An alternative approach to transverse and profile terrain curvature

Terrain curvature is one of the most important parameters of land surface topography. Well-established methods used in its measurement compute an index of plan or profile curvature for every single cell of a digital elevation model (DEM). The interpretation of these outputs may be delicate, especially when selected locations have to be analyzed. Furthermore, they involve a high level of simplification, contrasting with the complex and multiscalar nature of the surface curvature itself. In this paper, we present a new method to assess vertical transverse and profile curvature combining real-scale visualization and the possibility to measure these two terrain derivatives over a large range of scales. To this purpose, we implemented a GIS tool that extracts longitudinal and transverse elevation profiles from a high-resolution DEM. The performance of our approach was compared with some of the most commonly used methods (ArcMap, Redlands, CA, USA; ArcSIE, Landserf) by analyzing the terrain curvature around charcoal production sites in southern Switzerland. The different methods produced comparable results. While conventional methods quickly summarize terrain curvature in the form of a matrix of values, they involve a loss of information. The advantage of the new method lies in the possibility to measure and visualize the shape and size of the curvature, and to obtain a realistic representation of the average curvature for different subsets of spatial points. Moreover, the new method makes it possible to control the conditions in which the index of curvature is calculated.     

Multicollinearity and correlation among local regression coefficients in geographically weighted regression

Present methodological research on geographically weighted regression (GWR) focuses primarily on extensions of the basic GWR model, while ignoring well-established diagnostics tests commonly used in standard global regression analysis. This paper investigates multicollinearity issues surrounding the local GWR coefficients at a single location and the overall correlation between GWR coefficients associated with two different exogenous variables. Results indicate that the local regression coefficients are potentially collinear even if the underlying exogenous variables in the data generating process are uncorrelated. Based on these findings, applied GWR research should practice caution in substantively interpreting the spatial patterns of local GWR coefficients. An empirical disease-mapping example is used to motivate the GWR multicollinearity problem. Controlled experiments are performed to systematically explore coefficient dependency issues in GWR. These experiments specify global models that use eigenvectors from a spatial link matrix as exogenous variables.This study was supported by grant number 1 R1 CA95982-01, Geographic-Based Research in Cancer Control and Epidermiology, from the National Cancer Institute. The author thank the anonymous reviewers and the editor for their helpful comments.     

Local‐government data sharing: Evaluating the foundations of spatial data infrastructures

This paper presents a typology of local‐government data sharing arrangements in the US at a time when spatial data infrastructures (SDI) are moving into a second generation. In the first generation, the US National Spatial Data Infrastructure (NSDI) theoretically involved a pyramid of data integration resting on local‐government data sharing. Availability of local‐government data is the foundation for all SDI‐related data sharing in this model. However, first‐generation SDI data‐sharing activities and principles have gained only a tenuous hold in local governments. Some formalized data sharing occurs, but only infrequently in response to SDI programmes and policies. Previous research suggests that local‐government data sharing aligns with immediate organizational and practical concerns rather than state or national policies and programmes. We present research findings echoing extending these findings to show that local‐government data sharing is largely informal in nature and is undertaken to support existing governmental activities. NSDI principles remain simply irrelevant for the majority of surveyed local governments. The typology we present distinguishes four distinct types of local‐government data sharing arrangements that reflect institutional, political, and economic factors. The effectiveness of second generation, client‐service‐based SDI will be seriously constrained if the problems of local government take‐up fail to be addressed.     

A natural language processing and geospatial clustering framework for harvesting local place names from geotagged housing advertisements

Local place names are frequently used by residents living in a geographic region. Such place names may not be recorded in existing gazetteers, due to their vernacular nature, relative insignificance to a gazetteer covering a large area (e.g. the entire world), recent establishment (e.g. the name of a newly-opened shopping center) or other reasons. While not always recorded, local place names play important roles in many applications, from supporting public participation in urban planning to locating victims in disaster response. In this paper, we propose a computational framework for harvesting local place names from geotagged housing advertisements. We make use of those advertisements posted on local-oriented websites, such as Craigslist, where local place names are often mentioned. The proposed framework consists of two stages: natural language processing (NLP) and geospatial clustering. The NLP stage examines the textual content of housing advertisements and extracts place name candidates. The geospatial stage focuses on the coordinates associated with the extracted place name candidates and performs multiscale geospatial clustering to filter out the non-place names. We evaluate our framework by comparing its performance with those of six baselines. We also compare our result with four existing gazetteers to demonstrate the not-yet-recorded local place names discovered by our framework.     

