Prediction-based parking allocation framework in urban environments

ABSTRACT

Finding a parking space is usually challenging in urban areas. The literature shows that 30% of traffic congestion is caused by searching for parking spaces, which results in unnecessary energy consumption and environmental pollution. With the development of sensor technologies, smart parking guidance systems provide users with a variety of real-time parking space information. However, users cannot know whether the target parking space remains available upon arrival. Moreover, parking resources may be under competition when multiple users target the same open parking space. In this research, we develop a new framework named prediction-based parking allocation (PPA) that provides smart parking services to users. In PPA, we first construct a prediction model of parking occupancy and predict the subsequent parking availabilities. Then, we design a matching-based allocation strategy to assign users to selected parking spaces. To the best of our knowledge, this is the first study that combines occupancy prediction and space allocation simultaneously to address smart parking issues. Finally, we collect a real dataset from the SFPark on-street parking system for performance evaluation. According to experimental results, PPA can effectively increase the parking success rate and reduce costs, fuel consumption, and carbon emissions.     

Map Reading and Abuse

Abstract

The various advantages and limitations of geographical mapping known to map users can be functionally related to the few basic principles which constitute the very nature of the cartographic technique. Numerous map interpretation errors may be traced in large part to a common failure on the part of map users to grasp and effectively deal with these same mapping principles. Since it is possible to identify situations in environmental decision making and planning where the misuse of maps, or outright map reading abuses, could lead to adverse human and environmental impact, the problem is indeed serious. Strategies for minimizing the misuse or abuse of geographical maps appear to rely most heavily on a thorough understanding of basic mapping principles.     

An agenda for democratising cartographic visualisation

Cartographic visualisation tools aid exploration, but they are designed for, and used exclusively by, experts. A democratised visualisation tool will include second-generation users, and these non-specialists might also want to use the available computer technology to visualise their geographical data. In this paper, we argue that democratised GIS should have a functionality similar to visualisation tools and we forward our opinion on how these can be developed in order to do so. Our emphasis is on interactivity regarding representation methods and on elaborating principles for implementing map type selection in interfaces for democratised GISs.     

Competitive Learning Approach to GIS Based Land Use Suitability Analysis

This paper uses the expected utility under risk hypothesis to develop a new approach to GIS modeling for land use suitability analysis with competitive learning algorithms (CLG-LUSA). It uses Kohonen’s Self Organized Maps (SOM) and Linear Vector Quantization (LVQ) among other tools to create comprehensive ordering of high number of options. The model uses decision makers preferred locations and environmental data to construct a manifold of the decision’s attribute space. Then, decision and uncertainty maps are derived from this manifold. An application example is provided using the selection of suitable environments for coconut development in a municipality of Cuba. CLG-LUSA model was able to provide accurate visual feedback of key aspects of the decision process, making the methodology suitable for personal or group decision making.     

Points of interest recommendation from GPS trajectories

Recently, points of interest (POIs) recommendation has evolved into a hot research topic with real-world applications. In this paper, we propose a novel semantics-enhanced density-based clustering algorithm SEM-DTBJ-Cluster, to extract semantic POIs from GPS trajectories. We then take into account three different factors (popularity, temporal and geographical features) that can influence the recommendation score of a POI. We characterize the impacts caused by popularity, temporal and geographical information, by using different scoring functions based on three developed recommendation models. Finally, we combine the three scoring functions together and obtain a unified framework PTG-Recommend for recommending candidate POIs for a mobile user. To the best of our knowledge, this work is the first that considers popularity, temporal and geographical information together. Experimental results on two real-world data sets strongly demonstrate that our framework is robust and effective, and outperforms the baseline recommendation methods in terms of precision and recall.     

