Spatial Information Theory Research Articles

Evaluation Method of Equalization of Basic Medical Services from the Spatial Perspective: The Case of Xinjiang, China

Protecting residents’ health and improving equality are important goals of the United Nations Sustainable Development Goals. The recent outbreak of COVID-19 has placed a heavy burden on the medical systems of many countries and been disastrous for the low-income population of the world, which has further increased economic, health, and lifelong inequality in society. One way to improve the population’s health is to equalize basic medical services. A scientific evaluation of the status quo or the equalization of basic medical services (EBMS) is the basic prerequisite and an important basis for realizing the equitable allocation of medical resources. Traditional evaluation methods ignore the spatial characteristics of medical services, mostly using the indicator of equal weight evaluation, which restricts the objectivity of the evaluation results. Given this, this research proposes a set of EBMS evaluation methods from a spatial perspective and takes the Xinjiang Uygur Autonomous Region of China (Xinjiang) as an example for studying the status quo of EBMS. This study puts forward a set of EBMS evaluation methods from a geospatial perspective and makes full use of spatial analysis and information theory techniques to construct a two-level evaluation indicator that takes into account the spatial characteristics of EBMS. The entropy weight method and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method have been used to reveal the current status quo of EBMS in Xinjiang to objectively reflect the differences in EBMS. When using the entropy and TOPSIS methods, the evaluation is always based on the data so that the results can more objectively reveal the medical resources available to the residents. Therefore, the government can realize a reasonable allocation of medical resources.

Using spatial indices to measure dynamic racial residential segregation in Gauteng province (South Africa)

ABSTRACT Apartheid laws resulted in racial residential segregation that became entrenched into the urban morphology of South Africa. When apar-theid ended in the 1990’s, the new South African democratic government was resolved to bring about social and spatial justice, address inequalities and promote social cohesion. To determine progress towards racial residential integration, aspatial indices of segregation are widely employed despite their shortcomings and limi-tations. This study employs two spatial indices of segregation: the spatial information theory index and the spatial exposure/isolation index in order to measure and quantify the levels of racial residential segregation that individuals living in Gauteng province (South Africa) would experience on average within radii of 500m, 4km and 8km of their respective residential locations. The analysis is based on the 1996, 2001 and 2011 Census data. The results indicate that the levels of racial residential segregation have steadily declined between 1996 and 2011. The levels of exposure of Whites to Black Africans continue to increase while the levels of exposure of Black Africans to Whites have remained unchanged over the same period. These observations are true for the different geographic scales (i.e. within radii of 500m, 4km and 8km) of analysis considered in this study.

Quantitative imaging: Erring patterns in manual delineation of PET-imaged lung lesions

Background and purposeUncertainty and variability in manual contouring of PET-imaged tumor targets are well recognized; however, the error patterns associated with it were little known and rarely investigated. The present study is aimed to quantitatively assess the erring patterns inherent to manual delineation of PET-imaged lung lesions in a setting with complete ground truth. Materials and methodsImages being used for assessment consisted of 26 synthetic PET datasets created by using the anthropomorphic Zubal phantom in conjunction with the Monte Carlo based SimSET computational package. Each dataset included one PET-positive lesion differing in shape, dimension, uptake heterogeneity, and anatomical location inside the lung. Target contours were provided by 10 raters and the contour accuracy was evaluated using 12 metrics from five categories – spatial overlap, pair counting, information theory, distance, and volume. ResultsIn terms of spatial overlap, manual contouring results intersect substantially with the ground truth whereas tend to oversegment the lesions. Shapes of the segmented tumor volumes are in general geometrically consistent with the ground truth but lack sensitivity in characterizing topographical details. No complete consensus could be achieved between manual contours and the ground truth for any of the given lesions being examined when assessing using pair counting- and informatics-based metrics thus indicating an intrinsic stochastic component of manual contouring. Evaluation based on metrics related to distance and volume demonstrated that it is at the borderline areas between the lesions and the normal tissues where the majority part of manual delineation errors occurred and the extent of volume being identified false positively as cancerous by the raters is appreciable. ConclusionQuantification of segmentation errors associated with expert manual contouring of PET positive lesion in the lung reveals general patterns in what otherwise might be thought of as randomness. Findings from the current study may allow for the formation of new hypotheses towards improving the accuracy and precision of manual delineation of PET positive tumor targets in the lung.

