Geospatial Ontology Development and Semantic Analytics

Geospatial ontology development and semantic knowledge discovery addresses the need for modeling, analyzing and visualizing multimodal information, and is unique in offering integrated analytics that encompasses spatial, temporal and thematic dimensions of information and knowledge. The comprehensive ability to provide integrated analysis from multiple forms of information and use of explicit knowledge make this approach unique. This also involves specification of spatiotemporal thematic ontologies and populating such ontologies with high quality knowledge. Such ontologies form the basis for defining the meaning of important relations terms, such as near or surrounded by, and enable computation of spatiotemporal thematic proximity measures we define. SWETO (Semantic Web Technology Evaluation Ontology) and geospatial extension SWETO‐GS are examples of these ontologies. The Geospatial Semantics Analytics (GSA) framework incorporates: (1) the ability to automatically and semi‐automatically tract metadata from syntactically (including unstructured, semi‐structured and structured data) and semantically heterogeneous and multimodal data from diverse sources; and (2) analytical processing that exploits these ontologies and associated knowledge bases, with integral support for what we term spatiotemporal thematic proximity (STTP) reasoning and interactive visualization capabilities. This paper discusses the results of our geospatial ontology development efforts as well as some new semantic analytics methods on this ontology such as STTP.     

An Algebra for Spatiotemporal Data: From Observations to Events

Recent technological advances in geospatial data gathering have created massive data sets with better spatial and temporal resolution than ever before. These large spatiotemporal data sets have motivated a challenge for Geoinformatics: how to model changes and design good quality software. Many existing spatiotemporal data models represent how objects and fields evolve over time. However, to properly capture changes, it is also necessary to describe events. As a contribution to this research, this article presents an algebra for spatiotemporal data. Algebras give formal specifications at a high‐level abstraction, independently of programming languages. This helps to develop reliable and expressive applications. Our algebra specifies three data types as generic abstractions built on real‐world observations: time series, trajectory and coverage. Based on these abstractions, it defines object and event types. The proposed data types and functions can model and capture changes in a large range of applications, including location‐based services, environmental monitoring, public health, and natural disasters.     

A framework for mixed-use decomposition based on temporal activity signatures extracted from big geo-data

ABSTRACT

Mixed use has been extensively applied as an urban planning principle and hinders the study of single urban functions. To address this problem, it is worth decomposing the mixed use. Inspired by the concept of spectral unmixing in remote sensing applications, this paper proposes a framework for mixed-use decomposition based on big geo-data. Mixed-use decomposition in terms of human activities differs from traditional land use research, and it is more reasonable to infer the actual urban function of land. The framework consists of four steps, namely temporal activity signature extraction, urban function base curve extraction, mixed-use decomposition, and result validation. First, the temporal activity signatures (TASs) of each zone are extracted as the proxy of human activity patterns. Second, the diurnal TASs of routine activities are extracted as urban function base curves (i.e. endmembers). Third, a linear decomposition model is used to decompose the mixed use and obtain multiple results (urban function composition, dynamic activity proportions, and the mixing index). Finally, result validation strategies are concluded. This framework offers method extensibility and has few requirements for the input data. It is validated by means of a case study of Beijing, based on a social media check-in dataset.     

Modeling Alternative Sequences of Events in Dynamic Geographic Domains

A model that emphasizes possible alternative sequences of events that occur over time is presented in this article. Representing alternative or branching events captures additional semantics unrealized by linear or non‐branching approaches. Two basic elements of branching, divergence and convergence are discussed. From these elements, many complex branching models can be built capturing a perspective of events that will take place in the future or have occurred in the past. This produces likely sequences of events that a user may compare and analyze using spatial or temporal criteria. The branching events model is especially useful for spatiotemporal decision support systems, as decision‐makers are able to identify alternative locations and times of events and, depending on the context, also identify regions of multiple possible events. A branching events viewer application is presented illustrating a case study based on a tornado response.     

