Spatial Analysis of the Aging Population and Socio-economic Factors of China:Global and Local Perspectives

Population aging has become an inevitable trend and exerted profound influences on socio-economic development in China.In this study,we utilized data from national population census and statistical yearbooks in 2010 and 2020 to explore spatio-temporal patterns of aging population and its coupling correlations with socio-economic factors from both global and local perspectives.The results from Local Indicators of Spatial Association(LISA)uncover notable spatial disparities in aging population rates,with higher rates concentrated in the eastern regions and lower rates in the western areas of the Chinese mainland.The results from the global correlation analysis with the changes in aging population rates show significant positive correlations with government interventions and industrial structures,but negatively correlated with economic development,social consumption,and medical facilities.From a local perspective,a Geographically Weighted(GW)correlation analysis is employed to uncover local correlations between aging trends and socio-economic factors.The insights gained from this technique not only underscore the complexity and diversity of economic implications stemming from population aging,but also provide invaluable guidance for crafting region-specific economic policies tailored to various stages of population aging.

S^(2)ANet:Combining local spectral and spatial point grouping for point cloud processing

Background Despite the recent progress in 3D point cloud processing using deep convolutional neural networks,the inability to extract local features remains a challenging problem.In addition,existing methods consider only the spatial domain in the feature extraction process.Methods In this paper,we propose a spectral and spatial aggregation convolutional network(S^(2)ANet),which combines spectral and spatial features for point cloud processing.First,we calculate the local frequency of the point cloud in the spectral domain.Then,we use the local frequency to group points and provide a spectral aggregation convolution module to extract the features of the points grouped by the local frequency.We simultaneously extract the local features in the spatial domain to supplement the final features.Results S^(2)ANet was applied in several point cloud analysis tasks;it achieved stateof-the-art classification accuracies of 93.8%,88.0%,and 83.1%on the ModelNet40,ShapeNetCore,and ScanObjectNN datasets,respectively.For indoor scene segmentation,training and testing were performed on the S3DIS dataset,and the mean intersection over union was 62.4%.Conclusions The proposed S^(2)ANet can effectively capture the local geometric information of point clouds,thereby improving accuracy on various tasks.

Locally varying geostatistical machine learning for spatial prediction

Machine learning methods dealing with the spatial auto-correlation of the response variable have garnered significant attention in the context of spatial prediction.Nonetheless,under these methods,the relationship between the response variable and explanatory variables is assumed to be homogeneous throughout the entire study area.This assumption,known as spatial stationarity,is very questionable in real-world situations due to the influence of contextual factors.Therefore,allowing the relationship between the target variable and predictor variables to vary spatially within the study region is more reasonable.However,existing machine learning techniques accounting for the spatially varying relationship between the dependent variable and the predictor variables do not capture the spatial auto-correlation of the dependent variable itself.Moreover,under these techniques,local machine learning models are effectively built using only fewer observations,which can lead to well-known issues such as over-fitting and the curse of dimensionality.This paper introduces a novel geostatistical machine learning approach where both the spatial auto-correlation of the response variable and the spatial non-stationarity of the regression relationship between the response and predictor variables are explicitly considered.The basic idea consists of relying on the local stationarity assumption to build a collection of local machine learning models while leveraging on the local spatial auto-correlation of the response variable to locally augment the training dataset.The proposed method’s effectiveness is showcased via experiments conducted on synthetic spatial data with known characteristics as well as real-world spatial data.In the synthetic(resp.real)case study,the proposed method’s predictive accuracy,as indicated by the Root Mean Square Error(RMSE)on the test set,is 17%(resp.7%)better than that of popular machine learning methods dealing with the response variable’s spatial auto-correlation.Additionally,this method is not only valuable for spatial prediction but also offers a deeper understanding of how the relationship between the target and predictor variables varies across space,and it can even be used to investigate the local significance of predictor variables.

Fingerspelling Recognition by Hand Shape Using Higher-Order Local Auto-Correlation Features

The fingerspelling recognition by hand shape is an important step for developing a human-computer interaction system. A method of fingerspelling recognition by hand shape using HLAC （higher-order local auto-correlation） features is proposed. Furthermore, in order to use HLAC features more effectively, the use of image processing techniques： reducing an image resolution, dividing an image, and image pre-processing techniques, is also proposed. The experimental results show that the proposed method is promising.

Local spatial properties based image interpolation scheme using SVMs

Image interpolation plays an important role in image process applications. A novel support vector machines （SVMs） based interpolation scheme is proposed with increasing the local spatial properties in the source image as SVMs input patterns. After the proper neighbor pixels region is selected, trained support vectors are obtained by training SVMs with local spatial properties that include the average of the neighbor pixels gray values and the gray value variations between neighbor pixels in the selected region. The support vector regression machines are employed to estimate the gray values of unknown pixels with the neighbor pixels and local spatial properties information. Some interpolation experiments show that the proposed scheme is superior to the linear, cubic, neural network and other SVMs based interpolation approaches.

