A Web-Based Approach for the Efficient Management of Massive Multi-source 3D Models

Effectively managing extensive,multi-source,and multi-level real-scene 3D models for responsive retrieval scheduling and rapid visualization in the Web environment is a significant challenge in the current development of real-scene 3D applications in China.In this paper,we address this challenge by reorganizing spatial and temporal information into a 3D geospatial grid.It introduces the Global 3D Geocoding System(G_(3)DGS),leveraging neighborhood similarity and uniqueness for efficient storage,retrieval,updating,and scheduling of these models.A combination of G_(3)DGS and non-relational databases is implemented,enhancing data storage scalability and flexibility.Additionally,a model detail management scheduling strategy(TLOD)based on G_(3)DGS and an importance factor T is designed.Compared with mainstream commercial and open-source platforms,this method significantly enhances the loadable capacity of massive multi-source real-scene 3D models in the Web environment by 33%,improves browsing efficiency by 48%,and accelerates invocation speed by 40%.

A Bayesian Mixture Model Approach to Disparity Testing

The topic of this article is one-sided hypothesis testing for disparity, i.e., the mean of one group is larger than that of another when there is uncertainty as to which group a datum is drawn. For each datum, the uncertainty is captured with a given discrete probability distribution over the groups. Such situations arise, for example, in the use of Bayesian imputation methods to assess race and ethnicity disparities with certain insurance, health, and financial data. A widely used method to implement this assessment is the Bayesian Improved Surname Geocoding (BISG) method which assigns a discrete probability over six race/ethnicity groups to an individual given the individual’s surname and address location. Using a Bayesian framework and Markov Chain Monte Carlo sampling from the joint posterior distribution of the group means, the probability of a disparity hypothesis is estimated. Four methods are developed and compared with an illustrative data set. Three of these methods are implemented in an R-code and one method in WinBUGS. These methods are programed for any number of groups between two and six inclusive. All the codes are provided in the appendices.

Addressing administrative units in international tsunami early warning systems: shortcomings in international geocode standards

Administrative units reflect the territorial hierarchies established within all countries of the world.The units are addressable with geocodes that provide a bijective mapping between territories and unique identification codes.Early warning systems for natural or man-made hazards often map affected or threatened areas to administrative units to establish a spatial reference that is comprehensible to all parts of the population.Addressing these territories in an international context has several requirements,such as worldwide coverage,completeness and topicality,which must be met by geocode standards.In this paper,the practicability and suitability of international geocode standards are examined in the context of the requirements of large-scale early warning systems.This paper exposes the insufficiencies and limitations of existing geocode standards International Organization for Standardization(ISO)-3166,Second Administrative Level Boundaries data set project(SALB)and Nomenclature of the Territorial Units for Statistics(NUTS)and emphasises the suitability of the non-official hierarchical administrative subdivision codes(HASC).The analysis is framed in the context of addressing affected areas for an Indian Ocean tsunami early warning system.This system was developed within the Distant Early Warning Systems project according to the requirements of the United Nations Educational,Scientific and Cultural Organization(UNESCO)Intergovernmental Oceanic Commission for Regional Tsunami Watch Providers(RTWPs).

深水多波束回波强度数据处理技术探讨

多波束回波强度数据不但可用于分析和解释海底地貌,也可用于反演海底底质类型及其分布状况或者水下目标识别方面的研究。以EM 122多波束声纳系统数据为例,详细探讨应用CARIS多波束处理软件对某深水多波束作业区的回波强度数据的处理过程。根据软件处理后的数据浅析灰度量化及拼接方法,进一步改善声强图像的质量,后处理图像为多波束水下目标识别和海底底质分类研究提供准确、表述清晰的基础数据。

基于Google Android平台的应用程序开发与研究

由Google倡导发起的开放手机联盟及其推出的Android平台，一经发布便在业内引起轩然大波。首先介绍了android平台中的内部结构，并通过和现有的智能手机开发平台进行比较研究分析了该平台的结构特点，以此来说明android平台具有强大的开放性和兼容性。然后重点介绍了该平台强大的功能之一——Goode Map，通过开发一个利用地理定位服务来获取当前设备地理位置的应用实例，并详细地介绍了Google Map中丰富的API功能以及应用程序的基本开发流程．来验证Android SDK提供的强大的技术支持。

基于Google Maps API的地图解析研究与实现

首先对Google Maps的运行机制进行分析,研究了Google Maps主要采用的两种技术:图片数据分片和Ajax技术.然后重点探讨Google Maps的地图解析,并设计一个基于Google Maps API的地图解析的持久化方案,最后对这个方案进行了实现.

