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1.
The neighborhood effects of foreclosure 总被引：1，自引：0，他引：1
Neighborhood quality is an important attribute of housing yet its value is rarely known to researchers. We argue that changes
in nearby foreclosures reveal changes in neighborhood quality. Thus estimates of the hedonic price of nearby foreclosures
provide a glimpse of values that people hold for local neighborhood quality. The empirical models include controls for both
spatial dependence in housing prices and in the errors. The estimates indicate that nearby foreclosures produce externalities
that are capitalized into home prices—an additional foreclosure within 250 feet of a sale negatively impacts selling price
by approximately $1,666, ceteris paribus. 相似文献
2.
Incorporating spatial variation in housing attribute prices: a comparison of geographically weighted regression and the spatial expansion method 总被引：10，自引：3，他引：7
Christopher Bitter Gordon F. Mulligan Sandy Dall’erba 《Journal of Geographical Systems》2007,9(1):7-27
Hedonic house price models typically impose a constant price structure on housing characteristics throughout an entire market
area. However, there is increasing evidence that the marginal prices of many important attributes vary over space, especially
within large markets. In this paper, we compare two approaches to examine spatial heterogeneity in housing attribute prices
within the Tucson, Arizona housing market: the spatial expansion method and geographically weighted regression (GWR). Our
results provide strong evidence that the marginal price of key housing characteristics varies over space. GWR outperforms
the spatial expansion method in terms of explanatory power and predictive accuracy.
相似文献
| Christopher BitterEmail: |
3.
Multitemporal land cover classification over urban areas is challenging, especially when using heterogeneous data sources with variable quality attributes. A prominent challenge is that classes with similar spectral signatures (such as trees and grass) tend to be confused with one another. In this paper, we evaluate the efficacy of image point cloud (IPC) data combined with suitable Bayesian analysis based time-series rectification techniques to improve the classification accuracy in a multitemporal context. The proposed method uses hidden Markov models (HMMs) to rectify land covers that are initially classified by a random forest (RF) algorithm. This land cover classification method is tested using time series of remote sensing data from a heterogeneous and rapidly changing urban landscape (Kuopio city, Finland) observed from 2006 to 2014. The data consisted of aerial images (5 years), Landsat data (all 9 years) and airborne laser scanning data (1 year). The results of the study demonstrate that the addition of three-dimensional image point cloud data derived from aerial stereo images as predictor variables improved overall classification accuracy, around three percentage points. Additionally, HMM-based post processing reduces significantly the number of spurious year-to-year changes. Using a set of 240 validation points, we estimated that this step improved overall classification accuracy by around 3.0 percentage points, and up to 6 to 10 percentage points for some classes. The overall accuracy of the final product was 91% (kappa = 0.88). Our analysis shows that around 1.9% of the area around Kuopio city, representing a total area of approximately 0.61 km2, experienced changes in land cover over the nine years considered. 相似文献
4.
A rule-based model for managing uncertainty in spatial databases is presented. The overall goal of the model is to allow
a user to assign to a single map class each polygon whose class is not entirely certain using more information than only the
map class attributes of such polygons (that are herein termed abjects). This situation might arise when multiple map realizations of an area are available and interpreters/cartographers are not
in agreement as to what class is present at a given location or when a digital image is classified by algorithmic/probabilistic
means. The scale-based model developed relies on attribute, geometric, and neighborhood measures of abjects arranged in a hierarchical rule-based structure. Structural knowledge of these measures leads to the procedural knowledge
that determines what action – e.g., merge, reclassify, retain – is to be taken for a given abject. The wider applicability of the model and associated methodology is also discussed.
Received: 5 July 2001 / Accepted: 11 April 2002 相似文献
5.
《制图学和地理信息科学》2013,40(2):99-110
This paper examines the performance of artificial neural networks (ANNs) as a method of spatial interpolation, when presented with irregular and regular samples of elevation data. The results of the ANN interpolation are compared with results obtained by kriging. Tests of spatial bias in the systematic errors contained in each of the neural network-derived DEMs were conducted using four attributes: slope, aspect, average direction and average distance from the nearest sampled value. Based on RMS and other evaluation measures, the accuracy of estimated DEMs from regular and irregular sample distributions using neural networks is lower than the accuracy level derived from kriging. The accuracy level of the ANN interpolators also decreases as the range of elevation values in DEMs increases. As reported in the literature, ANNs are approximate interpolators, and the pattern of under-prediction and over-prediction of elevation values in this study revealed that all estimated values fell within the range of sample elevations. Neural networks cannot predict values outside the range of elevation values contained in the sample, a property shared by other interpolators such as inverse weighted distance. 相似文献
6.
