Spatial Weight Matrix Research Articles

Using the specification curve to teach spatial data analysis and explore geographic uncertainties

ABSTRACT Educational materials focused on spatial data analysis often feature mathematical descriptions of methods and step-by-step instructions of software tools, but infrequently discuss the set of decisions involved in specifying a statistical model. Failing to consider model specification may lead to specification searching, or the process of repeating analyses to obtain results that meet the criteria thought to be required for publication, and the disproportionate reporting of false-positive results in the academic literature. This article proposes that the specification curve – a meta-analytical technique that visualizes the specifications and results from a large set of justifiable and plausible statistical models – be used as a pedagogical tool to teach (spatial) data analysis and explore the geographic uncertainties that arise when specifying and interpreting spatial regression models. An example specification curve that focuses on two common specification decisions in a spatial regression model, specifically selecting predictor variables and constructing the spatial weight matrix, is illustrated. Strategies for using the specification curve in educational contexts to develop analytical plans, reflect on the generalizability of research findings, and highlight issues of replicability and publication bias are proposed.

Influential Factors and Spatiotemporal Characteristics of Carbon Intensity on Industrial Sectors in China.

Based on the extended STIRPAT model and panel data from 2005 to 2015 in 20 industrial sectors, this study investigates the influential factors of carbon intensity, including employee, industry added value, fixed-assets investment, coal consumption, and resource tax. Meanwhile, by expanding the spatial weight matrix and using the Spatial Durbin Model, we reveal the spatiotemporal characteristics of carbon intensity. The results indicate that Manufacturing of Oil Processing and Coking Processing (S7), Manufacturing of Non-metal Products (S10), Smelting and Rolling Process of Metal (S11), and Electricity, Gas, Water, Sewage Treatment, Waste and Remediation (S17) contribute most to carbon intensity in China. The carbon intensity of 20 industrial sectors presents a spatial agglomeration characteristic. Meanwhile, industry added value inhibits the carbon intensity; however, employee, coal consumption, and resource tax promote carbon intensity. Finally, coal consumption appears to have spillover effects, and the employee has an insignificant impact on the carbon intensity of industrial sectors.

Spatial Regression Model with Optimum Spatial Weighting Matrix on GRDP Data of Sulawesi Island

The two main spatial regression models are the spatial autoregressive model (SAR) and spatial error model (SEM). The extension of the SAR model is a spatial Durbin model (SDM), which considers the spatial dependence of response and explanatory variables. However, the determination of the spatial weight matrix is critical for the best estimation results. We consider two distance-based spatial weight matrices, i.e., the k-Nearest Neighbour (k-NN) and Inverse Distance Weighting (IDW). The objective of this study was to compare the performance of the Ordinary Least Squares (OLS) regression, SAR, SEM, and SDM models with k-NN and IDW on the estimation of Growth Regional Domestic Product (GRDP) and identify the critical factors that influence the value of GRDP of Sulawesi island. The study used the GRDP data of 81 districts/cities in Sulawesi island in 2018 with six explanatory variables. The results show that the 4-NN weighted SAR model outperforms the OLS, the 4-NN SEM, SDM models, IDW SAR, SEM, and SDM models. The factors that influence the value of GRDP in Sulawesi island are HDI (Human Development Index), population size, open unemployment, small/micro and medium industries, and the spatial lag autoregressive coefficient.

Emphasising spatial structure in geosocial media data using spatial amplifier filtering