Open source, spatial analysis, and activity-travel behaviour research: capabilities of the aspace package

This paper reports on recent experience with the development of aspace, an Open Source (OS) library for the geographic visualization and analysis of activity-travel behaviour. The paper begins with an overview of recent progress with respect to the convergence of Open Source technology, spatial analysis, and travel behaviour research. The remainder of the paper focuses on aspace; a collection of functions that, when combined with data describing the geographical location of daily activities, can be used to visualize and describe spatial properties of individual and household activity spaces. These properties include: size, orientation, shape, and the geographical dispersion of activity locations contained within the activity space. Several planar geometries are used to transform measurable spatial properties into intuitive objects for visualizing spatial patterns of activity participation. Experiments are conducted, using data from the first wave of the 2003 Toronto Travel Activity Panel Survey, to demonstrate the potential application of aspace for basic and applied policy-based research into activity-travel behaviour. The toolkit is distributed as a downloadable ‘package’ from the Open Source R Project for Statistical Computing.      

SensePlace3: a geovisual framework to analyze place–time–attribute information in social media

SensePlace3 (SP3) is a geovisual analytics framework and web application that supports overview + detail analysis of social media, focusing on extracting meaningful information from the Twitterverse. SP3 leverages social media related to crisis events. It differs from most existing systems by enabling an analyst to obtain place-relevant information from tweets that have implicit as well as explicit geography. Specifically, SP3 includes not just the ability to utilize the explicit geography of geolocated tweets but also analyze implicit geography by recognizing and geolocating references in both tweet text, which indicates locations tweeted about, and in Twitter profiles, which indicates locations affiliated with users. Key features of SP3 reported here include flexible search and filtering capabilities to support information foraging; an ingest, processing, and indexing pipeline that produces near real-time access for big streaming data; and a novel strategy for implementing a web-based multi-view visual interface with dynamic linking of entities across views. The SP3 system architecture was designed to support crisis management applications, but its design flexibility makes it easily adaptable to other domains. We also report on a user study that provided input to SP3 interface design and suggests next steps for effective spatiotemporal analytics using social media sources.     

Geometric Algebra Model for Geometry‐oriented Topological Relation Computation

Classical topological relation expressions and computations are primarily based on abstract algebra. In this article, the representation and computation of geometry‐oriented topological relations (GOTR) are developed. GOTR is the integration of geometry and topology. The geometries are represented by blades, which contain both algebraic expressions and construction structures of the geometries in the conformal geometric algebra space. With the meet, inner, and outer products, two topology operators, the MeetOp and BoundOp operators, are developed to reveal the disjoint/intersection and inside/on‐surface/outside relations, respectively. A theoretical framework is then formulated to compute the topological relations between any pair of elementary geometries using the two operators. A multidimensional, unified and geometry‐oriented algorithm is developed to compute topological relations between geometries. With this framework, the internal results of the topological relations computation are geometries. The topological relations can be illustrated with clear geometric meanings; at the same time, it can also be modified and updated parametrically. Case studies evaluating the topological relations between 3D objects are performed. The result suggests that our model can express and compute the topological relations between objects in a symbolic and geometry‐oriented way. The method can also support topological relation series computation between objects with location or shape changes.     

面向地理环境主体GIS初探

依据人地关系理论，提出发展面向地理环境主体的GIS理念。面向地理环境主体的GIS，主要研究在相关地理生态与社会经济环境中关于主体时空分布、社会经济活动行为时空特征与规律的表达、计算、模拟与分析。探讨了面向地理环境主体GIS的主要特征，以及智能体建模、时空行为数据模型与可视化表现方法。     

A high-precision heuristic model to detect home and work locations from smart card data

Smart card-automated fare collection systems now routinely record large volumes of data comprising the origins and destinations of travelers. Processing and analyzing these data open new opportunities in urban modeling and travel behavior research. This study seeks to develop an accurate framework for the study of urban mobility from smart card data by developing a heuristic primary location model to identify the home and work locations. The model uses journey counts as an indicator of usage regularity, visit-frequency to identify activity locations for regular commuters, and stay-time for the classification of work and home locations and activities. London is taken as a case study, and the model results were validated against survey data from the London Travel Demand Survey and volunteer survey. Results demonstrate that the proposed model is able to detect meaningful home and work places with high precision. This study offers a new and cost-effective approach to travel behavior and demand research.     