AbstractWith the rapid development of 3D Digital City, the focus of research has shifted from 3D city modeling and geo-database construction to 3D geo-database service and maintenance. The frequent modifications on geometry, texture, attribute, and topology present a great challenge to the 3D geo-database updating. This article proposes an event-driven spatiotemporal database model (ESDM) that combines the historical and present 3D city models with the semantic classification and state expression, triggered by changing events predefined. In addition, a corresponding dynamic updating method based on adaptive matching algorithm is presented to perform the dynamic updating operation for the complex 3D city models automatically, according to the compound matching of semantics, attributes, and spatial locations. Finally, the validity and feasibility of the proposed ESDM and its updating method are demonstrated through a 3D geo-database with more than 1.5 million 3D city models. 相似文献
7.
AbstractThe paper discusses the need of a high-level query language to allow analysts, geographers and, in general, non-programmers to easily cross-analyze multi-source VGI created by means of apps, crowd-sourced data from social networks and authoritative geo-referenced data, usually represented as JSON data sets (nowadays, the de facto standard for data exported by social networks). Since an easy to use high-level language for querying and manipulating collections of possibly geo-tagged JSON objects is still unavailable, we propose a truly declarative language, named J-CO-QL, that is based on a well-defined execution model. A plug-in for a GIS permits to visualize geo-tagged data sets stored in a NoSQL database such as MongoDB; furthermore, the same plug-in can be used to write and execute J-CO-QL queries on those databases. The paper introduces the language by exemplifying its operators within a real study case, the aim of which is to understand the mobility of people in the neighborhood of Bergamo city. Cross-analysis of data about transportation networks and VGI from travelers is performed, by means of J-CO-QL language, capable to manipulate and transform, combine and join possibly geo-tagged JSON objects, in order to produce new possibly geo-tagged JSON objects satisfying users’ needs. 相似文献
8.
精确估算森林生物量对全球碳平衡以及气候变化的研究有重要意义。以亚热带天然次生林为研究对象,借助地面实测样地数据,通过对机载LiCHy(LiDAR,CCD and Hyperspectral)传感器同时获取的高光谱和高空间分辨率数据进行信息提取和数据融合,建模反演森林生物量。首先通过面向对象分割方法进行单木冠幅提取,然后融合从高光谱数据提取的光谱特征变量和从高空间分辨率数据提取的单木冠幅统计变量,构建多元回归模型估算地上、地下生物量,最后利用地面实测生物量经交叉验证评价模型精度。结果表明,综合模型的精度(R~2为0.54—0.62)高于高光谱模型(R~2为0.48—0.57);在高光谱模型中地上生物量模型精度(R~2为0.57)高于地下生物量模型(R~2为0.48);在综合模型中地上生物量模型精度(R~2为0.62)同样高于地下生物量模型(R~2为0.54)。交叉验证结果表明,与仅使用高光谱数据(单一数据源)相比,通过集成高光谱和高空间分辨率数据的生物量反演效果有所提升,可以更加有效地估算亚热带森林生物量。 相似文献
9.
Riad Balaghi Bernard Tychon Herman Eerens Mohammed Jlibene 《International Journal of Applied Earth Observation and Geoinformation》2008,10(4):438
In Morocco, no operational system actually exists for the early prediction of the grain yields of wheat (Triticum aestivum L.). This study proposes empirical ordinary least squares regression models to forecast the yields at provincial and national levels. The predictions were based on dekadal (10-daily) NDVI/AVHRR, dekadal rainfall sums and average monthly air temperatures. The Global Land Cover raster map (GLC2000) was used to select only the NDVI pixels that are related to agricultural land. Provincial wheat yields were assessed with errors varying from 80 to 762 kg ha−1, depending on the province. At national level, wheat yield was predicted at the third dekad of April with 73 kg ha−1 error, using NDVI and rainfall. However, earlier forecasts are possible, starting from the second dekad of March with 84 kg ha−1 error, at least 1 month before harvest. At the provincial and national levels, most of the yield variation was accounted for by NDVI. The proposed models can be used in an operational context to early forecast wheat yields in Morocco. 相似文献
10.
