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1.
Abstract

When data on environmental attributes such as those of soil or groundwater are manipulated by logical cartographic modelling, the results are usually assumed to be exact. However, in reality the results will be in error because the values of input attributes cannot be determined exactly. This paper analyses how errors in such values propagate through Boolean and continuous modelling, involving the intersection of several maps. The error analysis is carried out using Monte Carlo methods on data interpolated by block kriging to a regular grid which yields predictions and prediction error standard deviations of attribute values for each pixel. The theory is illustrated by a case study concerning the selection of areas of medium textured, non-saline soil at an experimental farm in Alberta, Canada. The results suggest that Boolean methods of sieve mapping are much more prone to error propagation than the more robust continuous equivalents. More study of the effects of errors and of the choice of attribute classes and of class parameters on error propagation is recommended.  相似文献   

2.
ABSTRACT

Choropleth mapping provides a simple but effective visual presentation of geographical data. Traditional choropleth mapping methods assume that data to be displayed are certain. This may not be true for many real-world problems. For example, attributes generated based on surveys may contain sampling and non-sampling error, and results generated using statistical inferences often come with a certain level of uncertainty. In recent years, several studies have incorporated uncertain geographical attributes into choropleth mapping with a primary focus on identifying the most homogeneous classes. However, no studies have yet accounted for the possibility that an areal unit might be placed in a wrong class due to data uncertainty. This paper addresses this issue by proposing a robustness measure and incorporating it into the optimal design of choropleth maps. In particular, this study proposes a discretization method to solve the new optimization problem along with a novel theoretical bound to evaluate solution quality. The new approach is applied to map the American Community Survey data. Test results suggest a tradeoff between within-class homogeneity and robustness. The study provides an important perspective on addressing data uncertainty in choropleth map design and offers a new approach for spatial analysts and decision-makers to incorporate robustness into the mapmaking process.  相似文献   

3.
ABSTRACT

The increasing popularity of Location-Based Social Networks (LBSNs) and the semantic enrichment of mobility data in several contexts in the last years has led to the generation of large volumes of trajectory data. In contrast to GPS-based trajectories, LBSN and context-aware trajectories are more complex data, having several semantic textual dimensions besides space and time, which may reveal interesting mobility patterns. For instance, people may visit different places or perform different activities depending on the weather conditions. These new semantically rich data, known as multiple-aspect trajectories, pose new challenges in trajectory classification, which is the problem that we address in this paper. Existing methods for trajectory classification cannot deal with the complexity of heterogeneous data dimensions or the sequential aspect that characterizes movement. In this paper we propose MARC, an approach based on attribute embedding and Recurrent Neural Networks (RNNs) for classifying multiple-aspect trajectories, that tackles all trajectory properties: space, time, semantics, and sequence. We highlight that MARC exhibits good performance especially when trajectories are described by several textual/categorical attributes. Experiments performed over four publicly available datasets considering the Trajectory-User Linking (TUL) problem show that MARC outperformed all competitors, with respect to accuracy, precision, recall, and F1-score.  相似文献   

4.
ABSTRACT

This paper proposes a new classification method for spatial data by adjusting prior class probabilities according to local spatial patterns. First, the proposed method uses a classical statistical classifier to model training data. Second, the prior class probabilities are estimated according to the local spatial pattern and the classifier for each unseen object is adapted using the estimated prior probability. Finally, each unseen object is classified using its adapted classifier. Because the new method can be coupled with both generative and discriminant statistical classifiers, it performs generally more accurately than other methods for a variety of different spatial datasets. Experimental results show that this method has a lower prediction error than statistical classifiers that take no spatial information into account. Moreover, in the experiments, the new method also outperforms spatial auto-logistic regression and Markov random field-based methods when an appropriate estimate of local prior class distribution is used.  相似文献   

5.
Abstract

Mapping forest soils using conventional methods is time consuming and expensive. An expert system is described and applied to the mapping of five forest soil-landscape units formed on a single granitoid parent material. Three thematic maps were considered important in influencing the distribution of soils. The first showed the distribution of nine classes of native eucalypt forests, and the second and third were derived from a digital elevation model and represented slope gradient and a soil wetness index combined with topographical position. These layers were input to a raster based geographical information system (GIS) and then geometrically co-registered to a regular 30 m grid. From a knowledge of soil distributions, the relationships between the soil-landscape units and the three data layers were quantified by an experienced soil scientist and used as rules in a rule based expert system. The thematic layers accessed from the GIS provided data for the expert system to infer the forest soil-landscape unit most likely to occur at any given pixel. The soil-landscape map output by the expert system compared favourably with a conventional soil-landscape map generated using interpretation of aerial photographs.  相似文献   

6.

