Geography of Talent in China During 2000-2015:An Eigenvector Spatial Filtering Negative Binomial Approach

The increase in China’s skilled labor force has drawn much attention from policymakers,national and international firms and media.Understanding how educated talent locates and re-locates across the country can guide future policy discussions of equality,firm localization and service allocation.Prior studies have tended to adopt a static cross-national approach providing valuable insights into the relative importance of economic and amenity differentials driving the distribution of talent in China.Yet,few adopt longitudinal analysis to examine the temporal dynamics in the stregnth of existing associations.Recently released official statistical data now enables space-time analysis of the geographic distribution of talent and its determinants in China.Using four-year city-level data from national population censuses and 1%population sample surveys conducted every five years between 2000 and 2015,we examine the spatial patterns of talent across Chinese cities and their underpinning drivers evolve over time.Results reveal that the spatial distribution of talent in China is persistently unequal and spatially concentrated between 2000 and 2015.It also shows gradually strengthened and significantly positive spatial autocorrelation in the distribution of talent.An eigenvector spatial filtering negative binomial panel is employed to model the spatial determinants of talent distribution.Results indicate the influences of both economic opportunities and urban amenities,particularly urban public services and greening rate,on the distribution of talent.These results highlight that urban economic-and amenity-related factors have simultaneously driven China’s talent’s settlement patterns over the first fifteen years of the 21st century.

Improved spatial filtering velocimetry and its application in granular flow measurement

Spatial filtering velocimetry(SFV)has the advantages of simple structure,good stability,and wide applications.However,the traditional linear CCD-based SFV method requires an accurate angle between the direction of linear CCD and the direction of moving object,so it is not suitable for measuring a complex flow field or two-dimensional speed in a granular media.In this paper,a new extension of spatial filtering method(SFM)based on high speed array CCD camera is proposed as simple and effective technique for measuring two-dimensional speed field of granular media.In particular,we analyzed the resolution and range of array CCD-based SFV so that the reader can clarify the application scene of this method.This method has a particular advantage for using orthogonal measurement to avoid the angle measurement,which were problematic when using linear CCD to measure the movement.Finally,the end-wall effects of the granular flow in rotating drum is studied with different experimental conditions by using this improved technique.

An enhanced method for estimating snow water equivalent in the central part of the Tibetan Plateau using raster segmentation and eigenvector spatial filtering regression model

Snow water equivalent(SWE)is an important factor reflecting the variability of snow.It is important to estimate SWE based on remote sensing data while taking spatial autocorrelation into account.Based on the segmentation method,the relationship between SWE and environmental factors in the central part of the Tibetan Plateau was explored using the eigenvector spatial filtering(ESF)regression model,and the influence of different factors on the SWE was explored.Three sizes of 16×16,24×24 and 32×32 were selected to segment raster datasets into blocks.The eigenvectors of the spatial adjacency matrix of the segmented size were selected to be added into the model as spatial factors,and the ESF regression model was constructed for each block in parallel.Results show that precipitation has a great influence on SWE,while surface temperature and NDVI have little influence.Air temperature,elevation and surface temperature have completely different effects in different areas.Compared with the ordinary least square(OLS)linear regression model,geographically weighted regression(GWR)model,spatial lag model(SLM)and spatial error model(SEM),ESF model can eliminate spatial autocorrelation with the highest accuracy.As the segmentation size increases,the complexity of ESF model increases,but the accuracy is improved.

A True-Time-Delay Array Architecture for Wideband Multi-Beam Tracking

The true-time delay(TTD)units are critical for solving beam squint and frequency selective fading inWideband Large-Scale Antenna Systems(LSASs).In this work,we propose a TTD array architecture for wideband multi-beam tracking that eliminates the beam squint phenomenon and filters out interference signals by applying a spatial filter and time delay estimations(TDEs).The paper presents a novel approach to spatial filter design by introducing a transformation matrix that can optimize the beam response in a specific direction and at a specific frequency.Using the variable fractional delay(VFD)filters,we propose a TDE algorithm with a Newton-Raphson iteration update process that corrects the arrival time delay difference between sensors.Simulations and examples have demonstrated that the proposed architecture can achieve beam tracking within 10 ms at the low signalto-noise ratio(SNR)and demodulation loss is less than 0.5 dB in wideband multi-beam scenarios.

Modelling of Wideband Concentric Ring Frequency Selective Surface for 5G Devices

Frequency selective surfaces(FSSs)play an important role in wireless systems as these can be used as filters,in isolating the unwanted radiation,in microstrip patch antennas for improving the performance of these antennas and in other 5G applications.The analysis and design of the double concentric ring frequency selective surface(DCRFSS)is presented in this research.In the sub-6 GHz 5G FR1 spectrum,a computational synthesis technique for creating DCRFSS based spatial filters is proposed.The analytical tools presented in this study can be used to gain a better understanding of filtering processes and for constructing the spatial filters.Variation of the loop sizes,angles of incidence,and polarization of the concentric rings are the factors which influence the transmission coefficient as per the thorough investigation performed in this paper.A novel synthesis approach based on mathematical equations that may be used to determine the physical parameters ofDCRFSSbased spatial filters is presented.The proposed synthesis technique is validated by comparing results from high frequency structure simulator(HFSS),Ansys electronic desktop circuit editor,and an experimental setup.Furthermore,the findings acquired from a unit cell are expanded to a 2×2 array,which shows identical performance and therefore proves its stability.