Human conceptions of spaces: Implications for GIS

The way people conceptualize space is an important consideration for the design of GIS, because a better match with people's thinking is expected to lead to easier-to-use information systems. Everyday space, the basis to GIS, has been characterized in the literature as being either small-scale (from table-top to room-size spaces) or large-scale (inside-of-building spaces to city-size spaces). While this dichotomy of space is grounded in the view from psychology that people's perception of space, spatial cognition, and spatial behaviour are experience-based, it is in contrast to current GIS, which enable us to interact with large-scale spaces as though they were small-scale or manipulable. We analyse different approaches to characterizing spaces and propose a unified view in which space is based on the physical properties of manipulability, locomotion, and size of space. Within the structure of our framework, we distinguish six types of spaces: manipulable object space (smaller than the human body), non-manipulable object space (greater than the human body, but less than the size of a building), environmental space (from inside-of-building spaces to city-size spaces), geographic space (state, country, and continent-size spaces), panoramic space (spaces perceived via scanning the landscape), and map space. Such a categorization is an important part of Naive Geography, a set of theories on how people intuitively or spontaneously conceptualize geographic space and time, because it has implications for various theoretical and methodological questions concerning the design and use of spatial information tools. Of particular concern is the design of effective spatial information tools that lead to better communication.     

Application of GPS for transportation related engineering surveys

Summary The Texas State Department of Highways and Public Transportation(SDHPT) has been usingGPS for over two years to establish primary geodetic reference points for engineering projects and mapping control. In accordance with a Five YearGPS Implementation Plant developed in 1982, fourGPS, unmanned, automatic Regional Reference Point(RRP) stations will be installed by September 1, 1986. Five additional stations are planned as justified. EachRRP will consist of a dual frequencyGPS receiver that will ultimately track the satellites continuously. Operation of the receiver, telecommunications and other station keeping chores will be handled by a microcomputer. TheRRP station network will be controlled through another centrally located microcomputer which is also interfaced with a larger mainframe system. EachRRP is designed to service an area bounded by a200 KM radius and will act as the “other” receiver for roving field units operating in aGPS differential measurement mode. In order to meet the installation schedule, early decisions are being made concerning satellite tracking rates, operational scenarios, and telecommunications to facilitate development of the basic hardware and software systems. A period of continual enhancement to hardware, software andRRP operational procedures is expected asGPS technology expands.     

A time‐geographic approach to quantifying wildlife–road interactions

Recent advances in time geography offer new perspectives for studying animal movements and interactions in an environmental context. In particular, the ability to estimate an animal's spatial location probabilistically at temporal sampling intervals between known fix locations allows researchers to quantify how individuals interact with one another and their environment on finer temporal and spatial scales than previously explored. This article extends methods from time geography, specifically probabilistic space–time prisms, to quantify and summarize animal–road interactions toward understanding related diurnal movement behaviors, including road avoidance. The approach is demonstrated using tracking data for fishers (Martes pennanti) in New York State, where the total probability of interaction with roadways is calculated for individuals over the duration tracked. Additionally, a summarization method visualizing daily interaction probabilities at 60 s intervals is developed to assist in the examination of temporal patterns associated with fishers’ movement behavior with respect to roadways. The results identify spatial and temporal patterns of fisher–roadway interaction by time of day. Overall, the methodologies discussed offer an intuitive means to assess moving object location probabilities in the context of environmental factors. Implications for movement ecology and related conservation planning efforts are also discussed.     

REMOTE SENSING AND THE STUDY OF SIBERIAN MINERAL RESOURCES

The authors outline the general policy governing the use of remote sensing in Siberian mineral exploration—as a tool in the structural-geomorphologic mapping of potential mineral-bearing locations. Such maps are used to infer geologic structure (and endogenous conditions for mineral formation) and the character of contemporary exogenous processes (and conditions of transport and concentration of mineral residues) from geoindicators in surface terrain visible from space. Uses of space imagery in the study of seismic activity and engineering geomorphology are also described. Translated from: Kompleksnyye aerokosmicheskiye Issledovaniya Sibiri, A. L. Yanshin and L. K. Zyat'kova, eds. Novosibirsk: Nauka, 1984, pp. 5-14.     

