Furthermore, a feasibility evaluation of its implementation within the NSDI framework was conducted based on the Delphi method. The results of the assessment indicated that the SDSS can be incorporated into the NSDI framework by addressing the policy issue – one map policy, updating custodians’ decree and data, and improve the standard and protocol. 相似文献
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Seagrass habitats in subtidal coastal waters provide a variety of ecosystem functions and services and there is an increasing need to acquire information on spatial and temporal dynamics of this resource. Here, we explored the capability of IKONOS (IKO) data of high resolution (4 m) for mapping seagrass cover [submerged aquatic vegetation (%SAV) cover] along the mid-western coast of Florida, USA. We also compared seagrass maps produced with IKO data with that obtained using the Landsat TM sensor with lower resolution (30 m). Both IKO and TM data, collected in October 2009, were preprocessed to calculate water depth invariant bands to normalize the effect of varying depth on bottom spectra recorded by the two satellite sensors and further the textural information was extracted from IKO data. Our results demonstrate that the high resolution IKO sensor produced a higher accuracy than the TM sensor in a three-class % SAV cover classification. Of note is that the OA of %SAV cover mapping at our study area created with IKO data was 5–20% higher than that from other studies published. We also examined the spatial distribution of seagrass over a spatial range of 4–240 m using the Ripley’s K function [L(d)] and IKO data that represented four different grain sizes [4 m (one IKO pixel), 8 m (2 × 2 IKO pixels), 12 m (3 × 3 IKO pixels), and 16 m (4 × 4 IKO pixels)] from moderate-dense seagrass cover along a set of six transects. The Ripley’s K metric repeatedly indicated that seagrass cover representing 4 m × 4 m pixels displayed a dispersed (or slightly dispersed) pattern over distances of <4–8 m, and a random or slightly clustered pattern of cover over 9–240 m. The spatial pattern of seagrass cover created with the three additional grain sizes (i.e., 2 × 24 m IKO pixels, 3 × 34 m IKO pixels, and 4 × 4 m IKO pixels) show a dispersed (or slightly dispersed) pattern across 4–32 m and a random or slightly clustered pattern across 33–240 m. Given the first report on using satellite observations to quantify seagrass spatial patterns at a spatial scale from 4 m to 240 m, our novel analyses of moderate-dense SAV cover utilizing Ripley’s K function illustrate how data obtained from the IKO sensor revealed seagrass spatial information that would be undetected by the TM sensor with a 30 m pixel size. Use of the seagrass classification scheme here, along with data from the IKO sensor with enhanced resolution, offers an opportunity to synoptically record seagrass cover dynamics at both small and large spatial scales. 相似文献