In this article, a new method called spatial amplifier filtering is proposed. The presented method is related to Moran eigenvector filtering and allows the accentuation of spatial structures in heterogeneous data sets. The spatial amplifier filtering technique is based on the inclusion of certain eigenvectors of a spatial weights matrix into a regression model. The application of this method can be seen as a pre-processing step prior to subsequent analyses, and to separate different types of spatially correlated components in a data set. For this purpose, three different types of the so-called spatial amplifiers are proposed, each consisting of different subsets of eigenvectors of the weights matrix. These amplifiers can either emphasise the positive or negative spatial autocorrelation, or spatial structuring in general. In this way, it is possible to make desired spatial structures more visible, especially in spatially highly mixed data sets, whereby the focus here is on geosocial media data. In the empirical part of the article, it is first shown why georeferenced social media data are difficult to handle from a spatial analysis perspective, motivating the need for the method proposed. Subsequently, the technique of amplifier filtering is applied to two data sets: a census data set from Brazil and Twitter data from London. The results obtained show that the method is capable of strengthening existing spatial structures and mitigating potentially disturbing spatial randomness patterns and other nuisances. This facilitates the interpretation especially of the Twitter data used. While the analysis of the unfiltered Twitter data with established methods reveals little information about possible spatial structures in the tweets, the filtered data offer a much clearer picture with distinguishable clusters. In addition, the method also provides insights into the internal irregularity of spatial clusters and thus complements the toolbox for investigating spatial heterogeneity.

Modelling COVID-19 growth cases of provinces in java Island by modified spatial weight matrix GSTAR through railroad passenger's mobility

sThe movement of positive people Coronavirus Disease that was discovered in 2019 (Covid-19), written 2019-nCoV, from one location to another has a great opportunity to transmit the virus to more people. High-risk locations for transmission of the virus are public transportations, one of which is the train, because many people take turns in or together inside. One of the policies of the government is physical distancing, then followed by large-scale social restrictions. The keys to the policy are distance and movement. The most famous transportation used for the movement of people among provinces on Java is train. Here a Generalized Space Time Autoregressive (GSTAR) model is applied to forecast infected case of 2019-nCoV for 6 provinces in Java. The specialty of this model is the weight matrix as a tool to see spatial dependence. Here, the modified Inverse Distance Weight matrix is proposed as a combination of the population ratio factor with the average distance of an inter-provincial train on the island of Java. The GSTAR model (1; 1) can capture the pattern of daily cases increase in 2019-nCoV, evidenced by representative results, especially in East Java, where the increase in cases is strongly influenced by other provinces on the island of Java. Based on the Mean Squares of Residuals, it is obtained that the modified matrix gives better result in both estimating (in-sample) and forecasting (out-sample) compare with the ordinary matrix.

Impact of technological progress on industrial structure upgrading based on spatial panel measurement model in Beijing-Tianjin-Hebei region in China

Technological progress is the engine for a country’s long-term stable economic growth. Since China has put forward the development requirements of finding a new driving force for economic growth, optimizing industrial structure and improving the quality of economic development, technological progress has become the key object of economic research. Based on the spatial autoregression model, this paper constructs a spatial weight matrix from three perspectives: geographical distance, economic distance, and comprehensive economic geographical distance, and took 13 cities in Beijing-Tianjin-Hebei region as the research objects to analyze the impact of technological progress on the upgrading of regional industrial structure and the spatial effect from 2010 to 2017. The results show that there is an inverted “U” relationship between the technological progress and the upgrading of industrial structure in the Beijing-Tianjin-Hebei region. The empirical results indicate that technological progress can significantly promote the upgrading of industrial structure in the region, but has no significant impact on the upgrading of industrial structure in the surrounding areas.

Directional spatial autoregressive dependence in the conditional first- and second-order moments

In contrast to classical econometric approaches which are based on prespecified isotropic weighting schemes, we suggest that the spatial weighting matrix in the presence of directional dependencies should be estimated. We identify this direction based on different candidate neighbourhood sets. In this paper, we consider two different types of processes – namely spatial autoregressive and spatial autoregressive conditional heteroscedastic processes – and derive the consistency of the corresponding maximum likelihood estimates in the presence of directional dependencies. Moreover, Monte Carlo simulation results indicate that the model’s performance improves with sample size and with smaller neighbourhood subset sizes. Finally, we apply this approach to aerosol observations over the North Atlantic region and show that their spatial dependence matches the direction of the trade winds in this area.