We recommend use of the widely available computer package GLIM to analyze relationships involving categorical, or qualitative, variables. The package is flexible and fairly simple to use and readily produces data plots as well as information on parameter estimates and residual values. In order to explain how to fit log-linear and logistic-linear models using GLIM, we give two examples based on sample survey data from grocery shoppers in Oxford, England. In the first, we examine relationships between a categorical response to an attitude statement and categorical explanatory variables using the log-linear model and, in the second, relationships between use and a mixture of continuous and categorical explanatory variables using the logistic-linear (logit) model.  相似文献   

7.
Abstract

Progress in technical database management systems offers alternative strategies for the design and implementation of databases for geographical information systems. Desirable extensions in the user data types and database management are reviewed. A prototype geographical database tool-kit, SIRO-DBMS, which provides some spatial data types and spatial access methods as external attachments to a kernel relational database management system, is described. An ability to fragment a large set of entities into several relations while retaining the ability to search the full set as a logical unit is provided. Implementation of the geometric data types is based on mapping the types of data into a set of attributes of the atomic types supported by the kernel and specifying the relational designs for the set of atomic attributes.  相似文献   

8.
Abstract

An error model for spatial databases is defined here as a stochastic process capable of generating a population of distorted versions of the same pattern of geographical variation. The differences between members of the population represent the uncertainties present in raw or interpreted data, or introduced during processing. Defined in this way, an error model can provide estimates of the uncertainty associated with the products of processing in geographical information systems. A new error model is defined in this paper for categorical data. Its application to soil and land cover maps is discussed in two examples: the measurement of area and the measurement of overlay. Specific details of implementation and use are reviewed. The model provides a powerful basis for visualizing error in area class maps, and for measuring the effects of its propagation through processes of geographical information systems.  相似文献   

9.
Abstract

This paper presents a methodology which provides a practicable solution to a specific class of areal interpolation problems. The method allows for the transformation of the areal basis of land use data from a set of reporting units to a set of land capability classes within each of which land use mix is assumed to be homogeneous. Three alternative procedures, based on least squares criteria, are suggested for estimation of the land use mix in each land class. Although these are shown to produce similar and broadly plausible results in an empirical application, choice of procedure may be guided by consideration of the particular application and computational ease.  相似文献   

10.
The need to integrate large quantities of digital geoscience information to classify locations as mineral deposits or nondeposits has been met by the weights-of-evidence method in many situations. Widespread selection of this method may be more the result of its ease of use and interpretation rather than comparisons with alternative methods. A comparison of the weights-of-evidence method to probabilistic neural networks is performed here with data from Chisel Lake-Andeson Lake, Manitoba, Canada. Each method is designed to estimate the probability of belonging to learned classes where the estimated probabilities are used to classify the unknowns. Using these data, significantly lower classification error rates were observed for the neural network, not only when test and training data were the same (0.02 versus 23%), but also when validation data, not used in any training, were used to test the efficiency of classification (0.7 versus 17%). Despite these data containing too few deposits, these tests of this set of data demonstrate the neural network's ability at making unbiased probability estimates and lower error rates when measured by number of polygons or by the area of land misclassified. For both methods, independent validation tests are required to ensure that estimates are representative of real-world results. Results from the weights-of-evidence method demonstrate a strong bias where most errors are barren areas misclassified as deposits. The weights-of-evidence method is based on Bayes rule, which requires independent variables in order to make unbiased estimates. The chi-square test for independence indicates no significant correlations among the variables in the Chisel Lake–Andeson Lake data. However, the expected number of deposits test clearly demonstrates that these data violate the independence assumption. Other, independent simulations with three variables show that using variables with correlations of 1.0 can double the expected number of deposits as can correlations of –1.0. Studies done in the 1970s on methods that use Bayes rule show that moderate correlations among attributes seriously affect estimates and even small correlations lead to increases in misclassifications. Adverse effects have been observed with small to moderate correlations when only six to eight variables were used. Consistent evidence of upward biased probability estimates from multivariate methods founded on Bayes rule must be of considerable concern to institutions and governmental agencies where unbiased estimates are required. In addition to increasing the misclassification rate, biased probability estimates make classification into deposit and nondeposit classes an arbitrary subjective decision. The probabilistic neural network has no problem dealing with correlated variables—its performance depends strongly on having a thoroughly representative training set. Probabilistic neural networks or logistic regression should receive serious consideration where unbiased estimates are required. The weights-of-evidence method would serve to estimate thresholds between anomalies and background and for exploratory data analysis.  相似文献   

11.

This paper analyzes the migration of Puerto Rican-born women from the United States to Puerto Rico using longitudinal data. We hypothesize that sojourn length in the United States is a function of both structural (macro-level economic and cultural factors) and behavioral (micro-level life-cycle experiences and personal attributes) variables. We test these hypotheses by estimating a proportional hazards model. The parameter estimates of this model indicate that sojourn length in the United States, and thus the decision to return to Puerto Rico, is a function of wage trends and community characteristics on the mainland plus a number of individual attributes that include education, marriage, and childbirth.  相似文献   

12.
Abstract

In state-of-the-art GIS, geographical features are represented as geometric objects with associated topological relations and classification attributes. Semantic relations and intrinsic interrelations of the features themselves are generally neglected. In this paper, a feature-based model that enhances the representation of geographical features is described. Features, as the fundamental depiction of geographical phenomena, encompass both real world entities and digital representation. A feature-based object incorporates both topological relations among geometric elements and non-topological (semantic) relations among features. The development of an object-oriented prototype feature-based GIS that supports relations between feature attributes and feature classes is described. Object-oriented concepts such as class inheritance and polymorphism facilitate the development of feature-based GTS.  相似文献   

13.

