Exploratory Data Analysis Methods Research Articles

Exploratory Data Analysis of Human Activity Recognition Based on Smart Phone

In a smart urban environment, providing accurate information about human activities is an important task. It is a trend to implement human activity recognition (HAR) algorithms and applications on smart phones, including health monitoring, self-management system, health tracking and so on. However, human activity recognition(HAR) is very complex, and it is important to use the best technology and machine learning to understand human activities. One of the main problems of the existing HAR strategies is that the classification accuracy is relatively low, and in order to improve the accuracy, it needs high computational overhead. The purpose of this paper is to use an exploratory data analysis method to deal with HAR, after the implementation of different data mining techniques, the results of dimensionality reduction and visualization are obtained. The HAR method based on smart phone and EDA proposed in this paper is a high precision method. Compared with other classifiers, its accuracy is 96.56%. This article will discuss computational prediction activities and the computational limitations of using exploratory data to analyze (EDA) on 564 feature data frames. The experimental results show that HAR-based exploratory data analysis is a common sensory signal processing technology. The GridSearchCV and LinearSVC algorithm can provide accurate automatic human activity recognition (HAR), for elderly and disabled patients in need of continuous care, and it is a decision-making tool to support sports coaches to make plans.

Exploratory Data Analysis for Social Big Data Using Regression and Recurrent Neural Networks

In general the health care system and hospital takes a major role in the service sector. The clinical treatment successfully increases in the year for the treatment of both the medical and the technical innovations. A sample theoretical challenge exists in the patient flow analysis for real data. An existing method target on the audience and less concentration on the secondary statistical analysis, where the data obtain from the hospital not suitable for the analysis. So this limitation can overcome using the Exploratory Data Analysis (EDA), which helps in analysis of the patients flow in large hospital. The proposed frame work uses a machine learning method for the data classification processes. The feature extraction processes for the patient data applied for the larger hospital dataset and the individual hospital data. Some similar features are allowed to train over the Recurrent Neural Network (RNN) classifier for data modeling using the large hospital dataset. The output of the classifier has the specific details about the patients taken for the EDA method. The linear regression algorithm be the one kind of statistical tool for predicting the relationship between the variables. The proposed frame work is implemented using Mat lab R2014a software and the results were simulated. The relationship between the patient details and hospital information shows the status of the hospital as healthy and un healthy status.

Spatio-temporal trends and influencing factors of PM2.5 concentrations in urban agglomerations in China between 2000 and 2016.

An urban agglomeration (UA), similar to a megalopolis or a metropolitan area, is a region where cities and people are concentrated, and where air pollution has adversely impacted on sustainable and high quality development. Studies on the spatio-temporal trends and the factors which influence PM2.5 concentrations may be used as a reference to support air pollution control policy for major UAs throughout the world. Nineteen UAs in China covering the years 2000-2016 were chosen as the research object, the PM2.5 concentrations being used to reflect air pollution and being estimated from analysis of remote sensing images. The Exploratory Spatial Data Analysis method was used to study the spatio-temporal trends for PM2.5 concentrations, and the Geodetector method was used to examine the factors influencing the PM2.5 concentrations. The results revealed that (i) the temporal trend for the average values of the PM2.5 concentrations in the UAs followed an inverted U-shaped curve and the inflection points of the curve occurred in 2007. (ii) The PM2.5 concentrations in the UAs exhibited significant global spatial autocorrelation with the high-high type and the low-low type being the main categories. (iii) The rate of land urbanization and the structure of energy consumption were the main factors which influenced the PM2.5 concentrations in the UAs.

Research on coupling coordination and spatial differentiation of new-type urbanization and ecological environment in Wanjiang demonstration area

This study discussed the interaction mechanism of new-type urbanization and ecological environment, and then relevant data of the nine cities in Wanjiang Demonstration Area in 2016 was gotten. With the entropy weight method, the coupling coordination degree model and the exploratory spatial data analysis method, the coupling coordination degree and spatial agglomeration characteristics of new-type urbanization and ecological environment in Wanjiang Demonstration Area were analyzed. The results show that: Firstly, the comprehensive development index of the new-type urbanization and the ecological environment shows obvious positive correlation, and it appears a higher internal consistency through the whole development stage. The coupling degree of the nine cities is in a relatively balanced situation, Hefei and Ma'anshan are in the high level coupling stage, Xuancheng is in the antagonistic stage, and the other six cities are all in the stage of grinding. Secondly, the coupling coordination degree of new-type urbanization and ecological environment shows a weak positive correlation in space, and the degree of spatial agglomeration is not very significant. Except for Hefei with the over harmonization condition, the other eight cities are in the situation of maladjustment. Thirdly, according to the spatial agglomeration analysis, the local spatial autocorrelation of the coupling coordination degree is obvious. Although the different agglomeration regions show some expansion or contraction in the spatial evolution, cities with high degree of coupling coordination are clustered, while cities with low degree of coupling coordination are dispersed.

