Statistical diagnosis and gross error test for semiparametric linear model

This paper systematically studies the statistical diagnosis and hypothesis testing for the semiparametric linear regression model according to the theories and methods of the statistical diagnosis and hypothesis testing for parametric regression model. Several diagnostic measures and the methods for gross error testing are derived. Especially, the global and local influence analysis of the gross error on the parameter X and the nonparameter s are discussed in detail; at the same time, the paper proves that the data point deletion model is equivalent to the mean shift model for the semiparametric regression model. Finally, with one simulative computing example, some helpful conclusions are drawn.     

Forward simulation of gravity for crustal structure of Xiachayu-Gonghe profile in eastern Tibetan plateau

The crustal structure of Xiachayu-Gonghe geophysical profile in eastern Tibetan plateau is simulated with Bouguer anomaly corrected for sediments and lithosphere. The forward simulation shows that the thickness of upper crust in eastern Tibetan plateau is about 20 km, and the density is 2.78×103kg/m3. The bottom interface of middle crust changes from 30 km to 40 km, the density of middle crust is 2.89×103kg/m3. The materials with low density of 2.78 ×103kg/m3 exist in middle crust, and those with high density of 3.33 ×103kg/m3 exist at the bottom of middle crust between Wenquan and Tanggemu. The density is 3.10 ×103kg/m3 in lower crust. The shallowest depth of Moho interface is about 56 km, and the deepest one is about 74 km, the undulation of interface is large, the deep Moho is located in Xiachayu, Chayu, Nujiang, and Wenquan. The crustal density of eastern Tibetan plateau is larger than that of central section; the low velocity layers are located in middle crust and bottom in eastern Tibetan plateau and at the bottom of the upper crust in the central plateau.     

Discovering Spatial Interaction Communities from Mobile Phone Data

In the age of Big Data, the widespread use of location‐awareness technologies has made it possible to collect spatio‐temporal interaction data for analyzing flow patterns in both physical space and cyberspace. This research attempts to explore and interpret patterns embedded in the network of phone‐call interaction and the network of phone‐users’ movements, by considering the geographical context of mobile phone cells. We adopt an agglomerative clustering algorithm based on a Newman‐Girvan modularity metric and propose an alternative modularity function incorporating a gravity model to discover the clustering structures of spatial‐interaction communities using a mobile phone dataset from one week in a city in China. The results verify the distance decay effect and spatial continuity that control the process of partitioning phone‐call interaction, which indicates that people tend to communicate within a spatial‐proximity community. Furthermore, we discover that a high correlation exists between phone‐users’ movements in physical space and phone‐call interaction in cyberspace. Our approach presents a combined qualitative‐quantitative framework to identify clusters and interaction patterns, and explains how geographical context influences communities of callers and receivers. The findings of this empirical study are valuable for urban structure studies as well as for the detection of communities in spatial networks.     

Multi-resolution seamless image database

This paper presents the basic concepts and principles, data structure and high efficient spatial index for multi-resolution image database. The database is characterized by arrangement of multi-resource image data and seamless mosaic, distribution-based storage and management, integration with other spatial database software such as GeoStar and GeoGrid developed by Wuhan Technical University of Surveying and Mapping.     

Development of a data mining method for land control

Land resources are facing crises of being misused, especially for an intersection area between town and country, and land control has to be enforced. This paper presents a development of data mining method for land control. A vector-match method for the prerequisite of data mining i. e., data cleaning is proposed, which deals with both character and numeric data via vectorizing character-string and matching number. A minimal decision algorithm of rough set is used to discover the knowledge hidden in the data warehouse. In order to monitor land use dynamically and accurately, it is suggested to set up a real-time land control system based on GPS, digital photogrammetry and online data mining. Finally, the means is applied in the intersection area between town and country of Wuhan city, and a set of knowledge about land control is discovered.     

Definitions of natural-language spatial relations: Combining topology and directions

Because SQL for querying data from spatial databases is ineffective, the query based on natural or visual language becomes an attractive research field gradually. However, how to define and represent natural languages related to spatial data are still gigantic problems. Because existing models of direction relations can't describe by use of some common concepts. First of all, detailed direction relations are proposed to describe the directions related to the interior of spatial objects, such as “east part of a region”, “east boundary of a region”, and so on. Secondly, by integrating the detailed directions with exterior direction relations and topological relations, several NLSRs are defined, such as “a road goes across the east part of a lake”, “a river goes along the east boundary of a province”, etc. Finally, based on the NLSRs abovementioned, a natural spatial query language (NSQL) is formed to retrieve data from spatial databases.     

Data model and visualization of 3D city landscape based on integrated databases

Data acquisition and modeling are the two important, difficult and costful aspects in a Cybercity project. 2D-GIS is mature and can manage a lot of spatial data. Thus 3D-GIS should make the best of data and technology of 2D-GIS. Construction of a useful synthetic environment requires integration of multiple types of information like DEM, texture images and 3D representation of objects such as buildings. In this paper, the method for 3D city landscape data model and visualization based on integrated databases is presented. Since, the data volume of raster are very huge, special strategies (for example, pyramid gridded method) must be adopted in order to manage raster data efficiently. Three different methods of data acquisition, the proper data structure and a simple modeling method are presented as well. At last, a pilot project of Shanghai Cybercity is illustrated.     

Accuracy analysis for SST gravity field model in China

Taking China as the region for test the potential of the new satellite gravity technique, satelliteto-satellite tracking for improving the accuracy of regional gravity field model is studied. With WDM94 as reference, the gravity anomaly residuals of three models, the latest two GRACE global gravity field model (EIGEN_GRACE02S, GGM02S) and EGM96, are computed and compared. The causes for the differences among the residuals of the three models are discussed. The comparison between the residuals shows that in the selected region, EIGEN_GRACE02S or GGM02S is better than EGM96 in lower degree part (less than 110 degree). Additionally, through the analysis of the model gravity anomaly residuals, it is found that some systematic errors with periodical properties exist in the higher degree part of EIGEN and GGM models, the results can also be taken as references in the validation of the SST gravity data.     

Space,time, and situational awareness in natural hazards: a case study of Hurricane Sandy with social media data

ABSTRACT

Various methods have been developed to investigate the geospatial information, temporal component, and message content in disaster-related social media data to enrich human-centric information for situational awareness. However, few studies have simultaneously analyzed these three dimensions (i.e. space, time, and content). With an attempt to bring a space–time perspective into situational awareness, this study develops a novel approach to integrate space, time, and content dimensions in social media data and enable a space–time analysis of detailed social responses to a natural disaster. Using Markov transition probability matrix and location quotient, we analyzed the Hurricane Sandy tweets in New York City and explored how people’s conversational topics changed across space and over time. Our approach offers potential to facilitate efficient policy/decision-making and rapid response in mitigations of damages caused by natural disasters.     

Point grid map: a new type of thematic map for statistical data associated with geographic points

ABSTRACT

Social, economic, and environmental statistical data associated with geographic points are currently globally available in large amounts. When conventional thematic maps, such as proportional symbol maps or point diagram maps, are used to represent these data, the maps appear cluttered if the point data volumes are relatively large or cover a relatively dense region. To overcome these limitations, we propose a new type of thematic map for statistical data associated with geographic points: the point grid map. In a point grid map, an input point data set is transformed into a grid in which each point is represented by a square grid cell of equal size while preserving the relative position of each point, which leads to a clear and uncluttered appearance, and the grid cells can be shaded or patterned with symbols or diagrams according to the attributes of the points. We present an algorithm to construct a point grid map and test it with several simulated and real data sets. Furthermore, we present some variants of the point grid map.