A Domino Theory of Regionalism Revisited: The Role of Homogeneity/Heterogeneity in Institutions

This paper proposes that country-pairs sharing homogeneity in domestic institutions tend to sign preferential trade agreements (PTAs) interdependently, and such an interdependence effect decreases with institutional differential. Using spatial econometrics, we take democracy and economic freedom as fundamental components of the spatial weight matrix. The baseline results support our hypothesis from both the panel data over 1996 to 2017 through a probit model and the cross-sectional data through a spatial autoregressive probit model. We also employ a compound spatial weight matrix to account for the institutional differential and geographical distance simultaneously, which confirms the role of domestic institutions in the evolution of PTAs. Our findings are robust to the correction of potential endogeneity in the spatial weight matrix, and we provide not only novel evidence for the spatial effect of institutional differential on the domino-like spread of PTAs, but also some insights on the relationship between regionalism and multilateralism.

Industrial Efficiency Algorithm Based on Spatio‐Temporal‐Data‐Driven

Stochastic frontier model is an important and effective method to calculate industry efficiency. However, when dealing with temporal and spatial data from the industry, it is difficult to accurately calculate the industrial production efficiency due to the influence of spatial correlation and time lag effect. If the traditional spatial statistical method is used, the setting method of spatial weight matrix is often questioned. To solve this series of problems, one possible idea is to design a spatial data mining process based on stochastic frontier analysis. Firstly, the stochastic frontier model should be improved to analyze spatio‐temporal data. In order to accurately measure the technical efficiency in the case of dual correlation between time and space, a more effective spatio‐temporal stochastic frontier model method is proposed. Meanwhile, based on the idea of generalized moment estimation, an estimation method of spatiotemporal stochastic frontier model is designed, and the consistency of estimators is proved. In order to ensure that the most appropriate spatial weight matrix can be selected in the process of model construction, the K‐fold crossvalidation method is adopted to evaluate the prediction effect under the data‐driven idea. This set of spatio‐temporal data mining methods will be used to measure the technical efficiency of high‐tech industries in various provinces of China.

Local Semantic Enhanced ConvNet for Aerial Scene Recognition.

Aerial scene recognition is challenging due to the complicated object distribution and spatial arrangement in a large-scale aerial image. Recent studies attempt to explore the local semantic representation capability of deep learning models, but how to exactly perceive the key local regions remains to be handled. In this paper, we present a local semantic enhanced ConvNet (LSE-Net) for aerial scene recognition, which mimics the human visual perception of key local regions in aerial scenes, in the hope of building a discriminative local semantic representation. Our LSE-Net consists of a context enhanced convolutional feature extractor, a local semantic perception module and a classification layer. Firstly, we design a multi-scale dilated convolution operators to fuse multi-level and multi-scale convolutional features in a trainable manner in order to fully receive the local feature responses in an aerial scene. Then, these features are fed into our two-branch local semantic perception module. In this module, we design a context-aware class peak response (CACPR) measurement to precisely depict the visual impulse of key local regions and the corresponding context information. Also, a spatial attention weight matrix is extracted to describe the importance of each key local region for the aerial scene. Finally, the refined class confidence maps are fed into the classification layer. Exhaustive experiments on three aerial scene classification benchmarks indicate that our LSE-Net achieves the state-of-the-art performance, which validates the effectiveness of our local semantic perception module and CACPR measurement.

Determinants of Fertility Trend at District, Divisional and Regional Levels and Policy Implication for Poverty Alleviation

Bangladesh is one of the most densely populated countries of the world (1278 persons per sq. km.) but it represents one of the few countries in developing world where the pace of fertility decline has been unparalleled over the last few decades despite pervasive poverty and under-development. It has been passing through a critical phase of fertility transition. The data was collected for this research from the following five sources: i. Bangladesh Multiple Indicator Cluster Survey (MICS) 2012-13 for fertility rate, contraceptive use, proportion of children engaged in work and access to mass media and ICT of women. ii. Statistical Yearbook of Bangladesh for literacy rate, iii. Population Census 2011 for population density characteristics of different districts, iv. Directorate of Agricultural Extension personnel for daily average wage rate of agricultural labor and v. Bangladesh Poverty Map, 2014 for the level of poverty of different districts. This study contributes to basic knowledge of the structural determinants of fertility rate in the Bangladesh by analyzing the expanded set of determinants at district level, namely contraceptive use, literacy rate, daily wage rate of agricultural labor, population density, child labor, access to mass media and ICT of women and poverty level using spatial data analysis techniques. In this study, the binary join matrix/binary contiguity matrix and inverse distance spatial weight matrix techniques are used to capture spatial dependence in the data. This analysis extends the spatial model by allowing spatial dependence to vary across divisions and regions. The results strongly indicate that the fertility associated with contraceptive use, literacy rate, daily wage rate of agricultural labor, child labor, poverty level and their neighbors’ districts. These results suggest that district should pay particular attention to policies in neighboring districts and policy maker should realize that declining the fertility rate in neighboring districts are likely to affect fertility rate in their own district, therefore, a key issue for policy development is how to stimulate the contraceptive use, educational attainment, promote daily wage of agricultural labor and decrease child labor and poverty level could control the fertility and result in sustainable development and poverty alleviation of regions that are both high fertility and economically lagging. This needs to be addressed both in terms of national level policies and more emphatically within regional and sub-regional development strategies than it has been hither.