Discovering the Unexpected & Explicable: Scientific Reasoning and Research Design for Spatial Data Analysis

Abstract. Methods for exploratory data analysis and exploratory spatial data analysis (or ESDA) are useful to identify outliers, clusters, skewed distributions and correlations (see Figure 1 for examples implemented in our GeoDa software). Researchers routinely use these methods to find insights.However, what motivated this project is that, by default, it is easier to find insights that confirm the expected. Often, in fields like geography, statistics or computer science, available software and data drive the choice of research questions and the process of how we explore data. Typical insights gained this way are descriptive, like where a cluster is located or whether variables are correlated.To be sure, expected insights are reassuring. And descriptive insights are important. But instead of stopping here we want to build on them to also find insights that are new and relevant – we want to discover the unexpected (Anselin, 1998; Kielman & May, 2009). And, while we do need to know where clusters and outliers are – as researchers, we also want to go further and explain why these patterns exist (Good, 1983). In this project we presume that there are ways for structuring the process of data exploration that make it more likely to discover unexpected and explanatory insights (Platt, 1964).This presentation summarizes results from a summer 2020 lab where we started experimenting with how to do this using our Center’s GeoDa software. The summer lab was directed by Julia Koschinsky of the University of Chicago’s Center for Spatial Data Science. Marcos Falcone helped mentor five young University of Chicago and high school students for 7–10 weeks (majoring in statistics, computation, geography, political science, and economics).Our approach was to draw on philosophy of science and scientific reasoning to understand how the discovery of unexpected and explicable insights can work. We then tried to translate this to research designs for ESDA. Finally, we implemented the designs in replicable prototype examples for teaching and learning spatial research at the undergraduate or high school level.For instance, in terms of scientific reasoning, classic work on causal explanations (Mill, 1843) augments the typical current focus on correlations by also highlighting the need to assess the plausibility of your own explanation versus alternatives. This requires a mindset and practice of rigorously testing how our explanations might be wrong (Popper, 1959) rather than confirming that they're right (Nuzzo, 2015; Kahneman, 2011). To do this requires an iterative exchange between data and explanations – referred to as abductive reasoning, as it combines inductive and deductive approaches (Peirce, 1878; Heckman and Singer, 2017). We used Sherlock Holmes stories and the famous John Snow cholera case to illustrate the structure of these scientific reasoning concepts for a high school context (Coleman, 2019; cf Konnikova, 2013; Vinten-Johansen, 2020).Scientific reasoning goes back hundreds of years. Our challenge this summer and from here on has been to translate this reasoning to research designs that are applicable to modern interactive ESDA tools. Each of us developed four prototype resources for teaching and learning ESDA in GeoDa that we will develop further (Fig. 2): 1) protocols for how this could be done; 2) case examples to apply and revise the protocol; 3) GeoDa demo scripts to make the examples replicable; and 4) cleaned data and documentation. These resources will be released as part of a GeoDa Cookbook in the near future.Fig. 3 illustrates one of the protocols that differs from how ESDA is typically navigated. The starting point is the exploration of patterns in the outcome variable of interest. Next is the formulation of alternative explanations whose patterns plausibly match those of the outcome variable. Then we draw on quasi-experimental research designs to structure the testing of this match (Shadish et al., 2002). Finally, data about the hypothesized explanations are analyzed with ESDA and regressions to test or reformulate the hypotheses as part of an abductive process.