An Australian geographical information and modelling system for natural area management

A generic geographical information and modelling system (GIMS) has been developed and implemented for 6 million hectares of fire-prone rural land in eastern Australia. Viewed initially as a land management decision support system rather than as a geographical information system (GIS) per se, GIMS provides extensive capabilities for estimating and recording patterns of vegetation and fuel dynamics, analysing the behaviour of fire and its environmental effects, and reviewing strategies for fire control and related issues. The software operates on personal computers via linkage to an indexed, direct access, grid data base. The design, development, implementation, evolution and use of GIMS for land management decision support are described, and tests of the system's ability to predict real-time fire behaviour are provided. The relationship between commercial GIS packages and locally-developed dynamic modelling GIS programs is discussed.     

Calibrating cellular automata based on landscape metrics by using genetic algorithms

Landscape metrics have been widely used to characterize geographical patterns which are important for many geographical and ecological analyses. Cellular automata (CA) are attractive for simulating settlement development, landscape evolution, urban dynamics, and land-use changes. Although various methods have been developed to calibrate CA, landscape metrics have not been explicitly used to ensure the simulated pattern best fitted to the actual one. This article presents a pattern-calibrated method which is based on a number of landscape metrics for implementing CA by using genetic algorithms (GAs). A Pattern-calibrated GA–CA is proposed by incorporating percentage of landscape (PLAND), patch metric (LPI), and landscape division (D) into the fitness function of GA. The sensitivity analysis can allow the users to explore various combinations of weights and examine their effects. The comparison between Logistic- CA, Cell-calibrated GA–CA, and Pattern-calibrated GA–CA indicates that the last method can yield the best results for calibrating CA, according to both the training and validation data. For example, Logistic-CA has the average simulation error of 27.7%, but Pattern-calibrated GA–CA (the proposed method) can reduce this error to only 7.2% by using the training data set in 2003. The validation is further carried out by using new validation data in 2008 and additional landscape metrics (e.g., Landscape shape index, edge density, and aggregation index) which have not been incorporated for calibrating CA models. The comparison shows that this pattern-calibrated CA has better performance than the other two conventional models.     

Editorial

Abstract

The analysis of geographical information is compared with other production processes in which a user can only accept an end-product if it meets certain quality requirements. Whereas users are responsible for defining the levels of quality they need to use the results of the analyses of geographical information systems in their work, database managers, experts and modellers could greatly assist users to achieve the quality of results they seek by formalizing information on: (1) data collection, level of resolution and quality; (2) the use of the basic analytical functions of the geographical information system; and (3) the data requirements, sensitivity and error propagation in models. These meta-data could be incorporated in a knowledge base alongside the geographical information system where, together with procedures for on-line error propagation, a user could be advised on the best way to achieve a desired aim. If the analysis showed that the original constellation of data, methods and models could not achieve the aim with the desired quality, the intelligent geographical information system would present a range of alternative strategies—better methods, more data, different data, better models, better model calibration, or better spatial resolution—and their costs by which the user's aims could reasonably be achieved.     

Applying Four Different Risk Models in Local Ore Selection

Given the uncertainty in grade at a mine location, a financially risk-averse decision-maker may prefer to incorporate this uncertainty into the ore selection process. A FORTRAN program risksel is presented to calculate local risk-adjusted optimal ore selections using a negative exponential utility function and three dominance models: mean-variance, mean-downside risk, and stochastic dominance. All four methods are demonstrated in a grade control environment. In the case study, optimal selections range with the magnitude of financial risk that a decision-maker is prepared to accept. Except for the stochastic dominance method, the risk models reassign material from higher cost to lower cost processing options as the aversion to financial risk increases. The stochastic dominance model usually was unable to determine the optimal local selection.     

Infometric and statistical diagnostics to provide artificially-intelligent support for spatial analysis: the example of interpolation

The wider uptake of GIS tools by many application areas outside GIScience means that many newer users of GIS will have high-level knowledge of the wider task, and low-level knowledge of specific system commands as given in reference manuals. However, these newer users may not have the intermediate knowledge that experts in GI science have gained from working with GI systems over several years. Such intermediate knowledge includes an understanding of the assumptions implied by the use of certain functions, and an appreciation of how to combine functions appropriately to create a workflow that suits both the data and overall goals of the geographical analysis task.