PENGEMBANGAN MODEL PERAMALAN SPACE TIME

Based on Statistics Indonesia (BPS) South Sulawesi is one of the national rice granary province. There are three regions, Bone, Wajo, and Gowa that contribute to the high production of rice in South Sulawesi. However, rice production in Indonesia especially South Sulawesi often declined sharply due to climate disturbances, such as drought or flood. Therefore, Indonesia's government should provide a forecast related to rice production accurately to ensure the availability of food stocks as an integral part of national food security. Moreover, rainfall as climate factors should be included to produce an appropriate forecast model that can be expected to generate the estimation of the rice production data accurately. This research focused on comparing the forecasting model of rice production data by SARIMAX and GSTARIMAX model and used rainfall as explanatory variables. The SARIMAX model is a multivariate time series forecasting model that can accommodate the seasonal components. In contrast, the GSTARIMAX model, which is equipped with an inverse distance spatial weight matrix, is a space-time forecasting model that involves interconnection between locations. The GSTARIMAX model built for rice production forecasting in Bone, Wajo, and Gowa is GSTARIMAX (2,1,0)(0,1,1)12. Rainfall as an explanatory variable was significant at each location. The comparison of rice production forecasting models for the next six periods in four locations showed that the GSTARIMAX model provided more stable forecasting results than the SARIMAX model, viewed from the average MAPE value of the GSTARIMAX mode in each location.

Effect of Complex Road Networks on Intensive Land Use in China’s Beijing-Tianjin-Hebei Urban Agglomeration

Coupled with rapid urbanization and urban expansion, the spatial relationship between transportation development and land use has gained growing interest among researchers and policy makers. In this paper, a complex network model and land use intensity assessment were integrated into a spatial econometric model to explore the spatial spillover effect of the road network on intensive land use patterns in China’s Beijing–Tianjin–Hebei (BTH) urban agglomeration. First, population density, point of interest (POI) density, and aggregation index were selected to measure land use intensity from social, physical, and ecological aspects. Then, the indicator of average degree (i.e., connections between counties) was used to measure the characteristics of the road network. Under the hypothesis that the road network functions in shaping land use patterns, a spatial econometric model with the road network embedded spatial weight matrix was established. Our results revealed that, while the land use intensity in the BTH urban agglomeration increased from 2010 to 2015, the road network became increasingly complex with greater spatial heterogeneity. The spatial lag coefficients of land use intensity were positively significant in both years and showed a declining trend. The spatially lagged effects of sector structure, fixed asset investment, and consumption were also significant in most of our spatial econometric models, and their contributions to the total spillover effect increased from 2010 to 2015. This study contributes to the literature by providing an innovative quantitative method to analyze the spatial spillover effect of the road network on intensive land use. We suggest that the spatial spillover effect of the road network could be strengthened in the urban–rural interface areas by improving accessibility and promoting population, resource, and technology flows.