TIPOLOGIA DE CLUSTER NA PRODUÇÃO DA TILÁPIA: UM ESTUDO PARA O ESTADO DO PARANÁ

Este artigo tem por objetivo analisar a distribuição espacial da produção de tilápia no Estado do Paraná no período de 2014 e 2017. Assim, buscou-se identificar algum tipo de regularidade espacial (clusters). Para a pesquisa, 333 municípios paranaenses, de um total de 399 existentes, foram selecionados em consequência do volume de produção do pescado. Quanto à análise dos dados foi utilizado o método de Análise Exploratória dos Dados Espaciais (AEDE), o qual se refere a um conjunto de técnicas que apresenta e permite visualizar distribuições espaciais, padrões de associação espacial, e outras formas de heterogeneidade espacial. Os resultados da análise espacial detectaram autocorrelação positiva entre a produção e os vínculos ativos na região Oeste Paranaense. A técnica de mapeamento (formação de clusters) permitiu a localização geográfica das áreas onde se destacam as regularidades espaciais das aglomerações produtivas.DOI: 10.21714/2178-8030gep.v.21.6588

Approach to Urban Environmental Justice Using Exploratory Spatial Data Analysis. The Case of Valencia’s Monumental Trees

Environmental justice has been a relevant object of analysis in recent decades. The generation of patterns in the spatial distribution of urban trees has been a widely addressed issue in the literature. However, the spatial distribution of monumental trees still constitutes an unknown object of study. The aim of this paper was to analyse the spatial distribution of the monumental-tree heritage in the city of Valencia, using Exploratory Spatial Data Analysis (ESDA) methods, in relation to different population groups and to discuss some implications in terms of environmental justice, from the public-policy perspective. The results show that monumental trees are spatially concentrated in high-income neighbourhoods, and this fact represents an indicator of environmental inequality. This diagnosis can provide support for decision-making on this matter.

Exploratory data analysis on large data sets: The example of salary variation in Spanish Social Security Data

New challenges arise in data visualization when the research involves a sizable database. With many data points, classical scatterplots are non-informative due to the cluttering of points. On the contrary, simple plots, such as the boxplot that are of limited use in small samples, offer great potential to facilitate group comparison in the case of an extensive sample. This article presents exploratory data analysis methods useful for inspecting variation across groups in crucial variables and detecting heterogeneity. The exploratory data analysis methods (introduced by Tukey in his seminal book of 1977) encompass a set of statistical tools aimed to extract information from data using simple graphical tools. In this article, some of the exploratory data analysis methods like the boxplot and scatterplot are revisited and enhanced using modern graphical computational devices (as, for example, the heat-map) and their use illustrated with Spanish Social Security data. We explore how earnings vary across several factors like age, gender, type of occupation, and contract, and in particular, the gender gap in salaries is visualized in various dimensions relating to the type of occupation. The exploratory data analysis methods are also applied to assessing and refining competing regressions by plotting residuals-versus-fitted values. The methods discussed should be useful to researchers to assess heterogeneity in data, across-group variation, and classical diagnostic plots of residuals from alternative models fits. JEL CLASSIFICATION: C55; J01; J08; Y10; C80

The potential of heat release rate and cylinder pressure feedback control for conventional and premixed charge compression ignition combustion

Cylinder pressure or heat release rate tracking algorithms for direct-injected compression ignition engines allow to account for various disturbances, such as changes in the fuel characteristics or the effects of engine wear. They are suited for flex-fuel engines and allow the use of multi-injection strategies; hence, they show potential for mobile applications. However, they are based on the assumption that the heat release rate or cylinder pressure correlates well with engine efficiency and emissions and therefore allows a clean and efficient engine operation. With the objective to evaluate the potential of such tracking algorithms, this assumption is investigated for conventional as well as for low-temperature combustion strategies with diesel as fuel of choice. Based on experimental data, exploratory data analysis methods are applied to evaluate how sensitive engine efficiency and emissions are to changes of the heat release rate or cylinder pressure. Furthermore, an extended tracking algorithm is proposed, which can be applied for premixed charge compression ignition combustion concepts.

An Empirical Study on the Ecological Economy of the Huai River in China

The Huai River is an important flood control and discharge river in the middle and east of China, and the development of ecological economy with regional advantages is significant for the protection and improvement of the resources and environment of the basin. On the basis of defining the connotation of an ecological economic system, this study constructed an index system, and it applied the methods of data envelopment analysis (DEA) and exploratory spatial data analysis (ESDA) to study the ecological economy of the Huai River. This study concluded that (1) the efficiency in most areas was efficient, but inefficient in a few areas; (2) the causes of inefficiency were unreasonable production scale and unqualified production technology, which led to redundant input of resources, insufficient output of days with good air quality, and excessive output of particulate matter with less than or equal to 2.5 microns in diameter (PM2.5); and (3) the efficiency was different in different regions, so it was necessary to respectively formulate and implement strategies for protection and development of resources and environment. The research results can be used as an important reference for formulating ecological economic policies around the world.