Focusing on the common but non-trivial task of interpolating spatial data, this paper considers how to help users gain the necessary knowledge to complete their task and minimise the possibility of methodological error. We observe that both infometric (or cognitive) knowledge and statistical knowledge are usually required to find a solution that jointly and efficiently meets the requirements of a particular user and data set. Using the class of interpolation methods as an example, we outline an approach that combines knowledge from multiple sources and argue the case for designing a prototype ‘intelligent’ module that can sit between a user and a given GIS.

The knowledge needed to assist with the task of interpolation is constructed as a network of rules, structured as a binary decision tree, that assist the user in selecting an appropriate method according to task-related knowledge (or ‘purpose’) and the characteristics of the data sets. The decision tree triggers exploratory diagnostics that are run on the data sets when a rule requires to be evaluated. Following evaluation of the rules, the user is advised which interpolation method might be and should not be considered for the data set. Any parameters required to interpolate the particular data set (e.g. a distance decay parameter for Inverse Distance Weighting) are also supplied through subsequent optimisation and model selection routines. The rationale of the decision process may be examined, so the ‘intelligent interpolator’ also acts as a learning tool.     

Activity patterns,socioeconomic status and urban spatial structure: what can social media data tell us?

ABSTRACT

Individual activity patterns are influenced by a wide variety of factors. The more important ones include socioeconomic status (SES) and urban spatial structure. While most previous studies relied heavily on the expensive travel-diary type data, the feasibility of using social media data to support activity pattern analysis has not been evaluated. Despite the various appealing aspects of social media data, including low acquisition cost and relatively wide geographical and international coverage, these data also have many limitations, including the lack of background information of users, such as home locations and SES. A major objective of this study is to explore the extent that Twitter data can be used to support activity pattern analysis. We introduce an approach to determine users’ home and work locations in order to examine the activity patterns of individuals. To infer the SES of individuals, we incorporate the American Community Survey (ACS) data. Using Twitter data for Washington, DC, we analyzed the activity patterns of Twitter users with different SESs. The study clearly demonstrates that while SES is highly important, the urban spatial structure, particularly where jobs are mainly found and the geographical layout of the region, plays a critical role in affecting the variation in activity patterns between users from different communities.     

全球气候变暖的影响问题辨析

气候变暖对全球各领域及地区可能造成什么样的影响是气候变化相关研究中受关注的一个主题.本研究对全球观测数据和大量过去气候变化研究成果进行分析,针对几个关键问题就全球变暖对人类及其他物种造成不良后果的观点进行了辨析,包括气候模型可能存在的缺陷,冰川、海冰和海平面以及降雨量和水资源变化的不确定性,极端天气事件增强的可能性以及生物的适应能力,指出不宜简单地将所有灾害事件都直接归因于全球变暖,应该在全面科学评估的基础上趋利避害,从而达到更持续发展.     

Prediction of land subsidence caused by groundwater exploitation in Hanoi, Vietnam, using multifactorial correlation analysis

Multifactorial correlation analysis is a new method used to predict the land subsidence caused by groundwater exploitation. This article introduces and applies the method to establish the function of t...     

A framework for uncertainty assessment in simulation models

In this article, we introduce a conceptual framework for systematic identification and assessment of sources of uncertainty in simulation models. This concept builds on a novel typology of uncertainty in model validation and extends the GIScience research focus on uncertainty in spatial data to uncertainty in simulation modelling. Such a concept helps a modeller to interpret and handle uncertainty in order to efficiently optimise a model and better understand simulation results.

To illustrate our approach, we apply the proposed framework for uncertainty assessment to the TREE LIne Model (TREELIM), an individual-based model that simulates forest succession at the alpine tree line. Using this example, uncertainty is identified in the modelling workflow during conceptualisation, formalisation, parameterisation, analysis and validation. With help of a set of indicators we quantify the emerging uncertainties and assess the overall model uncertainty as a function of all occurring sources of uncertainty.

