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三生空间协调度的中泉镇农村居民点布局优化 总被引：3，自引：0，他引：3
针对构建乡村振兴新格局中分类推进乡村发展的问题,基于三生空间协调度模型,该文选取甘肃省景泰县中泉镇为研究区,开展三生空间协调度计算与分析,并结合居民点布局优化方向,将中泉镇居民点优化类型划分为城郊融合型、集聚提升型、规模管控型和搬迁撤并型4类。实验表明:中泉镇城郊融合型居民点共有21个,占比2.73%,主要分布在中庄村;集聚提升型居民点392个,占比52.84%,分布在龙湾村、腰水村、三合村、尾泉村、胡麻水村和长生村;规模管控型居民点共有365个,占比32.16%,分布在红砚台村和大水村;搬迁撤并型居民点共有95个,占比12.26%,分布在崇华村和白水村。该研究可为乡镇尺度农村居民点布局优化提供参考。 相似文献
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利用Voronoi图的城乡居民点布局优化研究 总被引：4，自引：0，他引：4
本文综合考虑居民点现状布局及相邻居民点间相互影响,以山西省晋城市为例,应用Voronoi图理论,依据熵值、聚类指数、标准差3个测度组合,将晋城市居民点划分为4种整理类型并绘制城乡用地布局等级图,结合2020年晋城市居民点用地规模预测值,进行晋城市居民点布局优化。结果显示,考虑居民点布局现状,利用Voronoi图理论及其聚合形态测度检验,为城乡居民点布局优化方案的提出提供了空间理论依据,是布局优化的直观方法。 相似文献
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农村居民点整理潜力分级是为了有效划分用地整理潜力而进行的,通过确定分级方法,合理划分出农村居民点的土地整理潜力级,有助于农村居民点土地整理相关规划的编制。本文综合考虑了影响农村居民点整理潜力的自然、经济、社会三方面因素,构建了农村居民点整理潜力评价分级的指标体系,运用AHP法确定评价分级指标的权重,采用综合评价和聚类分析相结合的方法进行了整理潜力评价分级。将淮安市农村居民点整理潜力区划分为5级,并针对不同级别的潜力区域提出了相关整理模式和用地策略。 相似文献
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不同农村居民点整理模式下的耕地潜力评价模型 总被引：5，自引：1，他引：4
设计了农村居民点整理复耕流程,划分了农村居民点城镇化、内部改造与迁村并点3种整理模式,统筹区域农户意愿与经济条件两大影响因子,分标准定量评价不同整理模式下的现实整理潜力。综合考虑坡度、耕作半径、耕地连片度、居耕比等影响因子,以GIS空间分析为平台,构建了不同农村居民点整理模式下的耕地潜力评价模型。 相似文献
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基于GIS的垦区农村居民点空间分布特征及影响因素分析 总被引：2，自引：0，他引：2
文章运用景观生态学理论与GIS空间分析技术,以宝泉岭垦区为例,对农村居民点的分布特点及其影响因素进行分析。结果表明：垦区农村居民点所占建设用地比重高,亟需统筹规划;农村居民点用地规模和密度在地域分布上存在明显差别;全区斑块形状较规则、集聚度较高。这种分布受河流、公路、经济环境和耕地条件的影响。在未来布局农村居民点用地时要考虑农业产业结构和布局的调整,使农村居民点更有利于农民的劳作。 相似文献
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加权Voronoi图在农村居民点布局优化中的应用研究 总被引：8，自引：0，他引：8
充分考虑农村居民点的微观状况,构建了农村居民点综合影响力评价指标体系,用于评价各居民点的综合影响力,并据此将农村居民点划分为中心村、保留村及零星居民点(搬迁村)3类。引入城市地理学空间分割方法,利用加权Voronoi图划分各中心村的综合影响力范围,确定各搬迁居民点的安置去向,以维持原有的社交范围和生活习惯。 相似文献
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研究目的：通过对佛冈县农村居民点的现状分析,运用SPSS软件的分析功能,筛选出影响农村居民点集约利用的评价指标,构建农村居民点集约利用的评价指标体系。采用层次分析法及DPS软件的矩阵计算功能,确定各项指标的权重。最终,根据评价指标的权重和无量纲化值,运用二次综合函数,确定目标的集约利用度。依据评价结果,提出提高佛冈县农村居民点用地集约利用的措施和意见。评价结果：经过探讨和评价,佛冈县各镇的集约利用度从高到低依次为水头镇、迳头镇、龙山镇、高岗镇和汤塘镇。其中,水头镇的农村居民点用地的集约利用程度最高,但属于文中划分标准的第Ⅱ等级,归于较集约利用的范畴;汤塘镇、迳头镇、高岗镇和汤塘镇的农村居民点用地的集约利用度属于基本节约的范畴,属于文中划分标准的第Ⅲ等级。总体来说,佛冈县的农村居民点的集约利用水平较低。在评价佛冈县各镇农村居民点集约利用水平的基础上,针对佛冈县农村居民点集约利用的现状,提出了相关措施和建议。 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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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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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
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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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Hidden negative spatial autocorrelation 总被引：3，自引：3，他引：0
Daniel A. Griffith 《Journal of Geographical Systems》2006,8(4):335-355
Mostly lip service treatments of negative spatial autocorrelation (NSA) appear in the literature, although spatial scientists confront it in practice. NSA was detected serendipitously in recalcitrant empirical analyses containing a sizeable amount of global positive spatial autocorrelation (PSA) unaccounted for by standard spatial statistical models, and labeled hidden because conventional spatial statistical tools detected only PSA while giving absolutely not hint of NSA existing. The meaning of this phenomenon is explored empirically, with findings including: a better understanding of NSA, spatial filter model construction guidelines, effective illustrations of NSA, and how hidden NSA furnishes a diagnostic for model misspecification.
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| Daniel A. GriffithEmail: Phone: +1-972-8834950Fax: +1-972-8836297 |