Identification of dominant features in spatial data

Dominant features of spatial data are connected structures or patterns that emerge from location-based variation and manifest at specific scales or resolutions. To identify dominant features, we propose a sequential application of multiresolution decomposition and variogram function estimation. Multiresolution decomposition separates data into additive components, and in this way enables the recognition of their dominant features. A dedicated multiresolution decomposition method is developed for arbitrary gridded spatial data, where the underlying model includes a precision and spatial-weight matrix to capture spatial correlation. The data are separated into their components by smoothing on different scales, such that larger scales have longer spatial correlation ranges. Moreover, our model can handle missing values, which is often useful in applications. Variogram function estimation can be used to describe properties in spatial data. Such functions are therefore estimated for each component to determine its effective range, which assesses the width-extent of the dominant feature. Finally, Bayesian analysis enables the inference of identified dominant features and to judge whether these are credibly different. The efficient implementation of the method relies mainly on a sparse-matrix data structure and algorithms. By applying the method to simulated data we demonstrate its applicability and theoretical soundness. In disciplines that use spatial data, this method can lead to new insights, as we exemplify by identifying the dominant features in a forest dataset. In that application, the width-extents of the dominant features have an ecological interpretation, namely the species interaction range, and their estimates support the derivation of ecosystem properties such as biodiversity indices.

PENENTUAN FAKTOR-FAKTOR POTENSIAL YANG MEMPENGARUHI KEJADIAN MALARIA DI PROVINSI PAPUA DENGAN EPIDEMIOLOGI SPASIAL

In Indonesia malaria is found to be widespread in all islands with varying degrees and severity of infection. Based on the Annual of Parasite Incidence (API) in Eastern Indonesia, Malaria is a disease that has a high incidence rate. The three provinces with the highest APIs are Papua (42.64%), West Papua (38.44%) and East Nusa Tenggara (16.37%). Spatial aspects are considered important to be studied because the spread of disease through mosquitoes is strongly influenced by fluctuating climate. The purpose of this study is to determine the potential factors that influence the incidence of Malaria disease in the province of Papua in 2013 by looking at aspects that are the focus of attention in spatial epidemiology. The methods used in analyzing the area are Simultaneous Autoregressive (SAR) and Conditional Autoregressive (CAR) models with a spatial weighting matrix up to second order. The result shows the average monthly wind velocity, average monthly rainfall, and malaria treatment with government program drugs by getting ACT drugs are substantial factors in determining the incidence number of Malaria in Papua based on the lowest AIC value for the second-order of CAR model. While the SAR model, in this case, has no spatial influence. By knowing the potential factors that influence the incidence of malaria, the Papua Province through the Health Office can take more effective preventive measures to reduce the number of malaria incidents.

Bias from Network Misspecification Under Spatial Dependence

AbstractThe prespecification of the network is one of the biggest hurdles for applied researchers in undertaking spatial analysis. In this letter, we demonstrate two results. First, we derive bounds for the bias in nonspatial models with omitted spatially-lagged predictors or outcomes. These bias expressions can be obtained without prior knowledge of the network, and are more informative than familiar omitted variable bias formulas. Second, we derive bounds for the bias in spatial econometric models with nondifferential error in the specification of the weights matrix. Under these conditions, we demonstrate that an omitted spatial input is the limit condition of including a misspecificed spatial weights matrix. Simulated experiments further demonstrate that spatial models with a misspecified weights matrix weakly dominate nonspatial models. Our results imply that, where cross-sectional dependence is presumed, researchers should pursue spatial analysis even with limited information on network ties.

Spatial dependence of family farms’ adoption behavior of green control techniques in China

ABSTRACT Based on field survey data from 443 family farms in Shandong and Henan Provinces, the green control techniques(GCT) adoption behavior of family farms was measured in terms of adoption or non-adoption. Based on the global Moran’s I test, the Bayesian spatial Durbin probit model(BSDPM) was constructed, the appropriate spatial weight matrix was set, the optimal model for parameter estimation was selected, and the direct and spatial spillover effects of family farm characteristics on GCTs adoption behavior of family farms were decomposed by means of the partial differential method. The results show that the GCTs adoption behaviors of adjacent family farmers are spatially correlated and strongest when they are within 2.0 km of each other. Farm leaders’ educational level, degree of risk preference, financial status, number of laborers, understanding of GCTs and of the dangers of chemical pesticides, knowledge of other GCT adopters, frequency of communication with neighbors, participation in technical training and the strength of media publicity have significantly positive effects on the GCT adoption behaviors of family farms, which are mainly influenced by the direct effects of characteristic variables. However, the spatial spillover effects of neighboring family farmers’ participation in technical training, number of laborers, and financial status cannot be ignored. This result provides not only theoretical support for the demonstration and extension of the effectiveness of GCTs but also a reference for the selection of family farms as model households.

