A Generalized Markov Chain Approach for Conditional Simulation of Categorical Variables from Grid Samples

Complex categorical variables are usually classified into many classes with interclass dependencies, which conventional geostatistical methods have difficulties to incorporate. A two‐dimensional Markov chain approach has emerged recently for conditional simulation of categorical variables on line data, with the advantage of incorporating interclass dependencies. This paper extends the approach into a generalized method so that conditional simulation can be performed on grid point samples. Distant data interaction is accounted for through the transiogram – a transition probability‐based spatial measure. Experimental transiograms are estimated from samples and further fitted by mathematical models, which provide transition probabilities with continuous lags for Markov chain simulation. Simulated results conducted on two datasets of soil types show that when sufficient sample data are conditioned complex patterns of soil types can be captured and simulated realizations can reproduce transiograms with reasonable fluctuations; when data are sparse, a general pattern of major soil types still can be captured, with minor types being relatively underestimated. Therefore, at this stage the method is more suitable for cases where relatively dense samples are available. The computer algorithm can potentially deal with irregular point data with further development.     

Enhancing a GIS Cellular Automata Model of Land Use Change: Bayesian Networks, Influence Diagrams and Causality

Cellular Automata (CA) models at present do not adequately take into account the relationship and interactions between variables. However, land use change is influenced by multiple variables and their relationships. The objective of this study is to develop a novel CA model within a geographic information system (GIS) that consists of Bayesian Network (BN) and Influence Diagram (ID) sub‐models. Further, the proposed model is intended to simplify the definition of parameter values, transition rules and model structure. Multiple GIS layers provide inputs and the CA defines the transition rules by running the two sub‐models. In the BN sub‐model, land use drivers are encoded with conditional probabilities extracted from historical data to represent inter‐dependencies between the drivers. Using the ID sub‐model, the decision of changing from one land use state to another is made based on utility theory. The model was applied to simulate future land use changes in the Greater Vancouver Regional District (GVRD), Canada from 2001 to 2031. The results indicate that the model is able to detect spatio‐temporal drivers and generate various scenarios of land use change making it a useful tool for exploring complex planning scenarios.     

An empirical test of the competing destinations model

 It has long been believed that properties of spatial structure have a strong effect on trip distribution, which thus leads to a bias in the estimated distance decay parameters of spatial interaction models. This paper is an attempt to identify to what extent the spatial structure effect affects the trip distribution and determine whether the incorporation of a term to account for the relative location of destinations into the conventional gravity models, results in a model that can more correctly represent the actual trip distribution. The main focus is on the comparison of the origin–specific estimates of the distance decay parameter, calibrated from the traditional production-constrained model and the production-constrained competing destinations model. The results show that the competing destinations model is superior to the conventional model in both reproducing the interaction flows and giving behavioral explanation to the distance decay parameters, but the essential aim of the competing destinations model to remove the map pattern from the distance decay parameters of the conventional model has not been identified. Received: 5 September 2001 / Accepted: 17 June 2002 We are grateful to Gloria. A. Swieczkowski for kindly providing the migration data. The authors also gratefully acknowledge the comments of the referees.     

Multi-day activity scheduling reactions to planned activities and future events in a dynamic model of activity-travel behavior

Modeling multi-day planning has received scarce attention in activity-based transport demand modeling so far. However, new dynamic activity-based approaches are being developed at the current moment. The frequency and inflexibility of planned activities and events in activity schedules of individuals indicate the importance of incorporating those pre-planned activities in the new generation of dynamic travel demand models. Elaborating and combining previous work on event-driven activity generation, the aim of this paper is to develop and illustrate an extension of a need-based model of activity generation that takes into account possible influences of pre-planned activities and events. This paper describes the theory and shows the results of simulations of the extension. The simulation was conducted for six different activities, and the parameter values used were consistent with an earlier estimation study. The results show that the model works well and that the influences of the parameters are consistent, logical, and have clear interpretations. These findings offer further evidence of face and construct validity to the suggested modeling approach.     

Negotiating on location, timing, duration, and participant in agent-mediated joint activity-travel scheduling

Agent-based simulation has become an important modeling approach in activity-travel analysis. Social activities account for a large amount of travel and have an important effect on activity-travel scheduling. Participants in joint activities usually have various options regarding location, participants, and timing and take different approaches to make their decisions. In this context, joint activity participation requires negotiation among agents involved, so that conflicts among the agents can be addressed. Existing mechanisms do not fully provide a solution when utility functions of agents are nonlinear and non-monotonic. Considering activity-travel scheduling in time and space as an application, we propose a novel negotiation approach, which takes into account these properties, such as continuous and discrete issues, and nonlinear and non-monotonic utility functions, by defining a concession strategy and a search mechanism. The results of experiments show that agents having these properties can negotiate efficiently. Furthermore, the negotiation procedure affects individuals’ choices of location, timing, duration, and participants.     