Real-time orbit determination and interplanetary navigation require accurate predictions of the orientation of the Earth in
the celestial reference frame and in particular that for Universal Time UT1. Much of the UT1 variations over periods ranging
from hours to a couple of years are due to the global atmospheric circulation. Therefore, the axial atmospheric angular momentum
(AAM) forecast series may be used as a proxy index to predict UT1. Our approach taking advantage of this fact is based on
an adaptive procedure. It involves incorporating integrations of AAM estimates into UT1 series. The procedure runs on a routine
basis using AAM forecasts that are based on the two meteorological series, from the US National Centers for Environmental
Prediction and the Japan Meteorological Agency. It is pertinent to test the prediction method for the period that includes
the special CONT08 campaign over which we expect a significant improvement in UT1 accuracy. The studies we carried out were
aimed both to compare atmospheric forecasts and analyses, as well as to compare the skills of the UT1 forecasts based on the
method with atmospheric forecasts and that using current statistical processes, as applied to the C04 Earth orientation parameters
series derived by the International Earth rotation and Reference Systems service (IERS). Here we neglect the oceanic sub-diurnal
and diurnal variations, as these signals are expected to be smaller than the UT1-equivalent of 100 μs, when averaged over a few days. The prediction performances for a 2-day forecast are similar, but at a forecast horizon of
a week, the AAM-based forecast is roughly twice as skillful as the statistically based one. 相似文献
11.
《International Journal of Digital Earth》2013,6(1):88-106
Abstract Digital Earth essentially consists of 3D and moreD models and attached semantic information (attributes). Techniques for generating such models efficiently are required very urgently. Reality-based 3D modelling using images as prime data source plays an important role in this context. Images contain a wealth of information that can be advantageously used for model generation. Images are increasingly available from satellite, aerial and terrestrial platforms. This contribution briefly describes some of the problems which we encounter if the process of model generation is to be automatised. With the help of some examples from Digital Terrain Model generation, Cultural Heritage and 3D city modelling we show briefly what can be achieved. Special attention is directed towards the use of model helicopters for image data acquisition. Some problems with interactive visualisation are discussed. Also, issues surrounding R&D, professional practice and education are also addressed. 相似文献
12.
Stochastic estimation of tropospheric path delays in global positioning system geodetic measurements 总被引：5，自引：2，他引：5
Water vapor radiometric (WVR) and surface meteorological (SM) measurements taken during three Global Positioning System (GPS) geodetic experiments are used to calculate process noise levels for random walk and first-order Gauss-Markov temporal models
of tropospheric path delays. Entire wet and combined wet and dry zenith delays at each network site then are estimated simultaneously
with the geodetic parameters without prior calibration. The path delays and corresponding baseline estimates are compared
to those obtained with calibrated data and stochastic residual delays. In this manner, the marginal utility of a priori tropospheric
calibration is assessed given the ability to estimate the path delays directly using only theGPS data. Estimation of total zenith path delays with appropriate random walk or Gauss-Markov models yields baseline repeatabilities
of a few parts in 108. This level of geodetic precision, and accuracy as suggested by analyses on collocated baselines estimated independently
by very long baseline interferometry, is comparable to or better than that obtained after path delay calibration usingWVR and/orSM measurements. Results suggest thatGPS data alone have sufficient strength to resolve centimeter-level zenith path delay fluctuations over periods of a few minutes. 相似文献
13.
Hyun Jung Cho Igor Ogashawara Deepak Mishra Joseph White Andrew Kamerosky Lori Morris 《地理信息系统科学与遥感》2014,51(2):120-138
Differentiation between benthic habitats, particularly seagrass and macroalgae, using satellite data is complicated because of water column effects plus the presence of chlorophyll-a in both seagrass and algae that result in similar spectral patterns. Hyperspectral imager for the coastal ocean data over the Indian River Lagoon, Florida, USA, was used to develop two benthic classification models, SlopeRED and SlopeNIR. Their performance was compared with iterative self-organizing data analysis technique and spectral angle mapping classification methods. The slope models provided greater overall accuracies (63–64%) and were able to distinguish between seagrass and macroalgae substrates more accurately compared to the results obtained using the other classifications methods. 相似文献
14.
Estimates of solar radiation distribution in urban areas are often limited by the complexity of urban environments. These limitations arise from spatial structures such as buildings and trees that affect spatial and temporal distributions of solar fluxes over urban surfaces. The traditional solar radiation models implemented in GIS can address this problem only partially. They can be adequately used only for 2‐D surfaces such as terrain and rooftops. However, vertical surfaces, such as facades, require a 3‐D approach. This study presents a new 3‐D solar radiation model for urban areas represented by 3‐D city models. The v.sun module implemented in GRASS GIS is based on the existing solar radiation methodology used in the topographic r.sun model with a new capability to process 3‐D vector data representing complex urban environments. The calculation procedure is based on the combined vector‐voxel approach segmenting the 3‐D vector objects to smaller polygon elements according to a voxel data structure of the volume region. The shadowing effects of surrounding objects are considered using a unique shadowing algorithm. The proposed model has been applied to the sample urban area with results showing strong spatial and temporal variations of solar radiation flows over complex urban surfaces. 相似文献
15.