A framework for identifying spatiotemporal hot spots of speculative land transactions using prospective monitoring procedures: the case of South Korea

Abnormally high-priced transactions in urban land speculation bring detrimental effects on economy, environment, and society. Governmental agencies around the world are striving hard to monitor and control land speculation by introducing various policy objectives and tools for an efficient urban development planning. One of the major challenges in controlling land speculation is to quickly identify the spatiotemporal locations of concern (hot spots) by monitoring the spatial clustering pattern changes over time and to alert the appropriate decision-making agencies for timely policy intervention. In this paper, we introduce a framework to rapidly detect the spatiotemporal hot spots of speculative land transactions in near-real-time data by exploiting the prospective monitoring procedures. We applied this method in the city of Hwasung, Republic of Korea, as an empirical illustration and found that the locations Jeongnam, Bongdam, Mado, and Dongtan were identified as hot spots with high, concentrated transaction values. The results indicate that the proposed framework is a capable tool for capturing prospective temporal indicators and pinpointing the localities of land speculation.     

A spatiotemporal mining framework for abnormal association patterns in marine environments with a time series of remote sensing images

A spatiotemporal mining framework is a novel tool for the analysis of marine association patterns using multiple remote sensing images. From data pretreatment, to algorithm design, to association rule mining and pattern visualization, this paper outlines a spatiotemporal mining framework for abnormal association patterns in marine environments, including pixel-based and object-based mining models. Within this framework, some key issues are also addressed. In the data pretreatment phase, we propose an algorithm for extracting abnormal objects or pixels over marine surfaces, and construct a mining transaction table with object-based and pixel-based strategies. In the mining algorithm phase, a recursion method to construct a direct association pattern tree is addressed with an asymmetric mutual information table, and a recursive mining algorithm to find frequent items. In the knowledge visualization phase, a “Dimension–Attributes” visualization framework is used to display spatiotemporal association patterns. Finally, spatiotemporal association patterns for marine environmental parameters in the Pacific Ocean are identified, and the results prove the effectiveness and the efficiency of the proposed mining framework.     

Parameter exploration of the raster space activity bundle simulation

Research on the transmission of infectious diseases amongst the human population at fine scales is limited. At this level, the dynamics of humans are determined by their social activities and the physical condition of the environment. Raster space AB (activity bundle) simulation is a method to simulate humans' contacts within a space under the framework of an individual space–time activity-based model (ISTAM). The parameters of the raster space AB simulation were explored. For static ABs, the results show the relations between proportion infected and proportion occupied, number of index cases, size of AB, size of cell, ratio of AB and ratio of cell, respectively due to the different spatial distributions of individuals. The most important parameters were number of index cases, size of AB and proportion occupied. For dynamic ABs, analysis shows that movement frequency is more important than movement proportion. An example application of raster space AB simulation shows that this method can be used effectively to quantify the infection risk (proportion infected) at the within-AB level. This research can further the understanding of transmission process at fine scales and is beneficial to the design and testing of control measures.      

Bayesian dynamic modeling for large space-time datasets using Gaussian predictive processes

In this paper, we extend the applicability of a previously proposed class of dynamic space-time models by enabling them to accommodate large datasets. We focus on the common setting where space is viewed as continuous but time is taken to be discrete. Scalability is achieved by using a low-rank predictive process to reduce the dimensionality of the data and ease the computational burden of estimating the spatio-temporal process of interest. The proposed models are illustrated using weather station data collected over the northeastern United States between 2000 and 2005. Here our interest is to use readily available predictors, association among measurements at a given station, as well as dependence across space and time to improve prediction for incomplete station records and locations where station data does not exist.     

