Common Spatial Structure Research Articles

Exploring the effects of urban spatial structure on green space in Chinese cities proper

The process of urbanization has led to the agglomeration of population in cities and the transformation of rural landscapes into impermeable surfaces, leaving green spaces severely eroded. Agglomeration is the essence of cities, and optimizing the spatial structure can preserve green space in cities. In urban planning practice, the most common urban spatial structures are monocentric and polycentric. It remains unclear whether monocentric structure with high-density attributes or a polycentric structure with decentralization has better green space performance. This study examines the effects of monocentric and polycentric urban spatial structures, as measured with the dimensions of spatial centralization and concentration, on the size and accessibility of green space in Chinese cities proper at the prefecture level and above from 2000 to 2015. Using a panel dataset and instrumental variable techniques, we observed that, under equal conditions, urban green space can be improved when cities proper have a compact monocentric structure. However, the accessibility of green space is better when cities proper have polycentric structures. These findings are robust across various specifications and instruments. Optimizing the urban spatial structure can increase the size of and accessibility to green space in cities. This is important for urban air purification, microclimate regulation, and residents’ recreation and happiness. Our results provide guidance for policymakers seeking to optimize urban spatial structures and develop green cities.

Partially overlapping spatial environments trigger reinstatement in hippocampus and schema representations in prefrontal cortex

When we remember a city that we have visited, we retrieve places related to finding our goal but also non-target locations within this environment. Yet, understanding how the human brain implements the neural computations underlying holistic retrieval remains unsolved, particularly for shared aspects of environments. Here, human participants learned and retrieved details from three partially overlapping environments while undergoing high-resolution functional magnetic resonance imaging (fMRI). Our findings show reinstatement of stores even when they are not related to a specific trial probe, providing evidence for holistic environmental retrieval. For stores shared between cities, we find evidence for pattern separation (representational orthogonalization) in hippocampal subfield CA2/3/DG and repulsion in CA1 (differentiation beyond orthogonalization). Additionally, our findings demonstrate that medial prefrontal cortex (mPFC) stores representations of the common spatial structure, termed schema, across environments. Together, our findings suggest how unique and common elements of multiple spatial environments are accessed computationally and neurally.

Gaussian process analysis of electron energy loss spectroscopy data: multivariate reconstruction and kernel control

Advances in hyperspectral imaging including electron energy loss spectroscopy bring forth the challenges of exploratory and physics-based analysis of multidimensional data sets. The multivariate linear unmixing methods generally explore similarities in the energy dimension, but ignore correlations in the spatial domain. At the same time, Gaussian process (GP) explicitly incorporate spatial correlations in the form of kernel functions but is computationally intensive. Here, we implement a GP method operating on the full spatial domain and reduced representations in the energy domain. In this multivariate GP, the information between the components is shared via a common spatial kernel structure, while allowing for variability in the relative noise magnitude or image morphology. We explore the role of kernel constraints on the quality of the reconstruction, and suggest an approach for estimating them from the experimental data. We further show that spatial information contained in higher-order components can be reconstructed and spatially localized.

Detecting the true urban polycentric pattern of Chinese cities in morphological dimensions: A multiscale analysis based on geospatial big data

With current decentralization trends and polycentric planning efforts, the urban spatial structures of Chinese cities have been changing tremendously. To detect the true urban polycentric pattern of Chinese cities, this article analyzed the urban polycentricity characteristics of 294 cities. The natural cities were delineated by points of interest (POIs), and road networks constituted street blocks. Based on check-in data and new spatial units, centers within both metropolitan areas and central cities were identified and examined. We discovered that all Chinese cities have at least one natural city in their metropolitan areas because of rapid urban sprawl. Although a monocentric structure is still the most common urban spatial structure, 110 Chinese cities displayed different degrees of polycentricity at the metropolitan level. Many natural cities beyond central cities contribute to polycentric development at the metropolitan level. Central cities have maintained their original vitality and importance, most Chinese cities have dispersed urban structures in central cities, and 45 central cities are polycentric. The spatial structures in metropolitan areas are more polycentric than those in central cities. The only 36 cities with polycentric urban structures at both the metropolitan and central city levels are all national or regional central cities in eastern China.