Local Modeling模式及其在月径流预测中的应用

Local Modeling方法是一种动力系统预测方法，将其应用于河西内陆区黑河干流出山口莺落峡水文站月平均流量的中长期预测预报，取得了较为理想的成果。预测试验的结果表明，该预测模型有较高计算精度，尤其适用于非主汛期各月的月平均流量的预测；对于主汛期6～9月的月平均流量的预测，在考虑前期来水与预见期内降水的影响后，亦可获到较为理想的预测结果。可以认为，该方法的预报精度达到了水文情报预报规范的要求，Local Modeling方法的应用，将为西北干旱地区河川径流的中长期预报提供了一个新的途径。     

HST UV measurements of wind structure and velocities in Local Group OB stars

Archival HST FOS and GHRS data sets have been used to collect ultraviolet evidence for large- and small-scale stellar wind structure in extragalactic Local Group OB stars (i.e. SMC, LMC including R136, M31, M33 and NGC 6822). By comparison with previous studies of Galactic OB stars, wind activity is principally diagnosed in individual spectrograms via the presence of 'narrow absorption components' and saturated 'black' absorption troughs in the resonance line doublets. Their characteristics broadly suggest that these stars share the same physical mechanisms for perturbing the winds as those that act in Galactic stars. Both of these spectral indicators are also used to provide reliable measures of wind terminal velocities. These velocities are directly compared with previously published Galactic values, without reliance on model profile fitting. Relative to Galactic OB stars, the most discrepant terminal velocities (and wind line profiles) result from main-sequence early O-type stars in the SMC.     

浅谈涉密测绘成果管理

随着全国二次土地调查工作的全面开展,基础测绘工作出现了飞跃式的发展,这就对基础测绘成果的管理和应用提出了更高、更新的要求。结合多年基层基础测绘成果管理经验,对涉密测绘成果的管理及使用上提出了一些建议。     

A data-driven approach to local gravity field modelling using spherical radial basis functions

We propose a methodology for local gravity field modelling from gravity data using spherical radial basis functions. The methodology comprises two steps: in step 1, gravity data (gravity anomalies and/or gravity disturbances) are used to estimate the disturbing potential using least-squares techniques. The latter is represented as a linear combination of spherical radial basis functions (SRBFs). A data-adaptive strategy is used to select the optimal number, location, and depths of the SRBFs using generalized cross validation. Variance component estimation is used to determine the optimal regularization parameter and to properly weight the different data sets. In the second step, the gravimetric height anomalies are combined with observed differences between global positioning system (GPS) ellipsoidal heights and normal heights. The data combination is written as the solution of a Cauchy boundary-value problem for the Laplace equation. This allows removal of the non-uniqueness of the problem of local gravity field modelling from terrestrial gravity data. At the same time, existing systematic distortions in the gravimetric and geometric height anomalies are also absorbed into the combination. The approach is used to compute a height reference surface for the Netherlands. The solution is compared with NLGEO2004, the official Dutch height reference surface, which has been computed using the same data but a Stokes-based approach with kernel modification and a geometric six-parameter “corrector surface” to fit the gravimetric solution to the GPS-levelling points. A direct comparison of both height reference surfaces shows an RMS difference of 0.6 cm; the maximum difference is 2.1 cm. A test at independent GPS-levelling control points, confirms that our solution is in no way inferior to NLGEO2004.     

Nonlinear regional economic dynamics: continuous-time specification,estimation and stability analysis

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.