NLoS Mitigation in ToA Localization Based on Spatial Correlation Filter and Iterative Minimum Residual

To mitigate the Non-Line-of-Sight (NLoS) error which seriously affects the localization accuracy and robustness in complex indoor environment,a novel Iterative Minimum Residual (IMR) based on the consistency hypothesis of the residual and the error is proposed in this paper.It chooses the best subset of measurements to calculate the coordinates of the unknown node by comparing the residuals obtained with different subsets of beacons.To reduce the time complexity of the IMR algorithm,Spatial Correlation Filter (SCF) is also proposed,which can remove the most serious NLoS distance with low calculation cost.Combined with the proposed SCF and IMR algorithm,nodes can be localized with high accuracy and low time complexity.Experimental results with real dataset demonstrate that the proposed algorithm can identify the NLoS range effectively with about 50% time cost of employing SCF only.

Spatial localization of ECE measurement in EAST LHW-heated plasmas

In this work, electron cyclotron emission(ECE) is simulated by using the code SPECE to study the spatial localization of ECE measurement in EAST plasmas heated by lower hybrid wave(LHW).The results indicate that generally there are two emission layers for an individual frequency in plasmas with non-thermal electrons, and they are separately attributed to the thermal electrons and non-thermal electrons. The emission layer due to the thermal electrons is nearly identical to that for the case with Maxwellian distribution. The emission layer due to non-thermal electrons is well localized in the location of the non-thermal electrons. Even though the non-thermal emission layer is broader, the emission intensity is smaller than that from the thermal emission layer for the cases studied in this work. Localized electron temperature fluctuations can still be distinguished by ECE measurement as long as it does not coexist with the non-thermal electrons. Sawtooth inversion radii and tearing mode island location determined respectively by the ECE measurement and the soft x-ray measurement for a LHW-heated plasma show a good agreement, and this indicates that the ECE measurement in the plasma core region is not seriously polluted.

Spatial Autocorrelation and Localization of Urban Development

A nonlinear analysis of urban evolution is made by using of spatial autocorrelation theory. A first-order nonlinear autoregression model based on Clark’s negative exponential model is proposed to show urban population density. The new method and model are applied to Hangzhou City, China, as an example. The average distance of population activities, the auto-correlation coefficient of urban population density, and the auto-regressive function values all show trends of gradual increase from 1964 to 2000, but there always is a sharp first-order cutoff in the partial auto- correlations. These results indicate that urban development is a process of localization. The discovery of urban locality is significant to improve the cellular-automata-based urban simulation of modeling spatial complexity.

Sound Source Localization Based on SRP-PHAT Spatial Spectrum and Deep Neural Network

Microphone array-based sound source localization(SSL)is a challenging task in adverse acoustic scenarios.To address this,a novel SSL algorithm based on deep neural network(DNN)using steered response power-phase transform(SRP-PHAT)spatial spectrum as input feature is presented in this paper.Since the SRP-PHAT spatial power spectrum contains spatial location information,it is adopted as the input feature for sound source localization.DNN is exploited to extract the efficient location information from SRP-PHAT spatial power spectrum due to its advantage on extracting high-level features.SRP-PHAT at each steering position within a frame is arranged into a vector,which is treated as DNN input.A DNN model which can map the SRP-PHAT spatial spectrum to the azimuth of sound source is learned from the training signals.The azimuth of sound source is estimated through trained DNN model from the testing signals.Experiment results demonstrate that the proposed algorithm significantly improves localization performance whether the training and testing condition setup are the same or not,and is more robust to noise and reverberation.

Object detection based on combination of local and spatial information

A method of object detection based on combination of local and spatial information is proposed. Firstly, the categorygiven representative images are chosen through clustering to be templates, and the local and spatial information of template are ex- tracted and generalized as the template feature. At the same time, the codebook dictionary of local contour is also built up. Secondly, based on the codebook dictionary, sliding-window mechanism and the vote algorithm are used to select initial candidate object win- dows. Lastly, the final object windows are got from initial candidate windows based on local and spatial structure feature matching. Experimental results demonstrate that the proposed approach is able to consistently identify and accurately detect the objects with better performance than the existing methods.