The Spatial Structure of The Landscape as One of the Elements of the Landscape Identity

The landscape identity is a holistic concept and includes many aspects of equal importance. Studies of the landscape identity often reflect local-scale aspects of the landscape identity; this is due to the emphasis and exploration of cultural and historical landscapes – through researching of a traditional management, traditional technology, the usage of local materials and local traditions. On a local scale, there are individual details, events, and people. The visual analysis and methods developed on its basis are mostly used in the studies of landscape identity. The architectural science recognizes the power of visual message which should be a planned and strategically guided aspect, as it has a significant impact on the forming of an overall image of the urban environment, as a visual message is a source of information and a topical issue in the context of the urban landscape. One of the characteristics of the visual aspects is the spatial structure formed by the building volume, the terrain, and the groups of woody plants, the planes formed by pavements, streets, meadows and fields, the water surface. Landscape identity is also based on the cognitive landscape research. The study of the interaction between identity and geographical environment provides an answer to how the transition from physical to socially cognitive identity occurs. Equally important is the group of cultural historical aspects of the landscape, based on multiple studies of the history of a particular place – from the beginnings of landscape formation, where morphological and climatic factors play an important role, to human made elements, where changes in the landscape structure and individual landscape elements are as a result of human economic activity that reflects the political, social and economic situation of the country. In the landscape planning process, the concept of the landscape identity is used to reinforce the character, distinction and specificity of each populated place or rural landscape, which is essential to preserve in creating structures and objects of a new landscape. The purpose of this article is to analyze the role of a spatial structure in the overall image of the landscape identity, as well as the subconscious, cognitive aspects that people think of landscape identity by common spatial structure, abstracting, smoothing out details, and giving symbolic meaning. The article summarizes the spatial structures of different landscapes, analyzes their diversity in both rural and urban environments, classifying them into types and subtypes. The article develops various spatial models and analyzes the cognitive perception of people using the survey capabilities. As a result, there are defined the role of the spatial structure in the comprehension of the landscape identity and recommendations for the inclusion and preservation of important spatial structures and elements in the planning process.

Convolutional Neural Network Case Studies: (1) Anomalies in Mortality Rates (2) Image Recognition

We provide a general introduction to convolutional neural networks (CNNs) in this tutorial. CNNs are particularly well suited to find common spatial structure in images or time series. As an insurance related example for life & health insurance we illustrate how to use a CNN to detect anomalies in mortality rates taken from the Human Mortality Database (HMD); the anomalies are caused by migration between countries and other errors. As a second example, we study a CNN to classify images of handwritten digits taken from one of the most widely used benchmark datasets, the Modified National Institute of Standards and Technology (MNIST) dataset. Our aim is to explore and discuss the building blocks and the properties of these CNNs, and we showcase their use.

Structure of spin correlations in high-temperature SU(N) quantum magnets

Quantum magnets with a large SU($N$) symmetry are a promising playground for the discovery of new forms of exotic quantum matter. Motivated by recent experimental efforts to study SU($N$) quantum magnetism in samples of ultracold fermionic alkaline-earth-like atoms in optical lattices, we study here the temperature dependence of spin correlations in the SU($N$) Heisenberg spin model in a wide range of temperatures. We uncover a sizeable regime in temperature, starting at $T=\infty$ down to intermediate temperatures and for all $N\ge2$, in which the correlations have a common spatial structure on a broad range of lattices, with the sign of the correlations alternating from one Manhattan shell to the next, while the amplitude of the correlations is rapidly decreasing with distance. Focussing on the one-dimensional chain and the two-dimensional square and triangular lattice for certain $N$, we discuss the appearance of a disorder and a Lifshitz temperature, separating the commensurate Manhattan high-$T$ regime from a low-$T$ incommensurate regime. We observe that this temperature window is associated to an approximately $N$-independent entropy reduction from the $\ln(N)$ entropy at infinite temperature. Our results are based on high-temperature series arguments and as well as large-scale numerical full diagonalization results of thermodynamic quantities for SU($3$) and SU($4$) square lattice samples, corresponding to a total Hilbert space of up to $4\times 10^9$ states.

A simple taxonomy for describing the spatio-temporal structure of environmental modelling data

Environmental modelling practitioners are now seeking to move forwards together and build standards and technologies with more universal applicability and integration. With so many overlapping environmental modelling technologies and infrastructures being offered and with so many relevant supporting technologies contributing on the periphery, there is considerable scope to articulate and utilise underlying concepts which draw them together. As such, this paper offers the Spatio-temporal Modelling Taxonomy, a simple taxonomy for describing the spatio-temporal structure of environmental numerical modelling data used as input to, or produced as output from environmental numerical models. The taxonomy is motivated from common spatial structures, a set of feature-types to describe observed environmental data and the implementation of the OpenMI integrated modelling standard. It serves as a natural evolution of terminology that is in common use in environmental numerical modelling and is designed to strike the right balance between complexity and utility. It implements a structured, theoretical framework, whilst being essentially practical in nature to apply to the ‘real world’ facing numerical modellers and those seeking to integrate environmental numerical models and the data supporting them.

