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利用核密度与空间自相关进行城市设施兴趣点分布热点探测 总被引：3，自引：0，他引：3
《武汉大学学报(信息科学版)》2016,(2)
城市设施兴趣点(POI)在局部地理空间下往往呈现聚集型分布特征(即热点),表达该特征的核密度法(kernel density estimation)是最常用到的可视化工具。考虑到核密度方法中缺少量化统计分析,提出了一种城市设施POI分布热点探测的新方法。首先基于"距离衰减效应"计算地理单元的属性值;然后采用GetisOrd G*i统计指数定量分析设施POI点的局部空间相关性特征。与传统基于样方法的空间自相关相比,核密度法由于顾及了地理学第一定律的区位影响,计算获得的地理单元属性值可保留空间的细节信息,热点的空间自相关分析结果可以反映设施服务影响的连续性特征。通过实际金融设施数据的自相关分析实验,表明该方法能有效提取POI基础设施在城市区域中的分布热点范围。 相似文献
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针对城市犯罪分布环境的研究问题,本文基于兰州市的犯罪统计数据,结合POI大数据,使用空间统计和GIS空间分析方法提取了兰州市3种类型犯罪的热点区,并进一步使用POI数据探索了犯罪热点区内的环境特征。结果表明:1)兰州市4个城区内犯罪分布情况不同,主要集中在人口较多的黄河南岸,其中又以西站、西湖公园、"西关十字—南关十字"一线、西固城步行街等区域为主,城关区作为中心城区,犯罪行为分布范围广,犯罪高发区多。2) 3类犯罪热点区的设施点环境多数是多类型POI设施共同主导,少数是单类型POI设施主导,但起主导作用的POI类型都能反映出对应的主要犯罪类型对城市环境的要求特点。3) 3类犯罪对人流、财富以及场所隐蔽性的趋向性不同,并在对应环境的POI分布特征中得到体现。 相似文献
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设施POI(point of interest)在城市地理空间中往往聚集分布,呈现热点特征。对该类POI分布热点的分析大多采用基于欧氏距离的空间密度估计,忽略了城市空间通达、连接是沿着街道路径的事实,从而很难准确、客观地反映城市功能的热点布局。本研究针对该缺陷,利用基于网络路径距离的核密度计算方法确定热点的区域密度,并提出了一种简单、高效的网络分析算法。该算法扩展二维栅格膨胀操作,以一维形态算子的连续扩展计算POI在网络单元上的密度值,通过评价试验表明,该算法比现有算法具有更好的性能和可扩展性。通过实际POI数据分析发现,考虑街道网络约束的热点范围可凸显设施功能沿交通网络布局的空间特征,为区域规划、导航以及地理信息查询等应用提供有价值的空间知识与信息服务。 相似文献
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随着科技的迅速发展,地理空间数据已经成为人们日常生活中的一部分,城市POI数据则是涵盖城市公共基础设施属性信息与位置信息的大型地理空间数据。针对现阶段对城市POI数据利用程度不充分的问题,本文运用密度分析、热点分析以及服务范围分析等方法研究大连金普新区城市POI数据的空间分布特征。结果表明:大连金普新区城市POI数据呈现"二区八带多点"的分布格局,金石滩街道、中长街道、先进街道以及大李家街道医疗、教育、娱乐设施较为完善,且远离工厂,比较宜居。本研究可为市民生活、工作、学习、娱乐提供参考,也可为城市发展、管理维护提供服务。 相似文献
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《测绘与空间地理信息》2020,(4)
采用数据挖掘技术,从百度地图获取广州市的科教文化POI数据,利用最邻近距离指数法、核密度估计法、多距离空间聚类分析法,对广州市的科教文化分布进行分析,探讨科教文化服务的分布特征。结果表明:在空间集聚特征上,广州市的各类科教文化服务的NNI均小于1,呈现显著的聚集特征;在空间分布特征上,中心城区高度聚集,从中心城区向外围区逐渐发散;从空间多尺度特征上,聚集程度的增大趋势均呈现出现增加后减少的趋势。 相似文献
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城市公共中心高度聚集了各类城市公共管理与公共服务设施。近年来城市建设发展迅速,传统的抽样调查等方法已不能满足新时代需要。互联网与计算机技术的发展给城市研究提供了新思路:城市兴趣点(POI)数据涵盖了广泛的地理空间和位置属性并且对公众开放。本研究使用POI数据,结合核密度估算(KED)模型定量研究其空间分布规律并对上海市城市中心体系的识别。结果显示,根据POI数据分析识别的上海市城市中心体系整体呈现单中心发展态势,城市副中心与郊区中心发展力度不足,尚有待进一步建设。将结果与《上海市城市总体规划(1999—2020年)》确定的城市公共中心体系进行对比,提出优化建议。 相似文献
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Heavy metals contaminated soils and water will become a major environmental issue in the mining areas. This paper intends to use field hyper-spectra to estimate the heavy metals in the soil and water in Wan-sheng mining area in Chongqing. With analyzing the spectra of soil and water, the spectral features deriving from the spectral of the soils and water can be found to build the models between these features and the contents of Al, Cu and Cr in the soil and water by using the Stepwise Multiple Linear Regression (SMLR). The spectral features of Al are: 480 nm, 500 nm, 565 nm, 610 nm, 680 nm, 750 nm, 1000 nm, 1430 nm, 1755 nm, 1887 nm, 1920 nm, 1950 nm, 2210 nm, 2260 nm; The spectral features of Cu are: 480 nm, 500 nm, 610 nm, 750 nm, 860 nm, 1300 nm, 1430 nm, 1920 nm, 2150 nm, 2260 nm; And the spectral features of Cr are: 480 nm, 500 nm, 610 nm, 715 nm, 750 nm, 860 nm, 1300 nm, 1430 nm, 1755 nm, 1920 nm, 1950 nm. With these features, the best models to estimate the heavy metals in the study area were built according to the maximal R2. The R2 of the models of estimating Al, Cu and Cr in the soil and water are 0.813, 0.638, 0.604 and 0.742, 0.584, 0.513 respectively. And the gradient maps of these three types of heavy metals’ concentrations can be created by using the Inverse distance weighted (IDW).The gradient maps indicate that the heavy metals in the soil have similar patterns, but in the North-west of the streams in the study area, the contents are of great differences. These results show that it is feasible to predict contaminated heavy metals in the soils and streams due to mining activities by using the rapid and cost-effective field spectroscopy. 相似文献