Articulating strategies to address heat resilience using spatial optimization and temporal analysis of utility assistance data of the Salvation Army Metro Phoenix

Long-term community resilience, which privileges a long-view look at chronic, slow-moving issues affecting communities, has begun to draw more attention from researchers and policymakers. In the Valley of the Sun, resilience to heat is both a necessity and a way of life. Solutions are ubiquitous but nevertheless still in demand over the long, hot summers in the Phoenix, Arizona metropolitan area. Residents heavily rely on air conditioning (AC) for relief from heat stress, illness, and to prevent indoor heat-related deaths. However, paying for the electricity to keep homes cool can be expensive and the electric bills can be cost prohibitive for many low-income individuals and families. Local government agencies, non-governmental organizations (NGOs), and charitable organizations have programs that provide financial assistance for qualified applicants offering limited relief from electricity costs. To better understand the utility assistance landscape in the Phoenix metropolitan area as a contributor to heat resilience among vulnerable communities, we created a collaborative team of individuals from the university and the Salvation Army, one of the more than 80 organizations that provides emergency economic aid for low-income families to pay high-cost electricity bills, to articulate insights about systemic efficiencies and efficacies, from a data-informed perspective. We utilized exploratory data analysis and advanced spatial analytical methods with the Salvation Army, to build a shared understanding of knowledge gaps and verified hunches. Our collaborative research confirms that minority groups (African American and Native American) disproportionately require assistance. Meanwhile, 30% of the travel time and distance to intake interviews could be saved by switching from zip code-based assignment systems to address-based assignment systems. Budgeting across empirically identified temporal patterns of need could offer resilience benefits to the most vulnerable. As a result of this community research partnership, data from the Salvation Army reveals the character and dimension of critical challenges within the utility assistance system as a whole, informs both immediate solutions and builds a knowledge base for transforming future operations for the organization, while it shapes broader conversations across the community of service providers about heat resilience in both spatial and temporal terms.

Spatial heterogeneity of factors influencing transportation CO2 emissions in Chinese cities: based on geographically weighted regression model

Based on the 2015 transportation CO2 emissions and economic and social data for the 286 cities in China, exploratory spatial data analysis (ESDA) method and a geographically weighted regression (GWR) model were used to analyze the spatial distribution characteristics of transportation CO2 emissions and their influencing factors. The results showed that the CO2 emissions from urban transportation in China featured significant spatial agglomeration. The high emission areas were mainly concentrated in Beijing, Shanghai, Chongqing, Chengdu, Nanjing, and other regional core cities, while the low emission areas were mainly concentrated in the cities of Gansu, Guizhou, Yunnan, and other underdeveloped provinces. Considering the overall evolution of the factors affecting transportation CO2 emissions, private car ownership, technological innovation, and industrial structure correlated positively with transportation CO2 emissions. Population density, urbanization rate, per capita urban road area, and transportation structure could significantly inhibit transportation CO2 emissions. The impact of per capita GDP, public transportation, and environmental regulation on transportation CO2 emissions was insignificant. From the perspective of spatial heterogeneity, there were significant regional differences in the impact of various factors on CO2 emissions. Private car ownership, technological innovation capacity, and per capita GDP all had a positive impact on transportation CO2 emissions. Urbanization rate, urban road area per capita, and transportation structure all had a negative impact on transportation CO2 emissions. Population density, industrial structure, public transportation, and environmental regulation all had a two-way impact on transportation CO2 emissions in different cities.

Application of exploratory and Spatial Data Analysis (SDA), singularity matrix analysis, and fractal models to delineate background of potentially toxic elements: A case study of Ahvaz, SW Iran