Intersubject Spatial Pattern Correlations During Movie Viewing Are Stimulus-Driven and Nonuniform Across the Cortex.

A fundamental step to predicting brain activity in healthy and diseased populations is characterizing the common spatio-temporal response to a shared experience. Multivoxel pattern analysis allows us to investigate information encoding through these patterns; however, we have yet to explore local, stimulus-driven, patterns of cortical activity during naturalistic stimulation. We sought to examine these patterns with minimum interpolation—excluding functional alignment—to characterize the most basic degree of shared response between subjects. We used an unbiased analytic approach, combined with rich, naturalistic, and nonsemantic stimulation to estimate shared spatial patterns in functional magnetic resonance imaging responses across a large group. We found that meso-scale spatial patterns were shared nonuniformly across the visual cortex and represent information distinct from the shared temporal response. Shared spatial patterns were stimulus-driven, modulated by pattern size, and more sensitive to the contrast of 3D versus 2D stimulus differences than the temporal signals. Although the grand functional structure of the brain is understood to be common, these results suggest that even at a meso-scale, we share common spatial structures with anatomical alignment alone. The strength of this similarity varies across the cortex, suggesting some spatial structures are innately organized, whereas others are shaped by factors such as learning and plasticity.

Semi‐Procedural Textures Using Point Process Texture Basis Functions

AbstractWe introduce a novel semi‐procedural approach that avoids drawbacks of procedural textures and leverages advantages of data‐driven texture synthesis. We split synthesis in two parts: 1) structure synthesis, based on a procedural parametric model and 2) color details synthesis, being data‐driven. The procedural model consists of a generic Point Process Texture Basis Function (PPTBF), which extends sparse convolution noises by defining rich convolution kernels. They consist of a window function multiplied with a correlated statistical mixture of Gabor functions, both designed to encapsulate a large span of common spatial stochastic structures, including cells, cracks, grains, scratches, spots, stains, and waves. Parameters can be prescribed automatically by supplying binary structure exemplars. As for noise‐based Gaussian textures, the PPTBF is used as stand‐alone function, avoiding classification tasks that occur when handling multiple procedural assets. Because the PPTBF is based on a single set of parameters it allows for continuous transitions between different visual structures and an easy control over its visual characteristics. Color is consistently synthesized from the exemplar using a multiscale parallel texture synthesis by numbers, constrained by the PPTBF. The generated textures are parametric, infinite and avoid repetition. The data‐driven part is automatic and guarantees strong visual resemblance with inputs.

Surface water quality assessment in a semiarid Mediterranean region (Medjerda, Northern Tunisia) using partial triadic analysis.

A range of methods have been developed specifically to analyze several tables of data simultaneously (variable × space × time) in the field of ecological research, although they have been less widely used to examine water quality. In this study, we assessed the spatiotemporal variability of water quality in the Medjerda River basin (Northern Tunisia). Partial triadic analysis (PTA) provides an effective framework for the assessment of spatiotemporal variability of water quality in the Medjerda River basin (Northern Tunisia). Fourteen physicochemical variables were monitored from 12 sampling sites monthly during 2013. PTA allowed correlations among different physicochemical parameters to be identified and to assess overall water quality in the Medjerda River. Salinity (S), Cl-, SO42-, Ca2+, and Mg2+ ions were associated with intensive agricultural activities (agricultural pollution sources) leading to salinization. However, NH4+, PO43-, chemical oxygen demand (COD), and biochemical oxygen demand (BOD5) we more strongly associated with polluted urban sites. PTA helped illustrate that strong links exist between land uses and adjacent water quality. The advantages of this multi-table method approach for water quality monitoring include as follows: (1) identifying common multivariate spatial structures and problems associated with maintaining water quality, (2) allowing identification of consistent patterns in water chemistry, and (3) allowing analysis on the temporal variability of water chemistry.