Spatial information theory of sensor array and its application in performance evaluation

The direction of arrival (DOA) and amplitude-phase (AP) of source signal can be estimated through array signal processing, while the process of information acquisition has not been described by information theory yet. In this study, the authors propose a novel spatial information theory framework to describe the DOA and AP estimation process. Firstly, they give a sensor array system model, where the spatial information is defined mathematically. Furthermore, in a single source scenario, the derivations of theoretical expression and an asymptotic upper bound for DOA information are presented. It also proves that the AP information accords with Shannon channel capacity when the source follows complex-Gaussian distribution. In addition, when in an uncorrelated multi-sources scenario, the AP information is the sum of that of each source for Gaussian distributed sources. Finally, they propose a definition of entropy error to evaluate the information acquisition capability of a sensor array. Numerical results verify that the entropy error is consistent with mean square error and approaches Cramer–Rao bound in high signal-to-noise ratio. It also verifies the correctness of theoretical derivation and shows that the spatial information can be employed as an evaluation index to quantitatively analyse the system performance which has significant guidance.

Improvements to Information Entropy for Raster Spatial Data: A Thermodynamic-based Evaluation

Abstract. Spatial information is fundamentally important to our daily life. It has been estimated by many scholars that almost 80 percent or more of all information in this world are spatially referenced and can be regarded as spatial information. Given such importance, a discipline called spatial information theory has been formed since the late 20th century. In addition, international conferences on spatial information have been frequently held. For example, COSIT (Conference on Spatial Information Theory) was established in 1993 and are held every two years all over the world.In spatial information theory, one fundamental question is how to measure the amount of information (i.e., information content) of a spatial dataset. A widely used method is to employ entropy, which is proposed by the American mathematician Claude Shannon in 1948 and usually referred to as Shannon entropy or information entropy. This information entropy was originally designed to measure the statistical information content of a telegraph message. However, a spatial dataset such as a map or a remote sensing image contains not only statistical information but also spatial information, which cannot be measured by using the information entropy.As a consequence, considerable efforts have been made to improve the information entropy for spatial datasets in either a vector format of a raster format. There are two basic lines of thought. The first is to improve the information entropy by defining how to calculate its probability parameters, and the other is to introduce new parameters into the formula of the information entropy. The former results in a number of improved information entropies, while the latter leads to a series of variants of the information entropy. Both seem to be capable of distinguishing different spatial datasets, but there is a lack of comprehensive evaluation of their performance in measuring spatial information.This study first presents a state-of-the-art review of the improvements to the information entropy for the information content of spatial datasets in a raster format (i.e., raster spatial data, such as a grey image and a digital elevation model). Then, it presents a comprehensive evaluation of the resultant measures (either improved information entropies or variants of the information entropy) according to the Second Law of Thermodynamics. A set of evaluation criteria were proposed, as well as corresponding measures. All resultant measures were ranked accordingly.The results reported in this study should be useful for entropic spatial data analysis. For example, in image fusion, a crucial question is how to evaluate the performance of a fusion algorithm. This evaluation is usually achieved by using the information entropy to measure the increase in the information content during the fusion. It can now be performed by the best-improved information entropy reported in this study.

A spatial analytical approach towards understanding racial residential segregation in Gauteng province (South Africa)