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Soil erodibility, which is difficult to estimate and upscaling, was determined in this study using multiple spectral models of soil properties (soil organic matter (SOM), water-stable aggregates (WSA) > 0.25 mm, the geometric mean radius (Dg)). Herein, the soil erodibility indicators were calculated, and soil properties were quantitatively analyzed based on laboratory simulation experiments involving two selected contrasting soils. In addition, continuous wavelet transformation was applied to the reflectance spectra (350–2500 nm) of 65 soil samples from the study area. To build the relationship, the soil properties that control erodibility were identified prior to the spectral analysis. In this study, the SOM, Dg and WSA >0.25 mm were selected to represent the most significant soil properties controlling erodibility and describe the erodibility indicator based on a logarithmic regression model as a function of SOM or WSA > 0.25 mm. Five, six and three wavelet features were observed to calibrate the estimated soil properties model, and the best performance was obtained with a combination feature regression model for SOM (R2 = 0.86, p < 0.01), Dg (R2 = 0.79, p < 0.01) and WSA >0.25 mm (R2 = 0.61, p < 0.01), respectively. One part of the wavelet features captured amplitude variations in the broad shape of the reflectance spectra, and another part captured variations in the shape and depth of the soil dry substances. The wavelet features for the validated dataset used to predict the SOM, WSA >0.25 mm and Dg were not significantly different compared with the calibrated dataset. The synthesized spectral models of soil properties, and the formation of a new equation for soil erodibility transformed from the spectral models of soil properties are presented in this study. These results show that a spectral analytical approach can be applied to complex datasets and provide new insights into emerging dynamic variation with erodibility estimation. 相似文献
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This paper presents a technique developed for the retrieval of the orientation of crop rows, over anthropic lands dedicated to agriculture in order to further improve estimate of crop production and soil erosion management. Five crop types are considered: wheat, barley, rapeseed, sunflower, corn and hemp. The study is part of the multi-sensor crop-monitoring experiment, conducted in 2010 throughout the agricultural season (MCM’10) over an area located in southwestern France, near Toulouse. The proposed methodology is based on the use of satellite images acquired by Formosat-2, at high spatial resolution in panchromatic and multispectral modes (with spatial resolution of 2 and 8 m, respectively). Orientations are derived and evaluated for each image and for each plot, using directional spatial filters (45° and 135°) and mathematical morphology algorithms. “Single-date” and “multi-temporal” approaches are considered. The single-date analyses confirm the good performances of the proposed method, but emphasize the limitation of the approach for estimating the crop row orientation over the whole landscape with only one date. The multi-date analyses allow (1) determining the most suitable agricultural period for the detection of the row orientations, and (2) extending the estimation to the entire footprint of the study area. For the winter crops (wheat, barley and rapeseed), best results are obtained with images acquired just after harvest, when surfaces are covered by stubbles or during the period of deep tillage (0.27 > R2 > 0.99 and 7.15° > RMSE > 43.02°). For the summer crops (sunflower, corn and hemp), results are strongly crop and date dependents (0 > R2 > 0.96, 10.22° > RMSE > 80°), with a well-marked impact of flowering, irrigation equipment and/or maximum crop development. Last, the extent of the method to the whole studied zone allows mapping 90% of the crop row orientations (more than 45,000 ha) with an error inferior to 40°, associated to a confidence index ranging from 1 to 5 for each agricultural plot. 相似文献