Polycentric Urban Development and its Determinants in China: A Geospatial Big Data Perspective

The urban structure of large Chinese cities has been well researched, but a systematic analysis of polycentric urban development and the determinants of subcenter formation across municipal districts in cities at the prefectural level and above (PLACMD) is lacking. Using geospatial big data and spatial analysis methods, we measure the urban spatial structure of all 294 PLACMDs to determine the polycentric urban structure in China and conduct an exploratory regression analysis of 59 PLACMDs (due to data restrictions) to explore the formation of polycentric cities. Our results suggest that using location‐based data allows for a timelier and more accurate center identification of detailed urban structural features than using other data. Each PLACMD in China has at least one center, and polycentricity is currently the most common urban spatial structure. PLACMDs with higher populations are more polycentric. Compared with the results obtained from large American urban areas, our regression results imply that population alone accounts for most of the variation in the polycentric index and that commuting costs provide a weak explanation of the existence of Chinese PLACMDs. Both economic development and agglomeration economics are associated with the polycentric index. In contrast, the topographical features are statistically nonsignificant in the regression model.

Mental models use common neural spatial structure for spatial and abstract content

Mental models provide a cognitive framework allowing for spatially organizing information while reasoning about the world. However, transitive reasoning studies often rely on perception of stimuli that contain visible spatial features, allowing the possibility that associated neural representations are specific to inherently spatial content. Here, we test the hypothesis that neural representations of mental models generated through transitive reasoning rely on a frontoparietal network irrespective of the spatial nature of the stimulus content. Content within three models ranges from expressly visuospatial to abstract. All mental models participants generated were based on inferred relationships never directly observed. Here, using multivariate representational similarity analysis, we show that patterns representative of mental models were revealed in both superior parietal lobule and anterior prefrontal cortex and converged across stimulus types. These results support the conclusion that, independent of content, transitive reasoning using mental models relies on neural mechanisms associated with spatial cognition.

Evaluation of Progressive Collapse Performance in Double layer Diamatic Domes

Double-layer spatial domes are one of the most common spatial structures, the stability and progressive collapse of which are of great importance in design, construction and maintenance of such special structures. In this paper considering three loading cases and two types of support conditions, the collapse behaviour of double layer Diamatic dome has been investigated utilizing non-linear static analysis and alternate path method usage. In order to modelling compressive member behaviour, effective buckling modes have been obtained by eigenvalue buckling analysis for all of the members. Behaviour of compressive members has been obtained via definition of initial imperfection and non-linear static analysis. Riks arc-length method has been utilized for non-linear static analysis. The numerical results have indicated that reducing the number of the supports and focusing of load in a local area of the dome extremely impact on its vulnerability to failure, as in similar loading condition, decreasing the number of the supports reduces the capacity of damage resistance in spatial domes up to 50 percent. Investigating some models has shown that removing the critical members of the top layer has little effect on load-bearing capacity of the dome and it causes a slight failure in the structure. In this condition, structural redundancy can be considered equal to static indeterminacy. Load bearing capacity of the structure decreased up to 39 percent when compressive members of the web and bottom layers were removed. In this condition, the structure failure is considered moderate.

Failure modes and failure mechanisms of single-layer reticulated domes subjected to interior blasts

Single-layer reticulated domes are very common spatial structures. As landmarks, these types of structures can more easily be the targets of terrorist attacks than other buildings. However, blast resistance is not taken into consideration in the design of most civil structures. Therefore, it is important to know the damage level that may be imparted to single-layer reticulated domes after a blast attack. In this study, the dynamic response of reticulated domes subjected to an interior blast was investigated with numerical simulations, and five typical failure modes were identified from the results. In addition, the effects of some important parameters were investigated with a case study. Relationships between failure modes and interior blast impulses were summarised. Finally, the failure mechanisms were analysed, which could provide some design suggestions to decrease the probability of severe damage in spatial structures subjected to extreme dynamic loads.

Intratumoral heterogeneity of oxygen metabolism and neovascularization uncovers 2 survival-relevant subgroups of IDH1 wild-type glioblastoma.

The intratumoral heterogeneity of oxygen metabolism in combination with variable patterns of neovascularization (NV) as well as reprogramming of energy metabolism affects the landscape of tumor microenvironments (TMEs) in glioblastoma. Knowledge of the hypoxic and perivascular niches within the TME is essential for understanding treatment failure. Fifty-two patients with untreated glioblastoma (isocitrate dehydrogenase 1 wild type [IDH1wt]) were examined with a physiological MRI protocol including a multiparametric quantitative blood oxygen level dependent (qBOLD) approach and vascular architecture mapping (VAM). Imaging biomarker information about oxygen metabolism (mitochondrial oxygen tension) and neovascularization (microvascular density and type) were fused for classification of 6 different TMEs: necrosis, hypoxia with/without neovascularization, oxidative phosphorylation (OxPhos), and glycolysis with/without neovascularization. Association of the different TME volume fractions with progression-free survival (PFS) was assessed using Kaplan-Meier analysis and Cox proportional hazards models. A common spatial structure of TMEs was detected: central necrosis surrounded by tumor hypoxia (with defective and functional neovasculature) and different TMEs with a predominance of OxPhos and glycolysis for energy production, respectively. The percentage of the different TMEs on the total tumor volume uncovered 2 clearly different subtypes of glioblastoma IDH1wt: a glycolytic dominated phenotype with predominantly functional neovasculature and a necrotic/hypoxic dominated phenotype with approximately 50% of defective neovasculature. Patients with a necrotic/hypoxic dominated phenotype showed significantly shorter PFS (P = 0.035). Our non-invasive mapping approach allows for classification of the TME and detection of tumor-supportive niches in glioblastoma which may be helpful for both clinical patient management and research.