It is essential to study spatial distribution of Potentially Toxic Elements (PTEs) in surface soil, and separate PTEs geochemical background from their human sources, and also determine their correlation with other environmental factors in order to assess their impacts on humans, provide realistic environmental geochemical maps, and carry out soil management. For this purpose, this study was designed to investigate the surface soil in Ahvaz, Southwest of Iran. The applied methods were exploratory data analysis (EDA), including boxplot, Q-Q plot, probability plot (PP), concentration-area (C-A), number-size (N-S) fractal model, and singularity index (SI) model. The obtained results revealed that the fractal models led to a more realistic distinction among the geochemical population compared to the EDA methods such as probability plot. Furthermore, the C-A model was found to be more effective on the separation of subpopulation compared to the N-S and PP models. The studied elements exhibited a similar pattern implying that pollution is a function of geochemical dispersion regarding the surface soil in Ahvaz (Zn ≫ Pb > Cu > As). The studied metals-major elements plot also indicated that there was no meaningful relationship between Pb, Zn, Cu, and major elements in the study area. Plots of association of Pb, Zn, Cu, and As distinctly showed two general geogenic and anthropogenic populations. Moreover, the results of SI revealed that the highly contaminated area was consistent with the main defined hotspots and anthropogenic sources of elements as well as places affected by the contaminated area that have not been reported in previous studies. Furthermore, a combination of geochemical and geographical model comprising different statistical models was developed to more effectively separate geogenic from anthropogenic sources. Also, the geochemical background for the studied elements (Pb 180 mg/kg; Cu 200 mg/kg; Zn 90 mg/kg; As 65 mg/kg) was shown to be higher than the Iranian soil quality guideline with Pb, Zn, Cu, and As of 100, 80, 200, and 18 mg/kg, respectively.

The spatial-temporal variation and convergence of green innovation efficiency in the Yangtze River Economic Belt in China.

The improvement of green innovation efficiency (GIE) in the Yangtze river economic belt (YREB) is beneficial to China's green transformation and upgrading because of its economic and ecological position. Therefore, based on the slacks-based measure of super-efficiency (Super-SBM) model, the paper studies the GIE and its spatial-temporal variation characteristics in the YREB during the period 2003-2015, and analyzes the spatial correlation and spatial-temporal convergence of GIE with the exploratory spatial data analysis (ESDA) method and convergence analysis method. The results show that the GIE in the YREB shows an "U-shaped" change pattern in time and an extremely unbalanced development pattern in space. The areas with high GIE contribute to the improvement of overall GIE, whereas they do not exert a radiation and driving effect on areas with low GIE. Accordingly, because of the short board effect, the convergent speed of the GIE is decreasing. To be specific, the GIE keeps converging in the upper and lower reaches, except for the year 2010 when GIE in the middle reaches changed from being convergent to being non-convergent. Even though environmental policy exerts great impacts on the improvement of GIE, the lack of collaborative environmental governance leads to the non-convergent and unbalanced development of the GIE. Therefore, green coordinated development of the YREB is necessary.

Identification of ecosystem services supply and demand areas and simulation of ecosystem service flows in Shanghai

Abstract In this paper, ecosystem service flow was defined as a dynamic process in which the supply of ecosystem services in a certain region compensates for the demand in other regions with the remaining ecosystem services after meeting its own demand. This process follows the principle of minimum distance. We selected four typical ecosystem services: food supply (supply services), carbon absorption and oxygen release (regulation services), leisure and entertainment (cultural services) and biological habitat preservation (support services). According to the characteristics of the various ecological services, we adopted different methods and indicators to quantitatively evaluate the supply and demand situation and to identify the supply and demand areas of ecosystem services at the level of towns and streets. The Exploratory Spatial Data Analysis Method (ESDA) was used to reveal the spatial clustering models of the supplies and demands of various ecosystem services. A linear programming model was used to simulate the flow direction and flow rate of ecosystem service flow. The results showed the following: (1) All ecosystem services in Shanghai were in short supply and depended heavily on the supply of ecological services outside the city. (2) The supply of various ecosystem services presented the spatial characteristic of “central city - inner suburbs - outer suburbs” corresponding to “low-value clustering - discrete distribution - high-value clustering”. (3) The ecosystem services transferred to compensate Shanghai formed flow of the ecosystem service from the outer suburbs to the central urban area, which were dominated by carbon sequestration and oxygen release and the preservation of biological habitats. (4) Within the scope of Shanghai, the priority areas for ecological space conservation, restoration and expansion should be the central city and the inner suburbs, not just the outer suburbs.

Study on the spatial distribution characteristics of urban innovation power in Yangtze River Delta urban agglomeration

Based on the evaluation index panel data of innovation capacity of 26 cities in the Yangtze River Delta urban agglomeration from 2013 to 2017, the global entropy method and exploratory spatial data analysis method (ESDA) were used to evaluate and analyze the spatial characteristics of urban innovation capacity. The results show that: (1) From 2013 to 2017, the innovation capacity of the Yangtze River Delta urban agglomeration has an obvious regional difference, showing a distribution pattern of “Strong In The East And Weak In The West” on the whole. (2) From 2013 to 2017, the innovation capacity of the Yangtze River Delta urban agglomeration shows significant positive agglomeration characteristics in space. (3) The spatial agglomeration evolution of the innovation capacity of each city has obvious spatial locking characteristics. Based on the research conclusion, this paper puts forward some suggestions on strengthening urban innovation connection, improving innovation structure, increasing innovation investment and strengthening scientific and technological strength.