Deriving rules from activity diary data: A learning algorithm and results of computer experiments

 Activity-based models consider travel as a derived demand from the activities households need to conduct in space and time. Over the last 15 years, computational or rule-based models of activity scheduling have gained increasing interest in time-geography and transportation research. This paper argues that a lack of techniques for deriving rules from empirical data hinders the further development of rule-based systems in this area. To overcome this problem, this paper develops and tests an algorithm for inductively deriving rules from activity-diary data. The decision table formalism is used to exhaustively represent the theoretically possible decision rules that individuals may use in sequencing a given set of activities. Actual activity patterns of individuals are supplied to the system as examples. In an incremental learning process, the system progressively improves on the selection of rules used for reproducing the examples. Computer experiments based on simulated data are performed to fine-tune rule selection and rule value update functions. The results suggest that the system is effective and fairly robust for parameter settings. It is concluded, therefore, that the proposed approach opens up possibilities to derive empirically tested rule-based models of activity scheduling. Follow-up research will be concerned with testing the system on empirical data. Received: 31 January 2001 / Accepted: 13 September 2001     

Towards a conjoint-based, context-dependent model of task allocation in activity settings: Some numerical experiments

 None of the currently developed activity-based models of transport demand explicitly models task allocation among household members. To fill this gap, the present paper suggests to complement activity-based models of activity scheduling with a context-dependent model of task allocation. That is, it is assumed that the allocation of tasks within households is partly based on such contextual variables as the amount of time a member has to spend on mandatory activities and car availability. In particular, the paper advocates a conjoint-based approach, based on an assignment task as opposed to the traditional ranking, rating or choice response formats. By definition, an assignment task involves a combinatorial explosion of choice alternatives, implying that additional operational decisions to estimate the context-dependent model are required. This study presents the results of various numerical experiments, conducted to better understand the impacts of those decisions on the degree of bias in the parameter estimates of the choice model. The results of these simulations indicate that under particular assumptions, the development and estimation of a conjoint-based, context-dependent model of task allocation within households is feasible. Received: March 1999 / Accepted: June 2001     

A Temporal Variant‐Invariant Validation Approach for Agent‐based Models of Landscape Dynamics

Agent‐based modeling provides a means for addressing the way human and natural systems interact to change landscapes over time. Until recently, evaluation of simulation models has focused on map comparison techniques that evaluate the degree to which predictions match real‐world observations. However, methods that change the focus of evaluation from patterns to processes have begun to surface; that is, rather than asking if a model simulates a correct pattern, models are evaluated on their ability to simulate a process of interest. We build on an existing agent‐based modeling validation method in order to present a temporal variant‐invariant analysis (TVIA). The enhanced method, which focuses on analyzing the uncertainty in simulation results, examines the degree to which outcomes from multiple model runs match some reference to how land use parcels make the transition from one land use class to another over time. We apply TVIA to results from an agent‐based model that simulates the relationships between landowner decisions and wildfire risk in the wildland‐urban interface of the southern Willamette Valley, Oregon, USA. The TVIA approach demonstrates a novel ability to examine uncertainty across time to provide an understanding of how the model emulates the system of interest.     

A strategy for GPS data processing in a precise local network during high solar activity

This paper presents the analyses connected with reduction of errors from ionospheric refraction using GPS data from local satellite networks. This is particularly essential during rising solar activity. The Bernese GPS Software v. 4.2 was used, as an analytical tool. The test data included measurements from a geodynamic network SUDETES situated in the Sudety Mountains across the border between the Czech Republic and Poland. A local ionospheric activity model developed from a regional model augmented with data from a local network has been tested at three levels. The criteria included comparison with a global model, the success rate of ambiguity determination using the quasi-ionosphere free and wide-lane/narrow-lane strategies, and in the position domain through analysis of residuals. The results show that the local model increases the success rate for ambiguity determination for the wide-lane/narrow-lane strategy and is available sooner than the global models. The output of the SUDETES network processing including the models of local ionospheric and tropospheric activity have been used to process data from a number of relatively small networks situated in the Sudety mountains.     