CCRC Common Facility Spatial Structure: A Study by Space Syntax

Spatial structure of 56 CCRCs(Continue Care Retirement Committee) which are mainly selected from AIA contest project are analyzed by using space syntax theory. Based on the result of common facility allocation and spatial centrality, three common facility allocation types and six route space integration models are defined. While the analysis shows the central allocation with route connected to a common facility being the highest integration place is the predominant spatial structure of common facility in US CCRCs, the combination of each common facility allocation type and route space integration models provide the fashion of common facility spatial structure in US CCRCs today.

Functional uniformity underlies the common spatial structure of macrofaunal assemblages in intertidal seagrass beds

Previous work has shown that the intertidal seagrass macrobenthos at three geographically and ecologically disparate localities (in the north-east Atlantic, south-west Indian and south-west Pacific Oceans) possess similar relative species occurrence distributions and uniform species densities. These common features are here demonstrated to be related to the presence in those assemblages of: (1) similar functional diversities and evennesses, (2) the same set of dominant component functional groups, and (3) similar ranked relative occurrence distributions both of those groups and of the component genera within each of the larger groups. The two lower-latitude systems were particularly similar in all these respects. Although sharing the same subset of individual functional groups, however, the relative importance of members of that subset varied from locality to locality and even within a single locality, whilst still maintaining the same ranked relative functional-group occurrence distribution. Therefore the broad structure of available macrobenthic functional roles and the relative occurrences of the component taxa in intertidal seagrass beds (and hence, granted stochastic assembly, the total numbers of taxa supported by unit area) are likely to be linked causally, although the form of the relationship is unclear.

Tuning and spontaneous spike time synchrony share a common structure in macaque inferior temporal cortex.

Investigating the relationship between tuning and spike timing is necessary to understand how neuronal populations in anterior visual cortex process complex stimuli. Are tuning and spontaneous spike time synchrony linked by a common spatial structure (do some cells covary more strongly, even in the absence of visual stimulation?), and what is the object coding capability of this structure? Here, we recorded from spiking populations in macaque inferior temporal (IT) cortex under neurolept anesthesia. We report that, although most nearby IT neurons are weakly correlated, neurons with more similar tuning are also more synchronized during spontaneous activity. This link between tuning and synchrony was not simply due to cell separation distance. Instead, it expands on previous reports that neurons along an IT penetration are tuned to similar but slightly different features. This constraint on possible population firing rate patterns was consistent across stimulus sets, including animate vs. inanimate object categories. A classifier trained on this structure was able to generalize category "read-out" to untrained objects using only a few dimensions (a few patterns of site weightings per electrode array). We suggest that tuning and spike synchrony are linked by a common spatial structure that is highly efficient for object representation.

On the potential for the Partial Triadic Analysis to grasp the spatio-temporal variability of groundwater hydrochemistry

Standard multivariate statistical techniques, such as principal components analysis and hierarchical cluster analysis, have been widely used as unbiased methods for extracting meaningful information from groundwater quality data. However, these classical multivariate methods deal with two-way matrices, usually parameters×sites or parameters×time, while often the dataset resulting from qualitative water monitoring programs should be seen as a datacube parameters×sites×time. Three-way matrices, such as the one proposed here, are difficult to handle and to analyse by classical multivariate statistical tools and thus should be treated with approaches dealing with three-way data structures. One possible analysis approach is the use of Partial Triadic Analysis (PTA). Applied to the dataset of the Luxembourg Sandstone aquifer, the PTA appears to be a new promising statistical instrument for hydrogeologists, for characterization of temporal or spatial hydrochemical variations induced by natural and anthropogenic factors. This new approach for groundwater management offers potential for (1) identifying a common multivariate spatial structure, (2) untapping the different hydrochemical patterns and explaining their controlling factors and (3) analyzing the temporal variability of this structure and grasping hydrochemical changes.