Abstract. The introduction of apartheid in 1948 resulted in racial residential segregation that has influenced the spatial distribution of the population in South Africa. Apartheid laws, which were mainly based on race, brought about the exclusion of the non-white population from urban areas and the mainstream economy of South Africa, as well as the benefits that come with it. In the early 1990’s, apartheid was abolished and the South African government set to bring about social and spatial justice, address inequalities and promote social cohesion. This also meant doing away with racial residential segregation that had been entrenched into the urban morphology of the country. Despite this, in the post-apartheid era, racial-residential segregation still exists (Parry and Van Eeden 2015).Figure 1 shows the density (kernel) distribution of each of the four population groups in Gauteng in 2011: Indian/Asian (IA), white (W), black African (BA) and coloured (C). It is a reflection of the legacy of apartheid town planning which isolated non-whites to the peripheral areas of urban economic centers. Densely populated areas are coloured in red while less populated areas are coloured in green. As it can be seen in the map (Figure 1), the white population group densely occupies areas close to the business centers of the province in places such as Pretoria and northern parts of Johannesburg, while non-whites densely occupy peripheral areas in former townships designated to non-whites, such as Soweto (black African), Mabopane (black African) and Lenasia (Indian/Asians). This observed pattern is more pronounced for black Africans.To study the pattern of racial residential segregation in South Africa, non-spatial indices of segregation are widely employed despite their shortcomings. Parry and Van Eeden (2015) are among the few authors who have acknowledged the importance of employing spatial indices of segregation, even though they did not explicitly use one due to the lack of ready to use GIS software. Massey and Denton (1988) define residential segregation as “the degree to which two or more groups live separately from one another, in different parts of the urban environment”, i.e. racial residential segregation manifests itself across space. Hence, in order to assess the extent to which the levels of racial residential segregation have subsided, adequate empirical studies that employ spatial segregation indices on socio-economic data are necessary. The purpose of this research is to study the pattern of racial residential segregation by employing a spatial index of segregation namely the ‘spatial information theory index (H)’ for Gauteng province, the economic hub and most populated province of South Africa.Some of the shortcomings of existing non-spatial indices of segregation (and also of some of the spatial ones) include the Modifiable Areal Unit Problem (MAUP) which refers to how such indices are sensitive to the size of the areal units (i.e. administrative or political boundaries) of analysis that might be arbitrarily chosen or might not accurately reflect the actual racial composition of the local neighbourhoods. This introduces possibilities of obtaining inaccurate measures of racial residential segregation and also being unable to compare the results at various scales of analysis (Reardon et al. 2004; Weir-Smith 2016). One of the major challenges that impedes the use of spatial segregation indices is the lack of ready to use software that has implemented spatial segregation indices which have attempted to address the MAUP. To address such a challenge, Hong et al. 2014 implemented a series of spatial equivalences of existing segregation measures in R under the package ‘seg’ based on Reardon et al. (2004)’s formulation of spatial segregation indices. Reardon et al. (2004) emphasise the computation of spatial indices of segregation based on the racial composition of the population as reflected by their immediate local environment instead of relying on arbitrary or fixed administrative boundaries. The ‘spatial information theory index (H)’ as implemented by Reardon et al. (2004) is experimented in this study.This study explores existing literature related to racial residential segregation in order to further complement and supplement existing theories on segregation in South Africa by adopting a spatial analytical approach. The authors take advantage of the R implementation of spatial measures of segregation (Hong et al. 2014), namely the spatial information theory index (H), to study the patterns of residential segregation in Gauteng province (South Africa).

A novel segmentation method for uneven lighting image with noise injection based on non-local spatial information and intuitionistic fuzzy entropy

Local thresholding methods for uneven lighting image segmentation always have the limitations that they are very sensitive to noise injection and that the performance relies largely upon the choice of the initial window size. This paper proposes a novel algorithm for segmenting uneven lighting images with strong noise injection based on non-local spatial information and intuitionistic fuzzy theory. We regard an image as a gray wave in three-dimensional space, which is composed of many peaks and troughs, and these peaks and troughs can divide the image into many local sub-regions in different directions. Our algorithm computes the relative characteristic of each pixel located in the corresponding sub-region based on fuzzy membership function and uses it to replace its absolute characteristic (its gray level) to reduce the influence of uneven light on image segmentation. At the same time, the non-local adaptive spatial constraints of pixels are introduced to avoid noise interference with the search of local sub-regions and the computation of local characteristics. Moreover, edge information is also taken into account to avoid false peak and trough labeling. Finally, a global method based on intuitionistic fuzzy entropy is employed on the wave transformation image to obtain the segmented result. Experiments on several test images show that the proposed method has excellent capability of decreasing the influence of uneven illumination on images and noise injection and behaves more robustly than several classical global and local thresholding methods.