A variational successive corrections approach for the sea ice concentration analysis

The sea ice concentration observation from satellite remote sensing includes the spatial multi-scale information.However,traditional data assimilation methods cannot better extract the valuable information due to the complicated variability of the sea ice concentration in the marginal ice zone.A successive corrections analysis using variational optimization method,called spatial multi-scale recursive filter(SMRF),has been designed in this paper to extract multi-scale information resolved by sea ice observations.It is a combination of successive correction methods(SCM)and minimization algorithms,in which various observational scales,from longer to shorter wavelengths,can be extracted successively.As a variational objective analysis scheme,it gains the advantage over the conventional approaches that analyze all scales resolved by observations at one time,and also,the specification of parameters is more convenient.Results of single-observation experiment demonstrate that the SMRF scheme possesses a good ability in propagating observational signals.Further,it shows a superior performance in extracting multi-scale information in a two-dimensional sea ice concentration(SIC)experiment with the real observations from Special Sensor Microwave/Imager SIC(SSMI).

Population spatialization with pixel-level attribute grading by considering scale mismatch issue in regression modeling

Population spatialization is widely used for spatially downscaling census population data to finer-scale.The core idea of modern population spatialization is to establish the association between ancillary data and population at the administrative-unit-level(AUlevel)and transfer it to generate the gridded population.However,the statistical characteristic of attributes at the pixel-level differs from that at the AU-level,thus leading to prediction bias via the cross-scale modeling(i.e.scale mismatch problem).In addition,integrating multi-source data simply as covariates may underutilize spatial semantics,and lead to incorrect population disaggregation;while neglecting the spatial autocorrelation of population generates excessively heterogeneous population distribution that contradicts to real-world situation.To address the scale mismatch in downscaling,this paper proposes a Cross-Scale Feature Construction(CSFC)method.More specifically,by grading pixel-level attributes,we construct the feature vector of pixel grade proportions to narrow the scale differences in feature representation between AU-level and pixel-level.Meanwhile,fine-grained building patch and mobile positioning data are utilized to adjust the population weighting layer generated from POI-density-based regression modeling.Spatial filtering is furtherly adopted to model the spatial autocorrelation effect of population and reduce the heterogeneity in population caused by pixel-level attribute discretization.Through the comparison with traditional feature construction method and the ablation experiments,the results demonstrate significant accuracy improvements in population spatialization and verify the effectiveness of weight correction steps.Furthermore,accuracy comparisons with WorldPop and GPW datasets quantitatively illustrate the advantages of the proposed method in fine-scale population spatialization.

Development of high damage threshold laser-machined apodizers and gain filters for laser applications

We have developed high damage threshold filters to modify the spatial profile of a high energy laser beam. The filters are formed by laser ablation of a transmissive window. The ablation sites constitute scattering centers which can be filtered in a subsequent spatial filter. By creating the filters in dielectric materials, we see an increased laser-induced damage threshold from previous filters created using ‘metal on glass' lithography.

1995-2015年中国老年人口迁移的空间格局与影响因素

Although China was one of the countries with the fastest-growing aging population in the world,limited scholarly attention has been paid to migration among older adults in China.The full picture of their migration in the entire country over time remains unknown.This study examines the spatial patterns of older interprovincial migration flows and their drivers in China over the period 1995 to 2015,using four waves of census data and intercensal population sample survey data.Results from eigenvector spatial filtering negative binomial regressions indicate that older adults tend to migrate away from low cost-of-living rural areas to high cost-of-living urban and rural areas,moving away from areas with extreme temperature differences.The location of their grandchildren is among the most important attractions.Our findings suggest that family-oriented migration is more common than amenity-led migration among retired Chinese older adults,and the cost-of-living is an indicator of economic opportunities for adult children and the quality of senior care services.

邻里效应对中国自然保护区家庭参与生态系统服务付费项目的影响--方法研究

Payments for Ecosystem Services(PES)programs have been implemented in both developing and developed countries to conserve ecosystems and the vital services they provide.These programs also often seek to maintain or improve the economic wellbeing of the populations living in the corresponding(usually rural)areas.Previous studies suggest that PES policy design,presence or absence of concurrent PES programs,and a variety of socioeconomic and demographic factors can influence decisions of households to participate or not in the PES program.However,neighborhood impacts on household participation in PES have rarely been addressed.This study explores potential neighborhood effects on villagers'enrollment in the Grain-to-Green Program(GTGP),one of the largest PES programs in the world,using data from China's Fanjingshan National Nature Reserve.We utilize a fixed effects logistic regression model in combination with the eigenvector spatial filtering(ESF)method to explore whether neighborhood size affects household enrollment in GTGP.By comparing the results with and without ESF,we find that the ESF method can help account for spatial autocorrelation properly and reveal neighborhood impacts that are otherwise hidden,including the effects of area of forest enrolled in a concurrent PES program,gender and household size.The method can thus uncover mechanisms previously undetected due to not taking into account neighborhood impacts and thus provides an additional way to account for neighborhood impacts in PES programs and other studies.