Non-life insurance consumption in Italy: a sub-regional panel data analysis

We analyze the consumption of non-life insurance across 103 Italian provinces in 1998–2002 in order to assess its determinants, in the light of the empirical literature. Using sub-regional data, we overcome an important limitation of cross-country analyses, i.e. the systemic heterogeneity due to country-specific characteristics. Individual heterogeneity is accounted for through panel data techniques. However, considering spatial units within a single market raises issues of cross-sectional or spatial dependence, either due to common nationwide and/or regional factors or to spatial proximity. We carefully assess spatial dependence, employing recent diagnostic tests, finding out that the regressors included in our specification successfully account for spatial dependence. Insurance turns out to depend on income, wealth and some demographics, as already established, but also on trust, judicial efficiency and borrowing conditions. These findings help in explaining the gap between Central-Northern Italy and the south of the country.     

A spatio-temporal econometric model of regional growth in Spain

In this paper, a combined cross-section and time-series econometric analysis of Spanish regional growth is presented. This analysis operates with a database where the number of cross-sectional units is small for a typical panel of data, while the time dimension is clearly dominant. First, using recent techniques in the econometric analysis of panel data (both panel unit root and panel co-integration tests), a co-integrating relationship between the level of regional output and the level of regional input factors is found, and the steady-state equilibrium production function is estimated. Second, a dynamic spatio-temporal panel error correction model is used in order to describe the short-run regional growth adjustment process in space and time. As conclusion, it is possible to identify significant spatial effects in the Spanish regional growth after controlling for temporal variation of the implied variables.     

Understanding the Combined Impacts of Aggregation and Spatial Non‐Stationarity: The Case of Migration‐Environment Associations in Rural South Africa

Socio‐demographic data are typically collected at various levels of aggregation, leading to the modifiable areal unit problem. Spatial non‐stationarity of statistical associations between variables further influences the demographic analyses. This study investigates the implications of these two phenomena within the context of migration‐environment associations. Global and local statistical models are fit across increasing levels of aggregation using household level survey data from rural South Africa. We raise the issue of operational scale sensitivity, which describes how the explanatory power of certain variables depends on the aggregation level. We find that as units of analysis (households) are aggregated, some variables become non‐significant in the global models, while others are less sensitive to aggregation. Local model results show that aggregation reduces spatial variation in migration‐related local associations but also affects variables differently. Spatial non‐stationarity appears to be the driving force behind this phenomenon as the results from the global model mask this relationship. Operational scale sensitivity appears related to the underlying spatial autocorrelation of the non‐aggregated variables but also to the way a variable is constructed. Understanding operational scale sensitivity can help to refine the process of selecting variables related to the scale of analysis and better understand the effects of spatial non‐stationarity on statistical relationships.     

净初级生产力遥感估算模型空间尺度转换

采用基于混合像元的结构分析方法和支持向量机(SVM)算法,建立了高分辨率遥感数据(TM)向低分辨率遥感数据(MODIS)的尺度转换模型,实现了由高分辨率遥感数据获得的NPP向低分辨率遥感数据获得的NPP的空间尺度转换。对低分辨率遥感数据(MODIS)估算的NPP结果进行了尺度效应校正。结果表明:SVM回归模型模拟出的尺度效应校正因子Rj_corrected与1-F中覆盖度草地之间的相关性较高,R2达到0.81。尺度效应校正前的NPPMODIS与NPPTM的相关性较低,R2仅为0.69,RMSE为3.47;尺度效应校正后的NPPMODIS_corrected与NPPTM的相关性较高,R2达到0.84,RMSE为1.87。因此,经过尺度效应校正后的NPP无论是在相关性还是在误差方面有了很大程度的提高。     

Accounting for Spatial Uncertainty in Optimization with Spatial Decision Support Systems

Spatial decision support systems (SDSS) are designed to make complex resource allocation problems more transparent and to support the design and evaluation of allocation plans. Recent developments in this field focus on the design of allocation plans using optimization techniques. In this paper we analyze how uncertainty in spatial (input) data propagates through, and affects the results of, an optimization model. The optimization model calculates the optimal location for a ski run based on a slope map, which is derived from a digital elevation model (DEM). The uncertainty propagation is a generic method following a Monte Carlo approach, whereby realizations of the spatially correlated DEM error are generated using 'sequential Gaussian simulation'. We successfully applied the methodology to a case study in the Austrian Alps, showing the influence of spatial uncertainty on the optimal location of a ski run and the associated development costs. We also discuss the feasibility of routine incorporation of uncertainty propagation methodologies in an SDSS.     