Irregularly-shaped school attendance zones and racial integration

This paper investigates how much the geographic shapes of school attendance zones within urban school districts are associated with levels of attendance zone racial segregation (while holding constant levels of residential segregation). Based on an analysis of 304 school districts, findings show that more irregularly-shaped school attendance zones are correlated with lower levels of racial segregation in attendance zones after accounting for residential segregation. In fact, not one school district contains both highly irregularly-shaped attendance zones and unusually high levels of attendance zone racial segregation—although there are several school districts with irregularly-shaped zones and unusually high levels of racial integration. These findings undermine recent claims that irregularly-shaped attendance zones generally serve to segregate students by race. In addition to these empirical findings, this paper introduces a variation of the spatial information theory segregation index H˜ that is useful for predicting segregation in school attendance zones and other types of geographic boundaries containing roughly equal populations.

Valuación de la calidad urbano-ambiental. Una modelación hedónica: San Nicolás de los Garza, México / Assessment of Urban-Environmental Quality. A Hedonic Modeling: San Nicolás de los Garza, Mexico

En esta investigación se aborda la teoría de la formación espacial de valores inmobiliarios (vivienda) en el municipio de San Nicolás de los Garza, México. Ese ámbito se encuentra inmerso en una dinámica metropolitana, de ahí que se evalúe el proceso inmobiliario frente a las fuerzas centrales y periféricas que generan el crecimiento y el ciclo de vida urbano e inmobiliario, así como la calidad urbana ambiental. El resultado es un modelo de precios hedónicos que definen los atributos que explican, con mayor grado de significancia el cambio en el valor del suelo. Todo ello coadyuva a la implementación de una política de suelo. AbstractThis research deals with the theory of spatial information in real estate values (housing) in the municipality of San Nicolás de los Garza, Mexico, an area is immersed in a metropolitan dynamic. Consequently, the real estate process is evaluated in the context of the central and peripheral forces that generate growth and the urban and housing life cycle as well as urban environmental quality. The result is a hedonic pricing model to define the attributes that explain, with a greater degree of significance, the change in land value. All this contributes to the implementation of a land policy

Spatial Information Theory Meets Spatial Thinking: Is Topology the Rosetta Stone of Spatio-temporal Cognition?

Topology is the most commonly used spatial construct to bridge the gap between formal spatial information theory and systems on the one side and (human) spatial cognition and thinking on the other. To this end, we find topological calculi in virtually all research areas pertinent to spatial information science such as ontological modeling, geographic information retrieval, or image analysis and classification. Manifold behavioral experiments have been conducted to assess the cognitive adequacy of topological calculi with varying results. Our contribution here is unique for two reasons: first, we are addressing, behaviorally, the role of topology in the crucial area of spatio-temporal information; second, we are evaluating the role of topology across different semantic domains. We report five experiments that were conducted in the framework we developed (Klippel and Li 2009), which combines critical constructs from spatial information theory and cognitive science. Topologically equivalent movement patterns were specified across five domains using paths through a conceptual neighborhood graph. This approach allows us to disentangle the role of topology from the influence of semantic context. The results show that topology plays an important yet not semantic-independent role in characterizing the cognitive conceptualization of geographic events.