Evaluation of PS-DInSAR technology for subsidence monitoring caused by repeated mining in mountainous area

The high resolution Terra SAR-X dataset was employed with DIn SAR and persistent scatterer interferometry（PSI） technique for subsidence monitoring in a mountainous area. For DInS AR technique, the generally used SRTM and relief-DEM, which was derived from aerial topographic map, were used to evaluate the influence of external DEM. The results show that SRTM could not fully compensate the complex topography of the research area. The corner reflectors installed during the acquisition of SAR dataset were used to estimate the accuracy of geocoding. The terrain corrected geocoding results based on relief-DEM were much better than using SRTM, with the root mean square error（RMSE） being 6.35 m in X direction and 11.65 m in Y direction（both in UTM projection）, around one pixel of the multilooked intensity image to be geocoded. For PSI technique, the results from time-series analysis of multi-baseline differential interferograms were integrated to restrict only persistent scatterer candidates near the boundary of subsiding area for regression analysis. The results demonstrate that PSI can refine the boundary of subsidence, which could then be used to derive some angular parameters to help people to learn the law of subsidence caused by repeated excavation in this area.

REVERSE-RANGE-DOPPLER METHOD FOR AUTOMATED GEOCODING SAR IMAGES

Range-Doppler (RD) method and Reverse-Range-Doppler (RRD) method are combined together to achieve automatic geocoding of Synthetic Aperture Radar (SAR) image quickly and accurately in the paper. The RD method is firstly used to locate the four corners of the image, then the other pixels of the image can be located by Reverse-Range-Doppler (RRD) method. Resampling is performed at last. The approach has an advantage over previous techniques in that it does not require ground control points and is independent of spacecraft attitude knowledge or control. It can compensate the shift due to the assumed Doppler frequency in SAR image preprocessing. RRD simplifies the process of RD, therefore speeds up the computation. The experimental results show that a SAR image can be automated geocoded in 30 s using the single CPU (3 GHz) with 1 G memory and an accuracy of 10 m is attainable with this method.

Spatio-Temporal Distribution of Malaria in Perak, Malaysia

Malaria incidence in Perak, Malaysia has generally declined, but there remain regions of high incidence. The spatio-temporal distribution pattern of malaria in Perak was studied using Geographical Information Systems (GIS) and spatial statistical tools. Malaria data cases at the subdistrict level in Perak from 2007 to 2011 were analysed to determine the spatial and temporal distribution patterns of malaria incidence. Geographical Information Systems (GIS) and spatial statistical tools were used to identify spatial correlation in the data and malaria hot-spots. Spatial correlation was tested by using an autocorrelation method called Moran’s I. Hot spot analysis was done using Getis-Ord G*?statistic technique. Malaria incidence rates were categorized into 3 classes to map the spatial distribution. Malaria cases in Perak were geo-spatially clustered. Most of the hot spots locations were in Kenering, Ulu Kinta, Gerik and Kampar sub-districts. The prevalence of malaria among foreigners was noticeably higher than Malaysians. Improved surveillance of foreign workers can prevent outbreaks and identify high risk areas. This study implies that geographic-based mapping and information system are needed for an effective malaria control.

An address regional tessellation method for spatial subdivision and geocoding in digital earth system

With the increased use of locational information,spatial location referencing and coding methods have become much more important to the mining of both geographical and nongeographical data in digital earth system.Unfortunately,current methods of geocoding,based on reverse lookup of coordinates for a given address,have proven too lossy with respect to administrative and socioeconomic data.This paper proposes a spatial subdivision and geocoding model based on spatial address regional tessellation(SART).Given a hierarchical address object definition,and based on the‘region of influence’characteristics of an address,SART creates multiresolution spatial subdivisions by irregular and continuous address regions.This model reflects most of the geographical features and many of the social and economic implications for a given address.It also better reflects the way people understand addresses and spatial locations.We also propose an appropriate method of geocoding for standard addresses(SART-GC).The codes generated by this method can record address footprints,hierarchical relationships,and spatial scales in a single data structure.Finally,by applying our methods to the Shibei District of Qingdao,we demonstrate the suitability of SART-GC for multi-scale spatial information representation in digital earth systems.

An automated geo-spatial correction framework for transportation

In this paper, we present a framework to validate and to correct the location information in crash reports. The proposed system is composed of several modules: creation of intersection network and pre-processing, validation, snapping, updating, and manual-spotting.The proposed system utilizes GPS coordinates(latitude/longitude), primary and reference road names, primary direction, and distance to the reference road. The algorithm starts at an initial GPS coordinate provided in the crash report. Additional location information is utilized in case this coordinate does not satisfy certain criteria. After verification of the road names, the correct snapping location is determined using the primary direction and primary distance information. At the final stage, the attributes in the crash data are retrieved from the base map given the snapping point. We tested the proposed system by three different test data sets. The number of correctly identified matches and the average snapping error are calculated for assessment. The experiments show that the proposed framework is capable of geocoding the crashes up to 98% accuracy with the average of 37 feet of snapping error and 64% of the matching records have less than snapping error of 1 foot. However, we observed that considering highly specific cases as an outlier, the snapping error significantly drops. We discuss these cases in detail and propose a remedy as a future work.