利用非线性各向异性扩散和EM的SAR图像分割技术

提出了一种SAR图像分割技术。首先利用各向异性平滑技术对原始图像进行平滑,然后利用高斯混合模型并结合强度和空间结构信息进行聚类,最后对类条件概率进行平滑以消除杂波影响。仿真结果表明,该算法具有良好的鲁棒性,自动化程度高。     

Hedging against service disruptions: an expected median location problem with site-dependent failure probabilities

The vector assignment p-median problem (VAPMP) (Weaver and Church in Transp Sci 19(1):58–74, 1985) was one of the first location-allocation models developed to handle split assignment of a demand to multiple facilities. The underlying construct of the VAPMP has been subsequently used in a number of reliable facility location and backup location models. Although in many applications the chance that a facility fails may vary substantially with locations, many existing models have assumed a uniform failure probability across all sites. As an improvement, this paper proposes a new model, the expected p-median problem as a generalization of existing approaches by explicitly considering site-dependent failure probabilities. Multi-level closest assignment constraints and two efficient integer linear programming (ILP) formulations are introduced. While prior research generally concludes that similar problems are not integer-friendly and cannot be solved by ILP software, computational results show that our model can be used to solve medium-sized location problems optimally using existing ILP software. Moreover, the new model can be used to formulate other reliable or expected location problems with consideration of site-dependent failure probabilities.     

Stochastic modeling of length of day and universal time

We propose a general stochastic model for the UT1/LOD system and derive the corresponding Kalman filter model. This stochastic model consists of an arbitrary sum of continous time autoregressive moving average (ARMA) processes, each chosen to characterize a different frequency band. The transition matrix which corresponds to the overall system and the time-dependent process noise covariance matrix are derived.Based on the general formulation, several models for UT1 were derived from spectral analysis of the Space 92 UT1 series (Gross,1993). Using Space 92 as the reference series, the candidate models were compared based on their ability to predict UT1 and LOD up to 30 days in the absence of data. These candidate models were compared with the JPL operational Kalman Earth Orientation Filter (KEOF) which assumes a random walk model for LOD (Morabito et al.,1987). The results of the comparison revealed that autoregressive modeling the 40–50 day oscillation in the LOD reduces the LOD prediction error by 10prediction.     

遥感影像居民地目标Bayesian网络识别算法研究

利用Naive Bayesian网络的学习和推理机制，提出一种在遥感影像上提取居民地目标的方法。该方法通过对所选取的正负样本进行学习，获取Bayesian网络的重要参数，即条件概率和概率分布密度。在此基础上，根据正负样本所构建的条件概率网，对未知类别信息的影像进行分类，从而获取居民地目标的信息。通过对实际全色SPOT5影像中居民地目标的提取，表明该方法具有较高的识别率。     

An assessment of coefficient accuracy in linear regression models with spatially varying coefficients

The realization in the statistical and geographical sciences that a relationship between an explanatory variable and a response variable in a linear regression model is not always constant across a study area has led to the development of regression models that allow for spatially varying coefficients. Two competing models of this type are geographically weighted regression (GWR) and Bayesian regression models with spatially varying coefficient processes (SVCP). In the application of these spatially varying coefficient models, marginal inference on the regression coefficient spatial processes is typically of primary interest. In light of this fact, there is a need to assess the validity of such marginal inferences, since these inferences may be misleading in the presence of explanatory variable collinearity. In this paper, we present the results of a simulation study designed to evaluate the sensitivity of the spatially varying coefficients in the competing models to various levels of collinearity. The simulation study results show that the Bayesian regression model produces more accurate inferences on the regression coefficients than does GWR. In addition, the Bayesian regression model is overall fairly robust in terms of marginal coefficient inference to moderate levels of collinearity, and degrades less substantially than GWR with strong collinearity.     

Modeling the risk of spread and establishment for Asian longhorned beetle (Anoplophora glabripennis) in Massachusetts from 2008-2009

Land managers responsible for invasive species removal in the USA require tools to prevent the Asian longhorned beetle (Anoplophora glabripennis) (ALB) from decimating the maple-dominant hardwood forests of Massachusetts and New England. Species distribution models (SDMs) and spread models have been applied individually to predict the invasion distribution and rate of spread, but the combination of both models can increase the accuracy of predictions of species spread over time when habitat suitability is heterogeneous across landscapes. First, a SDM was fit to 2008 ALB presence-only locations. Then, a stratified spread model was generated to measure the probability of spread due to natural and human causes. Finally, the SDM and spread models were combined to evaluate the risk of ALB spread in Central Massachusetts in 2008–2009. The SDM predicted many urban locations in Central Massachusetts as having suitable environments for species establishment. The combined model shows the greatest risk of spread and establishment in suitable locations immediately surrounding the epicentre of the ALB outbreak in Northern Worcester with lower risk areas in suitable locations only accessible through long-range dispersal from access to human transportation networks. The risk map achieved an accuracy of 67% using 2009 ALB locations for model validation. This model framework can effectively provide risk managers with valuable information concerning the timing and spatial extent of spread/establishment risk of ALB and potential strategies needed for effective future risk management efforts.     