Encoding of local and global cues in domestic dogs’ spatial working memory

The current study investigated whether domestic dogs encode local and/or global cues in spatial working memory. Seven dogs were trained to use a source of allocentric information (local and/or global cues) to locate an attractive object they saw move and disappear behind one of the three opaque boxes arrayed in front of them. To do so, after the disappearance of the target object and out of the dogs’ knowledge, all sources of allocentric information were simultaneously shifted to a new spatial position and the dogs were forced to follow a U-shaped pathway leading to the hiding box. Out of the seven dogs that were trained in the detour problem, only three dogs learned to use the cues that were moved from trial to trial. On tests, local (boxes and experimenter) and/or global cues (walls of the room) were systematically and drastically shifted to a new position in the testing chamber. Although they easily succeeded the control trials, the three dogs failed to use a specific source of allocentric information when local and global cues were put in conflict. In discussion, we explore several hypotheses to explain why dogs have difficulties to use allocentric cues to locate a hidden object in a detour problem and why they do not differentiate the local and global cues in this particular experimental setting.

A variation pixels identification method based on kernel spatial attraction model and local entropy for robust endmember extraction

A variation pixels identification method was proposed aiming at depressing the effect of variation pixels, which dilates the theoretical hyperspectral data simplex and misguides volume evaluation of the simplex. With integration of both spatial and spectral information, this method quantitatively defines a variation index for every pixel. The variation index is proportional to pixels local entropy but inversely proportional to pixels kernel spatial attraction. The number of pixels removed was modulated by an artificial threshold factor α. Two real hyperspectral data sets were employed to examine the endmember extraction results. The reconstruction errors of preprocessing data as opposed to the result of original data were compared. The experimental results show that the number of distinct endmembers extracted has increased and the reconstruction error is greatly reduced. 100% is an optional value for the threshold factor α when dealing with no prior knowledge hyperspectral data.

Local-Tetra-Patterns for Face Recognition Encoded on Spatial Pyramid Matching

Face recognition is a big challenge in the research field with a lot of problems like misalignment,illumination changes,pose variations,occlusion,and expressions.Providing a single solution to solve all these problems at a time is a challenging task.We have put some effort to provide a solution to solving all these issues by introducing a face recognition model based on local tetra patterns and spatial pyramid matching.The technique is based on a procedure where the input image is passed through an algorithm that extracts local features by using spatial pyramid matching andmax-pooling.Finally,the input image is recognized using a robust kernel representation method using extracted features.The qualitative and quantitative analysis of the proposed method is carried on benchmark image datasets.Experimental results showed that the proposed method performs better in terms of standard performance evaluation parameters as compared to state-of-the-art methods on AR,ORL,LFW,and FERET face recognition datasets.

Ultrafast dynamics of femtosecond laser-induced high spatial frequency periodic structures on silicon surfaces

Femtosecond laser-induced periodic surface structures(LIPSS)have been extensively studied over the past few decades.In particular,the period and groove width of high-spatial-frequency LIPSS(HSFL)is much smaller than the diffraction limit,making it a useful method for efficient nanomanufacturing.However,compared with the low-spatial-frequency LIPSS(LSFL),the structure size of the HSFL is smaller,and it is more easily submerged.Therefore,the formation mechanism of HSFL is complex and has always been a research hotspot in this field.In this study,regular LSFL with a period of 760 nm was fabricated in advance on a silicon surface with two-beam interference using an 800 nm,50 fs femtosecond laser.The ultrafast dynamics of HSFL formation on the silicon surface of prefabricated LSFL under single femtosecond laser pulse irradiation were observed and analyzed for the first time using collinear pump-probe imaging method.In general,the evolution of the surface structure undergoes five sequential stages:the LSFL begins to split,becomes uniform HSFL,degenerates into an irregular LSFL,undergoes secondary splitting into a weakly uniform HSFL,and evolves into an irregular LSFL or is submerged.The results indicate that the local enhancement of the submerged nanocavity,or the nanoplasma,in the prefabricated LSFL ridge led to the splitting of the LSFL,and the thermodynamic effect drove the homogenization of the splitting LSFL,which evolved into HSFL.

Spatial Development of Local Bantik Community in Malalayang, Indonesia

The Proximity between the central business district and the settlement has led to many changes in the local Bantik community. These include changes in the function of settlements, population size, location of residence, and the movement of local culture. This study aims to examine the spatial changes that occur in local neighborhoods with a focus on the Bantik tribal community in Malalayang. Data were obtained from a series of field observations, questionnaires and structured interviews. This study conducted a series of analyses on spatial patterns, sociocultural factors and urban policy. The results show that the dynamic changes are natural and hard to avoid, since they are related to the community＇s needs and development of the city. In order to face the changes, adjustments in the values of the local community towards the settlement terms and conditions are necessary. In addition, an increase in internal resources for those local communities is needed.