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Xia Yao Yan ZhuYongChao Tian Wei FengWeiXing Cao 《International Journal of Applied Earth Observation and Geoinformation》2010
Hyperspectral sensing can provide an effective means for fast and non-destructive estimation of leaf nitrogen (N) status in crop plants. The objectives of this study were to design a new method to extract hyperspectral spectrum information, to explore sensitive spectral bands, suitable bandwidth and best vegetation indices based on precise analysis of ground-based hyperspectral information, and to develop regression models for estimating leaf N accumulation per unit soil area (LNA, g N m−2) in winter wheat (Triticum aestivum L.). Three field experiments were conducted with different N rates and cultivar types in three consecutive growing seasons, and time-course measurements were taken on canopy hyperspectral reflectance and LNA under the various treatments. Then, normalized difference spectral indices (NDSI) and ratio spectral indices (RSI) based on the original spectrum and the first derivative spectrum were constructed within the range of 350–2500 nm, and their relationships with LNA were quantified. The results showed that both LNA and canopy hyperspectral reflectance in wheat changed with varied N rates, with consistent patterns across different cultivars and seasons. The sensitive spectral bands for LNA existed mainly within visible and near infrared regions. The best spectral indices for estimating LNA in wheat were found to be NDSI (R860, R720), RSI (R990, R720), NDSI (FD736, FD526) and RSI (FD725, FD516), and the regression models based on the above four spectral indices were formulated as Y = 26.34x1.887, Y = 5.095x − 6.040, Y = 0.609 e3.008x and Y = 0.388x1.260, respectively, with R2 greater than 0.81. Furthermore, expanding the bandwidth of NDSI (R860, R720) and RSI (R990, R720) from 1 nm to 100 nm at 1 nm interval produced the LNA monitoring models with similar performance within about 33 nm and 23 nm bandwidth, respectively, over which the statistical parameters of the models became less stable. From testing of the derived equations, the model for LNA estimation on NDSI (R860, R720), RSI (R990, R720), NDSI (FD736, FD526) and RSI (FD725, FD516) gave R2 over 0.79 with more satisfactory performance than previously reported models and physical models in wheat. It can be concluded that the present hyperspectral parameters of NDSI (R860, R720), RSI (R990, R720), NDSI (FD736, FD526) and RSI (FD725, FD516) can be reliably used for estimating LNA in winter wheat. 相似文献
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Gianfranco Piras Kieran P. Donaghy Giuseppe Arbia 《Journal of Geographical Systems》2007,9(4):311-344
This paper presents an innovative approach to the study of regional economic dynamics within a nonlinear continuous-time econometric
framework—a generalized specification of the Lotka–Volterra system of equations. This specification, which accounts for interdependent
behavior of three industrial sectors and spillover effects of activities in neighboring regions, is employed in an analysis
of five Italian regions between 1980 and 2003. For these regions, we report estimation results, characterize the varying systems
dynamics, analyze the models’ local and global stability properties, and determine via sensitivity analyses which structural
features appear to exert the greatest influence on these properties.
相似文献
| Kieran P. DonaghyEmail: |
16.