A place-based model of local activity spaces: individual place exposure and characteristics

Researchers for long have hypothesized relationships between mobility, urban context, and health. Despite the ample amount of discussions, the empirical findings corroborating such associations remain to be marginal in the literature. It is growingly believed that the weakness of the observed associations can be largely explained by the common misspecification of the geographical context. Researchers coming from different fields have developed a wide range of methods for estimating the extents of these geographical contexts. In this article, we argue that no single approach yet has sufficiently been capable of capturing the complexity of human mobility patterns. Subsequently, we discuss that reaching a better understanding of individual activity spaces can be possible through a spatially sensitive estimation of place exposure. Following this discussion, we take an integrative person and place-based approach to create an individualized residential exposure model (IREM) to estimate the local activity spaces (LAS) of the individuals. This model is created using data collected through public participation GIS. Following a brief comparison of IREM with other commonly used LAS models, the article continues by presenting an empirical study of aging citizens in Helsinki area to demonstrate the usability of the proposed framework. In this study, we identify the main dimensions of LASs and seek their associations with socio-demographic characteristics of individuals and their location in the region. The promising results from comparisons and the interesting findings from the empirical part suggest both a methodological and conceptual improvement in capturing the complexity of local activity spaces.     

Improving urban cellular automata performance by integrating global and geographically weighted logistic regression models

In many of the conventional cellular automata (CA) models, particularly Urban‐CA which are used for urban growth, the spatial heterogeneities and local differences of the land use conversion processes are ignored. Global logistic regression (LR) is a popular model employed to define the transition rules of Urban‐CA. By considering the local characteristics, Geographically Weighted Logistic Regression (GWLR) provides interesting capabilities for urban growth modelling. In this research, in addition to using GWLR in the definition of transition rules, the advantages of integrating GWLR and LR for urban growth simulation were evaluated; these have not been considered in previous studies. Local and global probabilities obtained from the calibration of GWLR and LR were combined to define the transition rules of an Urban‐CA. Urban growth was simulated in the Islamshahr sub‐region located southwest of Tehran, Iran for the two periods 1992‐1996 and 1996‐2002, and data from these periods were used for training and testing the prediction abilities, respectively. In the first period, GWLR showed good performance and a significant contribution to the enhancement of the simulation performance, but in the second period, the effectiveness of LR on the prediction accuracy increased. Due to their complementary roles, the integration of the GWLR and LR models resulted in improved simulation performance in both periods.     

Latent spatio-temporal activity structures: a new approach to inferring intra-urban functional regions via social media check-in data

Abstract

This article introduces a novel low rank approximation (LRA)-based model to detect the functional regions with the data from about 15 million social media check-in records during a year-long period in Shanghai, China. We identified a series of latent structures, named latent spatio-temporal activity structures. While interpreting these structures, we can obtain a series of underlying associations between the spatial and temporal activity patterns. Moreover, we can not only reproduce the observed data with a lower dimensional representative, but also project spatio-temporal activity patterns in the same coordinate system. With the K-means clustering algorithm, five significant types of clusters that are directly annotated with a combination of temporal activities can be obtained, providing a clear picture of the correlation between the groups of regions and different activities at different times during a day. Besides the commercial and transportation dominant areas, we also detected two kinds of residential areas, the developed residential areas and the developing residential areas. We further interpret the spatial distribution of these clusters using urban form analytics. The results are highly consistent with the government planning in the same periods, indicating that our model is applicable to infer the functional regions from social media check-in data and can benefit a wide range of fields, such as urban planning, public services, and location-based recommender systems.     

A geocomputational analysis of Twitter activity around different world cities

The penetration and use of social media services differs from city to city. This paper is aimed to provide a comparison of the use of Twitter between different cities of the world. We present a temporal analysis of activity on Twitter in 15 cities. Our study consists of two parts: First, we created temporal graphs of the activity in the 15 cities, through which hours of high and low activity could be identified. Second, we created heat map visualizations of the Twitter activities during the period of 19 September 2012–25 September 2013. The heat map visualizations make the periods of intense and sparse activity apparent and provide a snapshot of the activity during the whole year.     

Stability and mixing conditions for HIV/AIDS models with regional compartments

Compartmental models have been adapted to derive temporal epidemic forecasting systems for imitating the transfer of HIV infection between those with different behaviours or rates of risk activity. Alternatively, models with regional compartments, which forecast disease incidence in both space and time, have emerged as a response to the challenge of anticipating the pandemic pathways of this infection. This paper combines these frameworks to obtain properties for a multiregion model that also contains demographic compartments. Section 2 begins by showing how the stability conditions (starting thresholds) for a purely regional model are a special case of the existing conditions that have been derived for the general compartmental framework. Then, these results are extended to encompass a regions with compartments design. Section 3 presents an analysis of the population mixing relationships that are embedded in all these specifications. Here, the topics include the maintenance of contact symmetry, the representation of alternative partner selection behaviours, and the identification of core populations for the diffusion of HIV infection. The discussion considers how these theoretical findings might be applied to disease prevention. Received: 23 November 1998/Accepted: 4 August 1999