This paper considers the appropriateness of typologies of urban retailing areas based on multivariate functional data. The findings of a case study in which statistical and graphical methods were employed within the framework of multivariate ordination are presented. It is shown that the functional attributes of retail areas are closely associated with their location, accessibility, quality, morphology, size, period of development, and socioeconomic character.  相似文献   

14.
Abstract

Error and uncertainty in spatial databases have gained considerable attention in recent years. The concern is that, as in other computer applications and, indeed, all analyses, poor quality input data will yield even worse output. Various methods for analysis of uncertainty have been developed, but none has been shown to be directly applicable to an actual geographical information system application in the area of natural resources. In spatial data on natural resources in general, and in soils data in particular, a major cause of error is the inclusion of unmapped units within areas delineated on the map as uniform. In this paper, two alternative algorithms for simulating inclusions in categorical natural resource maps are detailed. Their usefulness is shown by a simplified Monte Carlo testing to evaluate the accuracy of agricultural land valuation using land use and the soil information. Using two test areas it is possible to show that errors of as much as 6 per cent may result in the process of land valuation, with simulated valuations both above and below the actual values. Thus, although an actual monetary cost of the error term is estimated here, it is not found to be large.  相似文献   

15.
Abstract

The lack of a coherent theory underpinning geographical databases is a serious obstacle to research efforts in this field. This article attempts to construct part of such a theory, namely the formalization of the underlying object mode for geographical data whose spatial references are embedded in the plane. Questions of approximation and error analysis, while exposed and briefly discussed here, do not form a major part of the discussion. This work extends earlier work by giving a detailed construction of the classes and operations for spatial objects embedded in the plane. It goes on to provide an explicit link between this object model and its representation in computationally meaningful terms using classes of simplicial complexes and operations acting upon these classes.  相似文献   

16.
ABSTRACT

We study the problem of landuse characterization at the urban-object level using deep learning algorithms. Traditionally, this task is performed by surveys or manual photo interpretation, which are expensive and difficult to update regularly. We seek to characterize usages at the single object level and to differentiate classes such as educational institutes, hospitals and religious places by visual cues contained in side-view pictures from Google Street View (GSV). These pictures provide geo-referenced information not only about the material composition of the objects but also about their actual usage, which otherwise is difficult to capture using other classical sources of data such as aerial imagery. Since the GSV database is regularly updated, this allows to consequently update the landuse maps, at lower costs than those of authoritative surveys. Because every urban-object is imaged from a number of viewpoints with street-level pictures, we propose a deep-learning based architecture that accepts arbitrary number of GSV pictures to predict the fine-grained landuse classes at the object level. These classes are taken from OpenStreetMap. A quantitative evaluation of the area of Île-de-France, France shows that our model outperforms other deep learning-based methods, making it a suitable alternative to manual landuse characterization.  相似文献   

17.
A review of quantitative methods for movement data   总被引：1，自引：0，他引：1  
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18.
This paper outlines a method for explicitly classifying landscape using 4 km2 grid cells, a set of attributes derived from topographic and geologic maps and an agglomerative numerical taxonomic procedure which can accommodate mixed data. The method was used to classify the landscapes of the Hunter Valley, NSW (22 000 km2) and the landscape classes generated are briefly described. These classes are compared with the land systems of the Hunter which were developed by traditional integrated survey procedure. It is concluded that the method could provide a more satisfactory starting-point for integrated land survey than air photo pattern analysis.  相似文献   

19.
《The Journal of geography》2012,111(5):184-193
Abstract

Numerous studies have shown both anecdotal and formal evidence of the benefits students obtain from doing writing activities in classes. Little formal discussion exists about how student writing in geography classes professionally affects faculty. In this article, focus shifts from student-derived benefits of writing in classes to faculty challenges and rewards for implementing writing in their classes. Based on the experience of participating in a Writing Across the Curriculum (WAC) Fellows program, the authors discuss how faculty overcame challenges and reaped the benefits of student writing in their teaching and scholarly pursuits.  相似文献   

20.
Abstract

The advantages of handling population data in GIS as a raster surface rather than as zonal attributes are discussed, and a technique for creating such surfaces briefly reviewed. Existing models created by this method using the 1991 UK Census are then compared with equivalent zonal data and some weaknesses identified, in particular the poor association between enumeration districts and 200 m grid cells. A refined version of the surface generation technique is presented in which population totals are constrained within zone boundaries, while residential geography is retained with considerable success.  相似文献   

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