SPATIAL OBJECTS AND SPATIAL RELATIONSHIPS

Spatial object and spatial relationship are two basic concepts of GIS.Spatial object is the digital representation of geographical entity or phenomenon,which forms the basis for data management and analysis;spatial relationship is theconnexion between spatial objects when geometric properties are considered.Thecommonly used classification of spatial objects as points,lines and areas is mathe-matically strict,and suitable for data management,but a bit too generalized forrepresenting real entities and extracting spatial relationships.A good classificationmodel should not only be good for representing real entities,but also good for re-vealing spatial relationships,therefore good for formalizing spatial analyses.     

Impacts of Positional Error on Spatial Regression Analysis: A Case Study of Address Locations in Syracuse, New York

Positional error is the error produced by the discrepancy between reference and recorded locations. In urban landscapes, locations typically are obtained from global positioning systems or geocoding software. Although these technologies have improved the locational accuracy of georeferenced data, they are not error free. This error affects results of any spatial statistical analysis performed with a georeferenced dataset. In this paper we discuss the properties of positional error in an address matching exercise and the allocation of point locations to census geography units. We focus on the error's spatial structure, and more particularly on impacts of error propagation in spatial regression analysis. For this purpose we use two geocoding sources, we briefly describe the magnitude and the nature of their discrepancies, and we evaluate the consequences that this type of locational error has on a spatial regression analysis of pediatric blood lead data for Syracuse, NY. Our findings include: (1) the confirmation of the recurrence of spatial clustering in positional error at various geographic resolutions; and, (2) the identification of a noticeable but not shockingly large impact from positional error propagation in spatial auto‐binomial regression analysis results for the dataset analyzed.     

Improved GRACE science results after adjustment of geometric biases in the Level-1B K-band ranging data

The GRACE (Gravity Recovery and Climate Experiment) satellite mission relies on the inter-satellite K-band microwave ranging (KBR) observations. We investigate systematic errors that are present in the Level-1B KBR data, namely in the geometric correction. This correction converts the original ranging observation (between the two KBR antennas phase centers) into an observation between the two satellites’ centers of mass. It is computed from data on the precise alignment between both satellites, that is, between the lines joining the center of mass and the antenna phase center of either satellite. The Level-1B data used to determine this alignment exhibit constant biases as large as 1–2 mrad in terms of pitch and yaw alignment angles. These biases induce non-constant errors in the Level-1B geometric correction. While the precise origin of the biases remains to be identified, we are able to estimate and reduce them in a re-calibration approach. This significantly improves time-variable gravity field solutions based on the CNES/GRGS processing strategy. Empirical assessments indicate that the systematic KBR data errors have previously induced gravity field errors on the level of 6–11 times the so-called GRACE baseline error level. The zonal coefficients (from degree 14) are particularly affected. The re-calibration reduces their rms errors by about 50%. As examples for geophysical inferences, the improvement enhances agreement between mass variations observed by GRACE and in-situ ocean bottom pressure observations. The improvement also importantly affects estimates of inter-annual mass variations of the Antarctic ice sheet.     

Modeling spatial dependence in high spatial resolution hyperspectral data sets

 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     

The corridor correction concept

Atmospheric delays are contributors to the GNSS error budget in precise GNSS positioning that can reduce positioning accuracy considerably if not compensated appropriately. Both ionospheric and tropospheric delay corrections can be determined with help of reference stations in active GNSS networks. One approach to interpolate these error terms to the user’s location that is employed in Germany’s SAPOS network is the determination of area correction parameters (ACP, German: “Fl?chenkorrekturparameter—FKP”). A 2D interpolation scheme using data from at least 3 reference stations surrounding the rover is employed. A modification of this method was developed which only makes use of as few as 2 reference stations and provides 1D linear correction parameters along a “corridor” in which the user’s rover is moving. We present the results of a feasibility study portraying results from use of corridor correction parameters for precise RTK-like positioning. The differences to the reference coordinates (3D) attained in average for 1 h of data employing selected network nodes in Germany are between 0.8 and 2.0 cm, which compares well with the traditional area correction method that yields an error of 0.7 up to 1.1 cm.     