An understanding of the sources of uncertainty in an ecological model proves beneficial for: (1) developing a structurally valid model in a systematic way; (2) deciding if further refinement of the conceptual model is beneficial for the modelling purpose; and (3) interpreting the overall model uncertainty by understanding its sources. Our approach results in a guideline for assessing uncertainty in the validation of simulation models in a feasible and defensible way, and thus functions as a toolbox for modellers. We consider this work as a contribution towards a general concept of uncertainty in spatially explicit simulation models.     

Sample surveying to estimate the mean of a heterogeneous surface: reducing the error variance through zoning

One of the major sources of uncertainty associated with geographical data in GIS arises when they are the outcome of a sampling process. It is well known that when sampling from a spatially autocorrelated homogeneous surface, stratification reduces the error variance of the estimator of the population mean. In this study, we evaluate the efficiency of different spatial sampling strategies when the surface is not homogeneous. When the surface is first-order heterogeneous (the mean of the surface varies across the map), we examine the effects of stratifying it into first-order homogeneous zones prior to the usual stratification for a systematic or stratified random sample. We investigate the effect of this form of spatial heterogeneity on the performance of different methods for estimating the population mean and its error variance. We do so by distinguishing between the real surface to be surveyed (?), the sampling frame (?) including the choice of zoning, and the statistical estimators (Ψ). The study shows that zoning improves estimator efficiency when sampling a heterogeneous surface. Systematic comparison provides rules of thumb for choice of sample design, sample statistics and uncertainty estimation, based on considering different spatial heterogeneities on real surfaces.     

Taxonomy for surveying the use and value of geographical information!

Abstract

Understanding the use and value of geographical information in decision making has been identified as crucial to the further development of geographical information systems and related spatial analysis techniques. Research into the use of geographical information will be through surveys and case studies. A taxonomy for investigating the use of geographical information and its associated value is needed to structure the surveys and case studies. This paper presents an initial attempt at such a taxonomy, intended to support continuing research into the use and value of geographical information. The survey methodology to be supported will be mostly telephone and/or mail questionnaires, and this has influenced the structure of the taxonomy. The goal of this work is to create a taxonomy suitable for use in surveys that will enhance the understanding of the use and value of geographical information in decision making.     

Testing local spatial autocorrelation using k-order neighbours

The analysis of local spatial autocorrelation for spatial attributes has been an important concern in geographical inquiry. In this paper, we propose a concept and algorithm of k-order neighbours based on Delaunay's triangulated irregular networks and redefine Getis and Ord's (1992) local spatial autocorrelation statistic as G_i(k) with weight coefficient w_ij(k) based on k-order neighbours for the study of local patterns in spatial attributes. To test the validity of these statistics, an experiment is performed using spatial data of the elderly population in Ichikawa City, Chiba Prefecture, Japan. The difference between the weight coefficients of the k-order neighbours and distance parameter to measure the spatial proximity of districts located in the city centre and near the city limits is found by Monte-Carlo simulation.     

Identifying Common Decision Problem Elements for the Management of Emerging Fungal Diseases of Wildlife

Emerging infectious diseases (EIDs) of wildlife have characteristics that make them difficult to manage, leading to reactive and often ineffective management strategies. Currently, two fungal pathogens, Pseudogymnoascus destructans (Pd) and Batrachochytrium salamandrivorans (Bsal), are causing declines in novel host species. To improve the application of management strategies addressing the risk of these pathogens to North American wildlife, we queried wildlife managers about their concerns regarding managing populations of bats and amphibians potentially impacted by Pd and Bsal. Using these responses, we identified aspects of each decision problem that were shared across pathogens, regions and agencies – and found similarities in decision-problem elements for disease management. Reframing management problems as decisions can enable managers to identify similarities across EIDs, i.e. uncertainties within management actions, and improve reactive responses if proactive management is not possible. Such an approach recognizes context-specific constraints and identifies relevant uncertainties that must be reduced in developing a response.