Mixed Integer-Real Valued Adjustment (IRA) Problems: GPS Initial Cycle Ambiguity Resolution by Means of the LLL Algorithm

In order to achieve to GPS solutions of first-order accuracy and integrity, carrier phase observations as well as pseudorange observations have to be adjusted with respect to a linear/linearized model. Here the problem of mixed integer-real valued parameter adjustment (IRA) is met. Indeed, integer cycle ambiguity unknowns have to be estimated and tested. At first we review the three concepts to deal with IRA: (i) DDD or triple difference observations are produced by a properly chosen difference operator and choice of basis, namely being free of integer-valued unknowns (ii) The real-valued unknown parameters are eliminated by a Gauss elimination step while the remaining integer-valued unknown parameters (initial cycle ambiguities) are determined by Quadratic Programming and (iii) a RA substitute model is firstly implemented (real-valued estimates of initial cycle ambiguities) and secondly a minimum distance map is designed which operates on the real-valued approximation of integers with respect to the integer data in a lattice. This is the place where the integer Gram-Schmidt orthogonalization by means of the LLL algorithm (modified LLL algorithm) is applied being illustrated by four examples. In particular, we prove that in general it is impossible to transform an oblique base of a lattice to an orthogonal base by Gram-Schmidt orthogonalization where its matrix enties are integer. The volume preserving Gram-Schmidt orthogonalization operator constraint to integer entries produces “almost orthogonal” bases which, in turn, can be used to produce the integer-valued unknown parameters (initial cycle ambiguities) from the LLL algorithm (modified LLL algorithm). Systematic errors generated by “almost orthogonal” lattice bases are quantified by A. K. Lenstra et al. (1982) as well as M. Pohst (1987). The solution point of Integer Least Squares generated by the LLL algorithm is = (L')⁻¹[L'◯] ∈ ℤ ^m where L is the lower triangular Gram-Schmidt matrix rounded to nearest integers, [L], and = [L'◯] are the nearest integers of L'◯, ◯ being the real valued approximation of z ∈ ℤ ^m , the m-dimensional lattice space Λ. Indeed due to “almost orthogonality” of the integer Gram-Schmidt procedure, the solution point is only suboptimal, only close to “least squares.” ? 2000 John Wiley & Sons, Inc.     

Network Density Estimation: A GIS Approach for Analysing Point Patterns in a Network Space

Human activities and more generally the phenomena related to human behaviour take place in a network‐constrained subset of the geographical space. These phenomena can be expressed as locations having their positions configured by a road network, as address points with street numbers. Although these events are considered as points on a network, point pattern analysis and the techniques implemented in a GIS environment generally consider events as taking place in a uniform space, with distance expressed as Euclidean and over a homogeneous and isotropic space. Network‐spatial analysis has developed as a research agenda where the attention is drawn towards point pattern analytical techniques applied to a space constrained by a road network. Little attention has been put on first order properties of a point pattern (i.e. density) in a network space, while mainly second order analysis such as nearest neighbour and K‐functions have been implemented for network configurations of the geographical space. In this article, a method for examining clusters of human‐related events on a network, called Network Density Estimation (NDE), is implemented using spatial statistical tools and GIS packages. The method is presented and compared to conventional first order spatial analytical techniques such as Kernel Density Estimation (KDE). Network Density Estimation is tested using the locations of a sample of central, urban activities associated with bank and insurance company branches in the central areas of two midsize European cities, Trieste (Italy) and Swindon (UK).     

In-depth examination of spatiotemporal figures in open reproducible research

ABSTRACT

Figures such as maps and time series are essential means to visualize spatiotemporal results in scientific papers. Being able to recompute them using the underlying source code and data is thus a core aspect in reproducible research. However, many scientists see the preparation of code and data for publication as an additional burden without immediate benefits. In this work, we investigate advantages and new capabilities of reproducible research papers. Our key contributions are (i) the extension of a geoscientist’s workflow while examining papers including reproducible figures such as maps and (ii) the prototypical implementation of the workflow as a web application. The workflow is based on current practices of geoscientists and encapsulates benefits of reproducible figures. It is informed by ideas and needs identified by geoscientists in a survey, interviews, and a focus group. Based on their statements, we first extend the traditional workflow steps Discovery and Inspection by additional capabilities and propose two new steps: Manipulation of the content of a spatiotemporal figure and Substitution of the underlying code and data. The extended workflow and its implementation might facilitate in-depth examination and reusability of geoscientific results.