地理信息科学中的尺度分析

尺度是对地理现象观察、度量的标准之一,是空间数据采集、建模、分析的重要依据,是空间数据的重要特征。在探讨空间尺度与人类认知空间的关系基础上,分析地理信息科学(G ISc)中空间尺度在不同应用对象和不同应用环境中的内涵:针对地图表达的比例尺;地理现象的内在尺度和分析建模时的分析尺度。并以地图表达为例重点论述尺度变化对空间模式的影响。     

基于生产过程的空间数据误差分类与质量控制

以空间数据生产过程为出发点,研究空间数据质量问题的产生原因,提出基于生产过程的空间数据误差分类,包括:数据源误差、数据采集误差和系统处理误差,并认为数据采集误差是影响空间数据质量的主要因素,而空间数据可视化是对数据误差最为有效的检查手段,通过在生产过程中实现地图符号化、隐性信息可视化、拓朴检查和地图接边等检查方法,提高空间数据质量.     

Positional Accuracy of Spatial Data: Non-Normal Distributions and a Critique of the National Standard for Spatial Data Accuracy

Spatial data quality is a paramount concern in all GIS applications. Existing spatial data accuracy standards, including the National Standard for Spatial Data Accuracy (NSSDA) used in the United States, commonly assume the positional error of spatial data is normally distributed. This research has characterized the distribution of the positional error in four types of spatial data: GPS locations, street geocoding, TIGER roads, and LIDAR elevation data. The positional error in GPS locations can be approximated with a Rayleigh distribution, the positional error in street geocoding and TIGER roads can be approximated with a log‐normal distribution, and the positional error in LIDAR elevation data can be approximated with a normal distribution of the original vertical error values after removal of a small number of outliers. For all four data types considered, however, these solutions are only approximations, and some evidence of non‐stationary behavior resulting in lack of normality was observed in all four datasets. Monte‐Carlo simulation of the robustness of accuracy statistics revealed that the conventional 100% Root Mean Square Error (RMSE) statistic is not reliable for non‐normal distributions. Some degree of data trimming is recommended through the use of 90% and 95% RMSE statistics. Percentiles, however, are not very robust as single positional accuracy statistics. The non‐normal distribution of positional errors in spatial data has implications for spatial data accuracy standards and error propagation modeling. Specific recommendations are formulated for revisions of the NSSDA.     

Accounting for Error When Modelling with Time Series Data: Estimating the Development of Crop Pests Throughout the Year

In order to attach some statement of reliability to mesoscale maps of how pest risk may develop over time, methods were developed to enable the detection and evaluation of errors in predictions that arise from the use of input data series from remote point sources. Firstly, we investigated how predicted model results may differ as a result of the ordering of the spatial interpolation and the model procedures. Principles of logic were used to detect errors occurring in the daily sequences of predicted pest development. Analyses of spatial autocorrelation within the gridded results showed that areas where a pest was predicted to reach a certain stage of development become more fragmented as a model run progressed over time. We identified that the less intensive approach of running a model only at data points and subsequently interpolating these to a grid can, in some cases, result in errors of logic and unrealistic degrees of autocorrelation. These errors occurred particularly when mapping a non-indigenous, marginal, pest at the later stages of its development. As a strategy for error evaluation, deterministic process models were run using point-based estimates of interpolated daily temperature to give RMS data errors at the sample points. This enabled us to investigate how the component of error related to sparsely distributed point data contributed to errors in the gridded estimates of pest development over time. The error detection and evaluation methods outlined are tractable and applicable to a wide variety of cases where point based models running over multiple time steps are extended to provide spatially continuous, landscape-wide, mappable results.     