Brain arousal and information theory: neural and genetic mechanisms

Preface and Dedication 1. Toward a Universal Theory of Brain Arousal What Is to Be Explained? Ethology and the Mechanisms of Arousal Operational Definition of Arousal A Quantitative Approach to Physical Measurement of Generalized Arousal The Neurobiology of Arousal Constitutes an Interesting Application of Information Theory Claims for This Chapter and Introduction to Chapters Following 2. Anatomy Is Not Destiny, but a Little Neuroanatomy Helps Multiplicity and Redundancy of Ascending Arousal Pathways Prevent Failure Primitive Master in the Brainstem Provide a Neuroanatomic Core that Theoretically Matches the Behavioral Data Long-Distance Lines Tuning Local Modules Summary and Hypothetical Implications for Human Behavior 3. Arousal Is Signaled by Electrical Discharges in a System of Nerve Cells Traveling Up the Brainstem Special Cases: Olfaction and Vision Informational Content Governs Amplitude of Response in Neurons Related to Arousal Cerebral Cortex, the EEG Electrical Activity in Three Arousal-Related Biological Systems 4. Autonomic Nervous System Changes Supporting Arousal the Unity of the Body Patterns of Autonomic Responses Reformulations A High Information System Shows Coordination sans Correlation Supporting Hormone-Dependent Behaviors Summary 5. Genes Whose Neurochemical Products Support Arousal Genes Associated with Classical Systems Genes Newly Recognized Concepts and Questions Summary 6. Heightened States of Arousal: Sex Compared to Fear Sex Behavior's CNS Mechanisms Require Arousal Generalized Arousal Affects Specific Arousals and Vice Versa Contrast Sex and Fear Applicability of Information Theory Libido and Stress in Humans Summary 7. Major Systems Questions about Brain Arousal Networks What Are Universal Operating Features of Arousal Systems? How Do We Meet the Requirement for Rapid Changes of CNS State? Sensitivity and Alacrity of Response, Yet Stability? How? Does Automata Theory Apply? Questions in the Time Domain Questions about Spatial Properties Thermodynamics, Information Theory, and Questions for the CNS How Does a Sine Wave Impact a Sawtooth? Unity from Diversity? 8. Summary and Practical Importance: From Biological Mechanisms to Health Applications Main Points Applications to Human Conditions Works Cited Acknowledgments Index

Fractal analysis of plant spatial patterns: a monitoring tool for vegetation transition shifts

Spatial heterogeneity, like species diversity, is an important ecosystem property. We examine the effects of land use on the diversity and spatial distribution of plants in five semi-arid communities of eastern Spain using non-linear methods to assess the spatial-temporal dynamics of plant populations. Specifically, we are interested in detecting long-term structural changes or drift in an ecosystem before it is too late to prevent irreversible degradation. Fractal analysis is used to characterize the complexity of plant spatial patterns and Information Theory indices are used to measure change in information flow with land use changes and soil substrate. We found that grazing favored diversity and heterogeneity of species distribution on the impoverished gypsum and saline substrate community, as opposed to the detrimental effect of grazing in the Alpha steppe community. Indeed, old-field succession after 30 years of abandonment showed a recovery of species diversity but not the spatial structure of the vegetation. Further, Information Fractal Dimension, representing the unpredictability of plant spatial patterns in the landscape, increased as we moved from a highly diverse to a less diverse community, revealing the change to a more scattered and homogeneous spatial plant distribution. The Information Fractal Dimension is a good estimator of ecosystem disturbance, independent of scale, and thus can be used to monitor ecosystem dynamics.

3. Measures of Spatial Segregation

The measurement of residential segregation patterns and trends has been limited by a reliance on segregation measures that do not appropriately take into account the spatial patterning of population distributions. In this paper we define a general approach to measuring spatial segregation among multiple population groups. This general approach allows researchers to specify any theoretically based definition of spatial proximity desired in computing segregation measures. Based on this general approach, we develop a general spatial exposure/isolation index ( ), and a set of general multigroup spatial evenness/clustering indices: a spatial information theory index ( ), a spatial relative diversity index ( ), and a spatial dissimilarity index ( ). We review these and previously proposed spatial segregation indices against a set of eight desirable properties of spatial segregation indices. We conclude that the spatial exposure/isolation index *—which can be interpreted as a measure of the average composition of individuals' local spatial environments—and the spatial information theory index —which can be interpreted as a measure of the variation in the diversity of the local spatial environments of each individual—are the most conceptually and mathematically satisfactory of the proposed spatial indices.

Urban Structure and the Labour Market: Worker Mobility, Commuting, and Underemployment in Cities.

Theory of urban spatial structure information, search and matching - a survey job-worker matching in urban space job-worker matching and urban commuting job-worker matching and underemployment employment policies and urban redevelopment final thoughts...for now.