Wenbin Shen Jin Li Jiancheng Li Zhengtao Wang Jinsheng Ning Dingbo Chao 《地球空间信息科学学报》2008,11(4):273-278
Given the second radial derivative Vrr（P） ｜δs of the Earth＇s gravitational potential V（P） on the surface δS corresponding to the satellite altitude, by using the fictitious compress recovery method, a fictitious regular harmonic field rrVrr（P）^＊ and a fictitious second radial gradient field V：（P） in the domain outside an inner sphere Ki can be determined, which coincides with the real field V（P） in the domain outside the Earth. Vrr^＊（P）could be further expressed as a uniformly convergent expansion series in the domain outside the inner sphere, because rrV（P）^＊ could be expressed as a uniformly convergent spherical harmonic expansion series due to its regularity and harmony in that domain. In another aspect, the fictitious field V^＊（P） defined in the domain outside the inner sphere, which coincides with the real field V（P） in the domain outside the Earth, could be also expressed as a spherical harmonic expansion series. Then, the harmonic coefficients contained in the series expressing V^＊（P） can be determined, and consequently the real field V（P） is recovered. Preliminary simulation calculations show that the second radial gradient field Vrr（P） could be recovered based only on the second radial derivative V（P）｜δs given on the satellite boundary. Concerning the final recovery of the potential field V（P） based only on the boundary value Vrr （P）｜δs, the simulation tests are still in process. 相似文献
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Tatenda Dalu Timothy Dube P. William Froneman Mwazvita T.B. Sachikonye Bruce W. Clegg Tamuka Nhiwatiwa 《国际地球制图》2015,30(10):1130-1143
Traditional approaches to monitoring aquatic systems are often limited by the need for data collection which often is time-consuming, expensive and non-continuous. The aim of the study was to map the spatio-temporal chlorophyll-a concentration changes in Malilangwe Reservoir, Zimbabwe as an indicator of phytoplankton biomass and trophic state when the reservoir was full (year 2000) and at its lowest capacity (year 2011), using readily available Landsat multispectral images. Medium-spatial resolution (30 m) Landsat multispectral Thematic Mapper TM 5 and ETM+ images for May to December 1999–2000 and 2010–2011 were used to derive chlorophyll-a concentrations. In situ measured chlorophyll-a and total suspended solids (TSS) concentrations for 2011 were employed to validate the Landsat chlorophyll-a and TSS estimates. The study results indicate that Landsat-derived chlorophyll-a and TSS estimates were comparable with field measurements. There was a considerable wet vs. dry season differences in total chlorophyll-a concentration, Secchi disc depth, TSS and turbidity within the reservoir. Using Permutational multivariate analyses of variance (PERMANOVA) analysis, there were significant differences (p < 0.0001) for chlorophyll-a concentration among sites, months and years whereas TSS was significant during the study months (p < 0.05). A strong positive significant correlation among both predicted TSS vs. chlorophyll-a and measured vs. predicted chlorophyll-a and TSS concentrations as well as an inverse relationship between reservoir chlorophyll-a concentrations and water level were found (p < 0.001 in all cases). In conclusion, total chlorophyll-a concentration in Malilangwe Reservoir was successfully derived from Landsat remote sensing data suggesting that the Landsat sensor is suitable for real-time monitoring over relatively short timescales and for small reservoirs. Satellite data can allow for surveying of chlorophyll-a concentration in aquatic ecosystems, thus, providing invaluable data in data scarce (limited on site ground measurements) environments. 相似文献
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On the multivariate total least-squares approach to empirical coordinate transformations. Three algorithms 总被引：2，自引：1，他引：1
The multivariate total least-squares (MTLS) approach aims at estimating a matrix of parameters, Ξ, from a linear model (Y−E
Y
= (X−E
X
) · Ξ) that includes an observation matrix, Y, another observation matrix, X, and matrices of randomly distributed errors, E
Y
and E
X
. Two special cases of the MTLS approach include the standard multivariate least-squares approach where only the observation
matrix, Y, is perturbed by random errors and, on the other hand, the data least-squares approach where only the coefficient matrix
X is affected by random errors. In a previous contribution, the authors derived an iterative algorithm to solve the MTLS problem
by using the nonlinear Euler–Lagrange conditions. In this contribution, new lemmas are developed to analyze the iterative
algorithm, modify it, and compare it with a new ‘closed form’ solution that is based on the singular-value decomposition.