基于表层卫星遥感观测的中深层海洋遥感对于了解海洋内部异常及其动力过程有重要意义。如何从现有的海洋表层遥感观测资料提取海洋内部关键动力环境信息场是具有挑战性的海洋遥感技术前沿。本文采用支持向量回归(SVR)方法,通过卫星遥感观测获取的多源海表参量(海表高度异常(SSHA)、海表温度异常(SSTA)、海表盐度异常(SSSA)和海表风场异常(SSWA)),选择最优参量输入组合,感知海洋次表层温度异常(STA),并用实测Argo数据作精度验证。结果表明SVR模型可准确估算全球尺度的STA(1000 m深度以浅);当SVR输入变量为2个(SSHA、SSTA)、3个(SSHA、SSTA、SSSA)、4个(SSHA、SSTA、SSSA、SSWA)时对应的平均均方差(MSE)分别为0.0090、0.0086、0.0087,平均决定系数(R2)分别为0.443、0.457、0.485。因此,除了SSHA和SSTA外,SSSA与SSWA的输入对SVR模型的估算有积极影响,有助于提高STA的估算精度。在全球增暖与减缓背景下,该研究可为从表层卫星遥感观测提取海洋内部热力异常信息研究提供重要技术支持,有利于拓展卫星对海观测范围。 相似文献
16.
Additional results are presented concerning a study that consider improvements over present Earth Rotation Parameter (ERP) determination methods by directly combining observations from various space geodetic systems in one adjustment. Earlier
results are extended, showing that in addition to slight improvements in accuracy substantial (a factor of three or more)
improvements in precision and significant reductions in correlations between various parameters can be obtained (by combining
Lunar Laser Ranging (LLR), Satellite Laser Ranging (SLR) to Lageos, and Very Long Baseline Interferometry (VLBI) data in one adjustment) as compared to results from individual systems. Smaller improvements are also seen over the weighted
means of the individual system results. Although data transmission would not be significantly reduced, negligible additional
computer time would be required if (standardized) normal equations were available from individual solutions. Suggestions for
future work and implications for the new International Earth Rotation Service (IERS) are also presented. 相似文献
17.
面向对象方法的时间序列MODIS数据湿地信息提取——以洞庭湖流域为例 总被引：2，自引：0，他引：2
以洞庭湖流域为研究区,对大范围湿地信息遥感提取方法进行了研究。先基于时间序列MODIS EVI及物候特征参数,通过J-M(Jeffries-Matusita distance)距离分析,构建了MODIS(250 m)最佳时序组合分类数据;其次,通过Johnson指数确定了最佳分割尺度,采用面向对象的遥感分类方法(Random tree分类器)提取了洞庭湖流域的湿地信息,并验证该方法的适用性。研究结果表明,基于时序数据与面向对象的Random tree分类的总体精度和Kappa系数分别为78.84%和0.71,较之基于像元的相同算法的总体分类精度和Kappa系数分别提高了5.79%和0.04。同时,基于面向对象方法的湿地整体的用户精度与生产者精度较基于像元方法分别提高了4.56%和6.21%,可有效提高大区域湿地信息提取的精度。 相似文献
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《制图学和地理信息科学》2013,40(2):89-101
Investigators in many fields are analyzing temporal change in spatial data. Such analyses are typically conducted by comparing the value of some metric (e. g., area, contagion, or diversity indices) measured at time T1 with the value of the same metric measured at time T2 . These comparisons typically include the use of simple interpolation models to estimate the value of the metric of interest at points in time between observations, followed by applications of differential calculus to investigate the rates at which the metric is changing. Unfortunately, these techniques treat the values of the metrics being analyzed as if they were observed values, when in fact the metrics are derived from more fundamental spatial data. The consequence of treating metrics as observed values is a significant reduction in the degrees of freedom in spatial change over time. This results in an oversimplified view of spatio-temporal change. A more accurate view can be produced by (1) applying temporal interpolation models to observed spatial data rather than derived spatial metrics; (2) expanding the metric of interest's computational equation by replacing the terms relating to the observed spatial data with their temporal interpolation equations; and (3) differentiating the expanded computational equation. This alternative, three-step spatio-temporal analysis technique will be described and justified. The alternative technique will be compared to the conventional approach using common metrics and a sample data set. 相似文献