Analysis of the atmospheric macro-physical using spatial methods

The central western area of Venezuela has an unequal distribution of precipitation. Due to its agricultural importance, is necessary to plan water accounting and this requires a evaluation of spatial and temporal variability of precipitation and an estimate of local geophysical effect from the relief. In this research we use an iterative computationally lattice approach to perform a confirmatory analysis of the variability and the spatial correlation structure in monthly precipitation stations. Spatial correlograms and pooled empirical semivariogram were applied to evaluate the most appropriate spatial weighting matrix to estimate the Moran’s I. The altitude effect over monthly rainfall was estimated through spatial regression algorithm which determine the predominant spatial process in each slice. A homogeneous spatial stochastic process with positive spatial autocorrelation is evidenced. There is a trend towards a higher frequency of spatial error and spatial auto-regressive processes between the months of June and August whilst there are not dominant process between October and December. This response is caused by the dynamics of the intertropical convergence zone, which generates a seasonal effect on precipitation. These estimations allows decision-making in modeling and will lead to an improvement for analysis and forecasting in areas strongly affected by climate change and water stress.

Spatial weight matrix in dimensionality reduction reconstruction for micro-electromechanical system-based photoacoustic microscopy

A micro-electromechanical system (MEMS) scanning mirror accelerates the raster scanning of optical-resolution photoacoustic microscopy (OR-PAM). However, the nonlinear tilt angular-voltage characteristic of a MEMS mirror introduces distortion into the maximum back-projection image. Moreover, the size of the airy disk, ultrasonic sensor properties, and thermal effects decrease the resolution. Thus, in this study, we proposed a spatial weight matrix (SWM) with a dimensionality reduction for image reconstruction. The three-layer SWM contains the invariable information of the system, which includes a spatial dependent distortion correction and 3D deconvolution. We employed an ordinal-valued Markov random field and the Harris Stephen algorithm, as well as a modified delay-and-sum method during a time reversal. The results from the experiments and a quantitative analysis demonstrate that images can be effectively reconstructed using an SWM; this is also true for severely distorted images. The index of the mutual information between the reference images and registered images was 70.33 times higher than the initial index, on average. Moreover, the peak signal-to-noise ratio was increased by 17.08% after 3D deconvolution. This accomplishment offers a practical approach to image reconstruction and a promising method to achieve a real-time distortion correction for MEMS-based OR-PAM.

Why Is Collaborative Agglomeration of Innovation so Important for Improving Regional Innovation Capabilities? A Perspective Based on Collaborative Agglomeration of Industry-University-Research Institution

Exploring the impact of collaborative agglomeration of industry-university-research institution innovation on regional innovation capabilities is of great significance for promoting China's high-quality economic development. This paper introduces the level of innovation collaborative agglomeration into the C-H production density model to theoretically explain the mechanism of the influence of innovation collaborative agglomeration on regional innovation capacity. On this basis, using the 2011–2017 Chinese subprovincial panel data to measure the level of regional innovation collaborative agglomeration and establishing a spatial model, the impact of innovation collaborative agglomeration on regional innovation capacity is empirically examined in two stages: knowledge innovation and outcome transformation. The study finds that the innovation collaborative agglomeration and the regional innovation capacity present a typical inverted U-shape relationship, while the human capital and the regional innovation capacity present an inverted N-shape relationship in the stage of knowledge innovation. There is a U-shaped relationship between the innovation collaborative agglomeration and the regional innovation capacity in the outcome transformation stage, while the impact of human capital on regional innovation capacity is not obvious. This result is still robust after replacing the core explanatory variables and the spatial weight matrix. In terms of three regions, the innovation collaborative agglomeration and the human capital in the middle and eastern regions have a stronger impact on regional innovation capacity than in the western region. The findings of this paper provide policy insights for the innovation collaborative agglomeration of industry, university, and research institution to promote regional innovation capacity.