For an application, the total least-squares approach is used to estimate the affine transformation parameters that convert
cadastral data from the old to the new Israeli datum. Technical aspects of this approach, such as scaling the data and fixing
the columns in the coefficient matrix are investigated. This case study illuminates the issue of “symmetry” in the treatment
of two sets of coordinates for identical point fields, a topic that had already been emphasized by Teunissen (1989, Festschrift
to Torben Krarup, Geodetic Institute Bull no. 58, Copenhagen, Denmark, pp 335–342). The differences between the standard least-squares
and the TLS approach are analyzed in terms of the estimated variance component and a first-order approximation of the dispersion
matrix of the estimated parameters. 相似文献
19.
In this study, sensible heat (H) calculation using remote sensing data over an alpine grass landscape is conducted from May to September 2010, and the calculation is validated using LAS (large aperture scintillometers) measurements. Data from two remote sensing sensors (FY3A-VIRR and TERRA-MODIS) are analysed. Remote sensing data, combined with the ground meteorological observations (pressure, temperature, wind speed, humidity) are fed into the SEBS (Surface Energy Balance System) model. Then the VIRR-derived sensible heat (VIRR_SEBS_H) and MODIS-derived sensible heat (MODIS_SEBS_H) are compared with the LAS-estimated H, which are obtained at the respective satellite overpass time. Furthermore, the similarities and differences between the VIRR_SEBS_H and MODIS_SEBS_H values are investigated. The results indicate that VIRR data quality is as good as MODIS data for the purpose of H estimation. The root mean square errors (rmse) of the VIRR_SEBS_H and MODIS_SEBS_H values are 45.1098 W/m2 (n = 64) and 58.4654 W/m2 (n = 71), respectively. The monthly means of the MODIS_SEBS_H are marginally higher than those of VIRR_SEBS_H because the satellite overpass time of the TERRA satellite lags by 25 min to that of the FT3A satellite. Relative evaporation (EFr), which is more time-independent, shows a higher agreement between MODIS and VIRR. Many common features are shared by the VIRR_SEBS_H and the MODIS_SEBS_H, which can be attributed to the SEBS model performance. In May–June, H is over-estimated with more fluctuations and larger rmse, whereas in July–September, H is under-estimated with fewer fluctuations and smaller rmse. Sensitivity analysis shows that potential temperature gradient (delta_T) plays a dominant role in determining the magnitude and fluctuation of H. The largest rmse and over-estimation in H occur in June, which could most likely be attributed to high delta_T, high wind speed, and the complicated thermodynamic state during the transitional period when bare land transforms to dense vegetation cover. 相似文献
20.
Past laboratory and field studies have quantified phenolic substances in vegetative matter from reflectance measurements for understanding plant response to herbivores and insect predation. Past remote sensing studies on phenolics have evaluated crop quality and vegetation patterns caused by bedrock geology and associated variations in soil geochemistry. We examined spectra of pure phenolic compounds, common plant biochemical constituents, dry leaves, fresh leaves, and plant canopies for direct evidence of absorption features attributable to plant phenolics. Using spectral feature analysis with continuum removal, we observed that a narrow feature at 1.66 μm is persistent in spectra of manzanita, sumac, red maple, sugar maple, tea, and other species. This feature was consistent with absorption caused by aromatic CH bonds in the chemical structure of phenolic compounds and non-hydroxylated aromatics. Because of overlapping absorption by water, the feature was weaker in fresh leaf and canopy spectra compared to dry leaf measurements. Simple linear regressions of feature depth and feature area with polyphenol concentration in tea resulted in high correlations and low errors (% phenol by dry weight) at the dry leaf (r2 = 0.95, RMSE = 1.0%, n = 56), fresh leaf (r2 = 0.79, RMSE = 2.1%, n = 56), and canopy (r2 = 0.78, RMSE = 1.0%, n = 13) levels of measurement. Spectra of leaves, needles, and canopies of big sagebrush and evergreens exhibited a weak absorption feature centered near 1.63 μm, short ward of the phenolic compounds, possibly consistent with terpenes. This study demonstrates that subtle variation in vegetation spectra in the shortwave infrared can directly indicate biochemical constituents and be used to quantify them. Phenolics are of lesser abundance compared to the major plant constituents but, nonetheless, have important plant functions and ecological significance. Additional research is needed to advance our understanding of the spectral influences of plant phenolics and terpenes relative to dominant leaf biochemistry (water, chlorophyll, protein/nitrogen, cellulose, and lignin). 相似文献