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.

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Early Works in Spatio-Temporal Mining

Temporal and Spatio-Temporal Data Mining ◽

10.4018/978-1-59904-387-6.ch006 ◽

2008 ◽

pp. 112-129 ◽

Cited By ~ 1

Author(s):

Wynne Hsu ◽

Mong Li Lee ◽

Junmei Wang

Keyword(s):

Data Mining ◽

Homeland Security ◽

Spatial Data ◽

Surveillance Systems ◽

Temporal Data Mining ◽

Temporal Data ◽

Monitoring Applications ◽

Spatio Temporal ◽

Mining Algorithms ◽

Transactional Data

Spatio-temporal data mining is an emerging area with increasing importance in a variety of applications, such as homeland security, mobile services, surveillance systems, and health monitoring applications. However, mining in spatio-temporal databases is still in its infancy. Existing work on spatio-temporal data mining has mainly focused on three types of patterns: evolution patterns of natural phenomena, frequent movements of objects over time, and space-time clusters. While there has been much research on association rule mining on transactional, spatial, and temporal data, there is little literature on finding interesting associations in spatio-temporal data. In this chapter, we introduce the early attempts at spatio-temporal data mining and review the techniques to discover various interesting spatio-temporal patterns. This is followed by a review of the traditional association rules mining algorithms and their variants on transactional data, temporal data, and spatial data.

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Estimation of technical efficiency of black cumin (Nigella sativa L.) farming in northwest Ethiopia: a stochastic frontier approach

Journal of Economic Structures ◽

10.1186/s40008-020-00198-1 ◽

2020 ◽

Vol 9 (1) ◽

Author(s):

Abebe Birara Dessie ◽

Tadie Mirie Abate ◽

Betelhem Tsedalu Adane ◽

Tiru Tesfa ◽

Shegaw Getu

Keyword(s):

Technical Efficiency ◽

Nigella Sativa ◽

Stochastic Frontier ◽

Market Price ◽

Northwest Ethiopia ◽

Flowering Plant ◽

Yield Potential ◽

Stochastic Frontier Model ◽

Black Cumin ◽

The Mean

Abstract Ethiopia is one of the east African countries which produce and exports various spices to other countries. Black cumin (Nigella sativa L.) is an important stiff annual flowering plant which mainly grows by producers for its seeds. An increasing demand of black cumin seed and oil in local, national and international market for medicinal, consumption and commercial purpose makes the best alternative crop for small holder farmers in Ethiopia. In spite of its importance, not much has been done to improve its production and productivity in Ethiopia. Therefore, this research was designed to examining efficiency variations and factors influencing technical inefficiency levels of producers on black cumin production in northwest Ethiopia. Primary data were collected using a semi-structured questionnaire administered on 188 black cumin producers selected using systematic random sampling technique. Moreover, various data analysis methods such as descriptive statistics and stochastic frontier model were used for analyzing the data. The empirical result obtained by applying maximum likelihood estimate of stochastic frontier model revealed that seed (p < 0.01) labor (p < 0.05), chemical (p < 0.01) and land (p < 0.05) were significant input variables in determining black cumin production. The mean technical efficiency level of black cumin producer was generally low, about 53.1%. The mean value of actual yield, potential yield and yield gap was 3.131, 5.832 and 2.701 quintals, respectively. Moreover, the result of stochastic frontier model together with the inefficiency parameters revealed that market price of black cumin (p < 0.01) and access of extension service (p < 0.1) were significant variables and positively influenced the efficiency levels of black cumin producers. Whereas age of producers (p < 0.05) and distance to farm plot (p < 0.01) negatively influenced the technical efficiency levels of black cumin producers. Therefore, the study recommends that adoption of latest agricultural technologies; development of institutions, agricultural extension services and infrastructure are advisable to improve the efficiency and commercial value of black cumin production.

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Comprehensive performance analysis of Spatio-Temporal Data Mining approach on multi-temporal coastal remote sensing datasets

2011 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2011.6049592 ◽

2011 ◽

Author(s):

Balakrishna Gokaraju ◽

Surya S. Durbha ◽

Roger L. King ◽

Nicolas H. Younan

Keyword(s):

Remote Sensing ◽

Data Mining ◽

Performance Analysis ◽

Temporal Data Mining ◽

Temporal Data ◽

Data Mining Approach ◽

Comprehensive Performance ◽

Multi Temporal ◽

Spatio Temporal

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Exploratory spatio-temporal data mining and visualization

Journal of Visual Languages & Computing ◽

10.1016/j.jvlc.2007.02.006 ◽

2007 ◽

Vol 18 (3) ◽

pp. 255-279 ◽

Cited By ~ 84

Author(s):

P. Compieta ◽

S. Di Martino ◽

M. Bertolotto ◽

F. Ferrucci ◽

T. Kechadi

Keyword(s):

Data Mining ◽

Temporal Data Mining ◽

Temporal Data ◽

Spatio Temporal

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Towards a predictive model for opal exploration using a spatio-temporal data mining approach

Australian Journal of Earth Sciences ◽

10.1080/08120099.2012.754793 ◽

2013 ◽

Vol 60 (2) ◽

pp. 217-229 ◽

Cited By ~ 9

Author(s):

A. S. Merdith ◽

T. C. W. Landgrebe ◽

A. Dutkiewicz ◽

R. D. Müller

Keyword(s):

Data Mining ◽

Predictive Model ◽

Temporal Data Mining ◽

Temporal Data ◽

Data Mining Approach ◽

Spatio Temporal

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A Survey on Spatio-temporal Data Analytics Systems

ACM Computing Surveys ◽

10.1145/3507904 ◽

2022 ◽

Author(s):

Md Mahbub Alam ◽

Luis Torgo ◽

Albert Bifet

Keyword(s):

Programming Languages ◽

Spatial Data ◽

Data Analytics ◽

Spatial Databases ◽

Location Based Services ◽

Temporal Data ◽

Wide Range ◽

Data Volume ◽

Spatio Temporal ◽

Gis Software

Due to the surge of spatio-temporal data volume, the popularity of location-based services and applications, and the importance of extracted knowledge from spatio-temporal data to solve a wide range of real-world problems, a plethora of research and development work has been done in the area of spatial and spatio-temporal data analytics in the past decade. The main goal of existing works was to develop algorithms and technologies to capture, store, manage, analyze, and visualize spatial or spatio-temporal data. The researchers have contributed either by adding spatio-temporal support with existing systems, by developing a new system from scratch, or by implementing algorithms for processing spatio-temporal data. The existing ecosystem of spatial and spatio-temporal data analytics systems can be categorized into three groups, (1) spatial databases (SQL and NoSQL), (2) big spatial data processing infrastructures, and (3) programming languages and GIS software. Since existing surveys mostly investigated infrastructures for processing big spatial data, this survey has explored the whole ecosystem of spatial and spatio-temporal analytics. This survey also portrays the importance and future of spatial and spatio-temporal data analytics.

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