空间信息多级网格索引技术研究

空间信息多极网格(SIMG)是一种适合网格计算环境下空间信息表示的新方法,介绍了SIMG的核心思想及其空间数据组织原理,基于现有空间索引技术和SIMG相对量表达特点,提出了一种SIMG-R树空间索引技术,可实现SIMG细部地物的快速查询。     

On the adjustment of combined GPS/levelling/geoid networks

A detailed treatment of adjustment problems in combined global positioning system (GPS)/levelling/geoid networks is given. The two main types of `unknowns' in this kind of multi-data 1D networks are usually the gravimetric geoid accuracy and a 2D spatial field that describes all the datum/systematic distortions among the available height data sets. An accurate knowledge of the latter becomes especially important when we consider employing GPS techniques for levelling purposes with respect to a local vertical datum. Two modelling alternatives for the correction field are presented, namely a pure deterministic parametric model, and a hybrid deterministic and stochastic model. The concept of variance component estimation is also proposed as an important statistical tool for assessing the actual gravimetric geoid noise level and/or testing a priori determined geoid error models. Finally, conclusions are drawn and recommendations for further study are suggested. Received: 9 September 1998 / Accepted: 8 June 1999     

土壤重金属的空间插值方法比较分析——以阳谷县为例

空间插值通过采集少量的数据点,利用其中的空间关联,推求该区域内其他位置的属性值。本文以山东省阳谷县土壤重金属Cu采样数据为例进行空间探索性分析,分别采用了反距离权重插值和普通克里金插值两种方法进行空间分布插值模拟。结果表明,针对研究区采样数据,反距离权重插值方法生成的模型平均误差为1.97 mg/kg,总体精度为92%;普通克里金插值模型的平均误差为1.91 mg/kg,总体精度为92.35%,普通克里金插值方法更优。     

Effect of the SRTM global DEM on the determination of a high-resolution geoid model: a case study in Iran

Any errors in digital elevation models (DEMs) will introduce errors directly in gravity anomalies and geoid models when used in interpolating Bouguer gravity anomalies. Errors are also propagated into the geoid model by the topographic and downward continuation (DWC) corrections in the application of Stokes’s formula. The effects of these errors are assessed by the evaluation of the absolute accuracy of nine independent DEMs for the Iran region. It is shown that the improvement in using the high-resolution Shuttle Radar Topography Mission (SRTM) data versus previously available DEMs in gridding of gravity anomalies, terrain corrections and DWC effects for the geoid model are significant. Based on the Iranian GPS/levelling network data, we estimate the absolute vertical accuracy of the SRTM in Iran to be 6.5 m, which is much better than the estimated global accuracy of the SRTM (say 16 m). Hence, this DEM has a comparable accuracy to a current photogrammetric high-resolution DEM of Iran under development. We also found very large differences between the GLOBE and SRTM models on the range of −750 to 550 m. This difference causes an error in the range of −160 to 140 mGal in interpolating surface gravity anomalies and −60 to 60 mGal in simple Bouguer anomaly correction terms. In the view of geoid heights, we found large differences between the use of GLOBE and SRTM DEMs, in the range of −1.1 to 1 m for the study area. The terrain correction of the geoid model at selected GPS/levelling points only differs by 3 cm for these two DEMs.     

Interpolating Spatial Interaction Data1

Spatial interpolation has been widely used to improve the spatial granularity of data, or to mediate between inconsistent zoning schemes of spatial data. Traditional areal interpolation methods translate values of source zones to those of target zones. These methods have difficulty in dealing with flow data, as each instance is associated with a pair of zones. This study develops a new concept, flow line interpolation, to fill the abovementioned gap. We also develop a first flow line interpolation method to estimate commuting flow data between spatial units in a target zoning scheme based on such data in a source zoning scheme. Three models (i.e., areal‐weighted, intelligent, and gravity‐type flow line interpolation) are presented. To test the estimation accuracy and the application potential of these models, a case study of Fulton County in Georgia is conducted. The results reveal that both the areal‐weighted and intelligent models are very promising flow line interpolation methods.     

Alonso's Theory of Movements: Developments in Spatial Interaction Modeling

 The Spatial Interaction Model proposed by Alonso as “Theory of Movements” offers an innovative specification of spatial origin-destination flow models. Equations for flows between regions, total outflow from and total inflow to a region are linked by balancing factors. This paper presents a consistent formulation of Spatial Interaction Models in the Wilson tradition and Alonso's Theory of Movements. The paper is intended as an introduction to the model and a review of␣the state of the art. Besides it is argued that simultaneous equation techniques are required to estimate the so-called systemic parameters. Received: 21 May 2000 / Accepted: 18 January 2001