中文简版Spatial Hearing Questionnaire的信效度分析

目的空间听觉指在复杂声环境下,患者能够理解言语和识别不同方位声音的能力,可以采用Spatial Hearing Questionnaire评估。本研究在正常听力受试者及耳聋受试者中进行中文简版Spatial Hearing Questionnaire信效度分析。方法首先对Spatial Hearing Questionnaire进行汉化形成C-SHQ12。纳入50例正常听力受试者及57例耳聋受试者进行研究,采用内部一致性检验评价量表的信度,采用探索性因子分析评价量表的结构效度。采用t检验分析正常听力受试者与耳聋受试者C-SHQ12得分的差别,采用pearson相关分析SHQ量表得分与实验室声源定位测试的相关性。采用配对t检验对人工耳蜗植入术前和术后的C-SHQ12得分进行比较。结果 Cronbach’sα系数为0.98,说明量表具有良好的内部一致性信度。采用探索性因子分析提取了2个公因子,分别是声源定位和噪声环境下言语能力,累计贡献率为94.8%。正常听力受试者C-SHQ12得分为87.74±7.99,耳聋受试者得分为52.80±21.76,P=2.2×10-16<0.01,两者差异有显著统计学意义。C-SHQ12量表得分与实验室声源定位得分有很好的相关性,P=2.858×10-6<0.01。人工耳蜗植入术前和术后的C-SHQ12得分有显著差异。结论 C-SHQ12量表信效度很高,可用于耳聋人群的空间听觉能力评估。

弱空间式Locale

空间式locale范畴SLoc是locale范畴Loc的余反射满子范畴,但对locale乘积不封闭．本文引入弱空间式locale,证明弱空间式locale范畴WSloc为范畴Loc的余反射满子范畴,且对locale秉积封闭．还证明了一个locale A是空间式的当且仅当它的枝映射localeN(A)是弱空间式的;一个空问式locale的每一个子locale都是空间式的当且仅当它的每一个子locale是弱空间式的．最后,证明了弱空间式性在定向函子下保持不变．

关于Locale的T_2特征

首先对Locale的两种关于有点式T2特征作出了完整的比较．其次通过引入一种关系“”（称之为weakwellinside），给出了一种合理的无点locale的T2定义，并证明了在空间式条件下其与有点式T2性刻划是等价的．

Examining the Relationship Between Spatial Configurations of Urban Impervious Surfaces and Land Surface Temperature

The urban heat island(UHI) effect has significant effects on the quality of life and public health. Numerous studies have addressed the relationship between UHI and the increase in urban impervious surface area(ISA), but few of them have considered the impact of the spatial configuration of ISA on UHI. Land surface temperature(LST) may be affected not only by urban land cover, but also by neighboring land cover. The aim of this research was to investigate the effects of the abundance and spatial association of ISAs on LST. Taking Harbin City, China as an example, the impact of ISA spatial association on LST measurements was examined. The abundance of ISAs and the LST measurements were derived from Landsat Thematic Mapper(TM) imagery of 2000 and 2010, and the spatial association patterns of ISAs were calculated using the local Moran’s I index. The impacts of ISA abundance and spatial association on LST were examined using correlation analysis. The results suggested that LST has significant positive associations with both ISA abundance and the Moran’s I index of ISAs, indicating that both the abundance and spatial clustering of ISAs contribute to elevated values of LST. It was also found that LST is positively associated with clustering of high-ISA-percentage areas(i.e.,>50%) and negatively associated with clustering of low-ISA-percentage areas(i.e.,<25%). The results suggest that, in addition to the abundance of ISAs,their spatial association has a significant effect on UHIs.

Modeling of Spatial Distributions of Farmland Density and Its Temporal Change Using Geographically Weighted Regression Model

This study used spatial autoregression(SAR)model and geographically weighted regression(GWR)model to model the spatial patterns of farmland density and its temporal change in Gucheng County,Hubei Province,China in 1999 and 2009,and discussed the difference between global and local spatial autocorrelations in terms of spatial heterogeneity and non-stationarity.Results showed that strong spatial positive correlations existed in the spatial distributions of farmland density,its temporal change and the driving factors,and the coefficients of spatial autocorrelations decreased as the spatial lag distance increased.SAR models revealed the global spatial relations between dependent and independent variables,while the GWR model showed the spatially varying fitting degree and local weighting coefficients of driving factors and farmland indices(i.e.,farmland density and temporal change).The GWR model has smooth process when constructing the farmland spatial model.The coefficients of GWR model can show the accurate influence degrees of different driving factors on the farmland at different geographical locations.The performance indices of GWR model showed that GWR model produced more accurate simulation results than other models at different times,and the improvement precision of GWR model was obvious.The global and local farmland models used in this study showed different characteristics in the spatial distributions of farmland indices at different scales,which may provide the theoretical basis for farmland protection from the influence of different driving factors.