The Data Mining Group at University of Vienna

How can we extract meaningful knowledge from massive amounts of data? The data mining group at University of Vienna contributes novel methods for exploratory data analysis. Our main research focus is on unsupervised learning, where we want to identify any kind of non-random structure or patterns in the data without restricting ourselves to a pre-defined target variable or analysis goal. Our major lines of current research are clustering, causality detection and highly efficient exploratory data analysis on massive data. Besides that, we develop application-specific methods addressing specific challenges in biomedicine, neuroscience and environmental sciences. In teaching, we offer fundamental and advanced courses in data mining, machine learning and scientific data management for Bachelor and Master students of computer science and related programs.

Використання навчальних нейронних мереж для прогнозування подій у розробленні продукції IT

The state of development of innovations in Ukraine is characterized by an increase in development on the basis of start-up projects with the use as a project product of information systems of varying complexity. The article analyzes the weak survivability of the results of start-up projects. The conclusion on the need to predict the stages of develop-ment of IT project products based on the analysis of the processes of interaction of users (customers) with the infor-mation system (product). In this article, components of the model of forecasting of IT products development of innova-tive start-up projects are considered based on the analysis of formed datasets of the interactions of prospective clients. We offered the algorithm of formation of initial datasets based on Customer Journey Map (CJM), which are the tool of fixing of events of the interaction of clients with the system. Examples of models of analogues of clients' travel maps are given, which are the basis for recording and analyzing interactions. This fact is the basis for the formation of appropri-ate data sets of large dimension. As a mechanism for processing big data sets and building strategies for IT products development, it is proposed to use a learning neural network. Mathematical models for further modeling and analysis of the obtained results are built. We used a simple linear regression analysis to model the relationship between a single explanatory variable and a continuous response variable (dependent variable). An exploratory data analysis method was applied to the available data to find repetitive patterns and anomalies. In the course of the research, we construct-ed a model of linear regression implementation using the gradient optimisation approach. The linear models of the scikit-learn library for the regression task were also applied, and the stabilisation regression method was implemented. Modelling and analysis of the obtained results were carried out, which showed greater efficiency over the extended life cycle of IT project products

The financialization of single-family rental housing: An examination of real estate investment trusts’ ownership of single-family houses in the Atlanta metropolitan area

ABSTRACT Single-family rental housing (SFR) is becoming increasingly prevalent in suburban neighborhoods. Historically, small-scale investors have owned SFR, but since the 2008 housing crisis, it has become increasingly financialized—dominated by large, global investment firms. In the wake of the housing crisis, a new type of SFR investor emerged: the real estate investment trust (REIT). SFR REITs funnel large amounts of global capital into local housing markets. This paper presents an examination of the four largest publicly traded SFR REITs’ investments in the Atlanta metropolitan area. Using exploratory spatial data analysis methods, the study examines the intensity and locations of statistically significant spatial clusters of SFR owned by REITs. Then, a generalized linear mixed model is used to identify the property, neighborhood, and school district characteristics associated with houses owned by SFR REITs. Findings indicate that overall, houses owned by SFR REITs are highly spatially clustered in neighborhoods forming a U shape surrounding the city of Atlanta, and the locations of the spatial clusters vary for the four SFR REITs. Moreover, property, neighborhood, and school district characteristics differ among properties owned by each of the SFR REITs.

Sports Industry Agglomeration and Green Economic Growth—Empirical Research Based on Panel Data of 30 Provinces and Cities in China

Based on the panel data of 30 provinces and cities in China from 2008 to 2017, this paper empirically analyzes the current situation of the development of China’s sports industry and the impact of the concentration of sports industry on green economic growth by using the exploratory spatial data analysis method (ESDA), static panel model and dynamic panel model. The empirical results show that the sports industry in the southeast coastal provinces of China has a good foundation for concentration. The sports industry concentration in the provinces along the Yangtze River Economic Belt is in the stage of rapid development. The development of sports industry concentration has obvious positive spatial aggregation characteristics, and the spatial spillover effect is obvious; The concentration of sports industry has a significant influence on green economic growth, which expresses the “inverted U-type” relationship. There are significant differences in influence between regions, which show the greatest influence in the middle, followed by the east and the smallest in the west.