GEOGRAPHICAL INFORMATION SYSTEMS AND THE DURABLE CONSTRUCTION OF URBAN AND RURAL ENVIRONMENTS

The idea of basis is the present condition and developing trend of cadastral management Valcea. The paper expatiates on the construction status of the Cadastral Management Information System on Integration of Urban and Rural area of Valcea. For a long time, urban and rural area is managed separately, and their database is established respectively, so it's very hard to update data synchronously. These problems can't be solved until cadastral management information system is established, which also makes cadastral product accord with new land classification system of urban and rural integration. Several issues are discussed: the objective, idea, principle of system design, the hardware and software environment, overall framework design, cadastral database structure design and function design.Geographical Informatics Systems, Database, Cadastral Management Information System

The representation and management of evolving features in geospatial databases

Geographic features change over time, this change being the result of some kind of event or occurrence. It has been a research challenge to represent this data in a manner that reflects human perception. Most database systems used in geographic information systems (GIS) are relational, and change is either captured by exhaustively storing all versions of data, or updates replace previous versions. This stems from the inherent diffculty of modelling geographic objects in relational tables. This diffculty is compounded when the necessary time dimension is introduced to model how those objects evolve. There is little doubt that the object-oriented (OO) paradigm holds signi cant advantages over the relational model when it comes to modelling real-world entities and spatial data, and it is argued that this contention is particularly true when it comes to spatio-temporal data. This thesis describes an object-oriented approach to the design of a conceptual model for representing spatio-temporal geographic data, called the Feature Evolution Model (FEM), based on states and events. The model was used to implement a spatio-temporal database management system in Oracle Spatial, and an interface prototype is described that was used to evaluate the system by enabling querying and visualisation

Geodata

Empirical data can be characterized by a precise location in space and time. An estimated 80% of all data holds such a spatio-temporal reference and is termed geodata. This paper starts with the question: What is the additional benefit for socio-economic sciences using geodata and the spatial dimension respectively? In the following a multidimensional approach is chosen to outline the Status Quo of geodata and spatial techniques in Germany. It is particularly the continuously growing amount and the variety of available geodata which is stated. Data security is an issue of high importance when using geodata. Furthermore, the present developments in price and user concepts, accessibility, technical standards and institutionalisation are addressed. A number of challenges concerning the field of geodata are identified including the open access to geodata, data security issues and standardization. The main challenge however seems to be the exchange between the rather segregated fields of geoinformation and the information infrastructure. Furthermore, the census 2011 is identified as a major challenge for the acquisition and management of geodata. Geodata and spatial techniques are a rapidly developing field due to technology developments of data and methods as well as due to recently growing public interest. Their additional be efit for socioeconomic research should be exploited in the future.geodata, geoinformation, Web-GIS, geodata-infrastructure, spatial techniques

An Exploratory Data Analysis Approach for Land Use-Transportation Interaction: The Design and Implementation of Transland Spatio-Temporal Data Model

Land use and transportation interaction is a complex and dynamic process. Many models have been used to study this interaction during the last several decades. Empirical studies suggest that land use and transportation patterns can be highly variable between geographic areas and at different spatial and temporal scales. Identifying these changes presents a major challenge. When we recognize that long-term changes could be affected by other factors such as population growth, economic development, and policy decisions, the challenge becomes even more overwhelming. Most existing land use and transportation interaction models are based on some prior theories and use mathematical or simulation approaches to study the problem. However, the literature also suggests that little consensus regarding the conclusions can be drawn from empirical studies that apply these models. There is a clear research need to develop alternative methods that will allow us to examine the land use and transportation patterns in more flexible ways and to help us identify potential improvements to the existing models. This dissertation presents a spatio-temporal data model that offers exploratory data analysis capabilities to interactively examine the land use and transportation interaction at use-specified spatial and temporal scales. The spatio-temporal patterns and the summary statistics derived from this interactive exploratory analysis process can be used to help us evaluate the hypotheses and modify the structures used in the existing models. The results also can suggest additional analyses for a better understanding of land use and transportation interaction. This dissertation first introduces a conceptual framework for the spatio-temporal data model. Then, based on a systematic method for explorations of various data sets relevant to land use and transportation interaction, this dissertation details procedures of designing and implementing the spatio-temporal data model. Finally, the dissertation describes procedures of creating tools for generating the proposed spatio-temporal data model from existing snapshot GIS data sets and illustrate its use by means of exploratory data analysis. Use of the spatio-temporal data model in this dissertation study makes it feasible to analyze spatio-temporal interaction patterns in a more effective and efficient way than the conventional snapshot GIS approach. Extending Sinton’s measurement framework into a spatio-temporal conceptual interaction framework, on the other hand, provides a systematic means of exploring land use and transportation interaction. Preliminary experiments of data collected for Dade County (Miami), Florida suggest that the spatio-temporal exploratory data analysis implemented for this dissertation can help transportation planners identify and visualize interaction patterns of land use and transportation by controlling the spatial, attribute, and temporal components. Although the identified interaction patterns do not necessarily lead to rules that can be applied to different areas, they do provide useful information for transportation modelers to re-evaluate the current model structure to validate the existing model parameter

Quantifying the benefits of vehicle pooling with shareability networks

Taxi services are a vital part of urban transportation, and a considerable contributor to traffic congestion and air pollution causing substantial adverse effects on human health. Sharing taxi trips is a possible way of reducing the negative impact of taxi services on cities, but this comes at the expense of passenger discomfort quantifiable in terms of a longer travel time. Due to computational challenges, taxi sharing has traditionally been approached on small scales, such as within airport perimeters, or with dynamical ad-hoc heuristics. However, a mathematical framework for the systematic understanding of the tradeoff between collective benefits of sharing and individual passenger discomfort is lacking. Here we introduce the notion of shareability network which allows us to model the collective benefits of sharing as a function of passenger inconvenience, and to efficiently compute optimal sharing strategies on massive datasets. We apply this framework to a dataset of millions of taxi trips taken in New York City, showing that with increasing but still relatively low passenger discomfort, cumulative trip length can be cut by 40% or more. This benefit comes with reductions in service cost, emissions, and with split fares, hinting towards a wide passenger acceptance of such a shared service. Simulation of a realistic online system demonstrates the feasibility of a shareable taxi service in New York City. Shareability as a function of trip density saturates fast, suggesting effectiveness of the taxi sharing system also in cities with much sparser taxi fleets or when willingness to share is low.Comment: Main text: 6 pages, 3 figures, SI: 24 page

Spatio-temporal architecture-based framework for testing services in the cloud

Increasingly, various services are deployed and orchestrated in the cloud to form global, large-scale systems. The global distribution, high complexity, and physical separation pose new challenges into the quality assurance of such complex services. One major challenge is that they are intricately connected with the spatial and temporal characteristics of the domains they support. In this paper, we present our visions on the integration of spatial and temporal logic into the system design and quality maintenance of the complex services in the cloud. We suggest that new paradigms should be proposed for designing software architecture that will particularly embed the spatial and temporal properties of the cloud services, and new testing methodologies should be developed based on architecture including spatio-temporal aspects. We also discuss several potential directions in the relevant research