A model for big spatial rural data infrastructure in Turkey: Sensor-driven and integrative approach

A Spatial Data Infrastructure (SDI) enables the effective spatial data flow between providers and users for their prospective land use analyses. The need for an SDI providing soil and land use inventories is crucial in order to optimize sustainable management of agricultural, meadow and forest lands. In an SDI where datasets are static, it is not possible to make quick decisions about land use. Therefore, SDIs must be enhanced with online data flow and the capabilities to store big volumes of data. This necessity brings the concepts of the Internet of Things (IoT) and Big Data (BD) into the discussion.Turkey needs to establish an SDI to monitor and manage its rural lands. Even though Turkish decision-makers and scientists have constructed a solid national SDI standardization, a conceptual model for rural areas has not been developed yet. In accordance with the international agreements, this model should adopt the INSPIRE Directive and Land Parcel Identification System (LPIS) standards. In order to manage rural lands in Turkey, there are several legislations which characterize the land use planning, land classification and restrictions. Especially, the Soil Protection and Land Use Law (SPLUL) enforces to use a lot and a variety of land use parameters that should be available in a big rural SDI. Moreover, this model should be enhanced with IoT, which enables to use of smart sensors to collect data for monitoring natural occurrences and other parameters that may help to classify lands.This study focuses on a conceptual model of a Turkish big rural SDI design that combines the sensor usage and attribute datasets for all sorts of rural lands. The article initially reviews Turkish rural reforms, current enterprises to a national SDI and sensor-driven agricultural monitoring. The suggested model integrates rural land use types, such as agricultural lands, meadowlands and forest lands. During the design process, available data sets and current legislation for Turkish rural lands are taken into consideration. This schema is associated with food security databases (organic and good farming practices), non-agricultural land use applications and local/European subsidies in order to monitor the agricultural parcels on which these practices are implemented. To provide a standard visualization of this conceptual schema, the Unified Modeling Language (UML) class diagrams are used and a supplementary data dictionary is prepared to make clear definitions of the attributes, data types and code lists used in the model.This conceptual model supports the LPIS, ISO 19156 International Standard (Geographic Information: Observations and Measurements) catalogue and INSPIRE data theme specifications due to the fact that Turkey is negotiating the accession to EU; however, it also provides a local understanding that enables to manage rural lands holistically for sustainable development goals. It suggests an expansion for the sensor variety of Turkish agricultural monitoring project (TARBIL) and it specifies a rural theme for Turkish National SDI (TUCBS).     

Aggregating the DEA prospect cross-efficiency with an application to state key laboratories in China

With growing investments in the state key laboratories (SKLs) of China, it is essential to assess the efficiency of the administrative departments regarding managing the SKLs of China. However, few studies have been conducted about the evaluation of the performance of Chinese administrative departments in managing SKLs. To fill this gap, this paper investigates the performance within 22 SKL administrative departments in China, based on a data envelopment analysis cross-efficiency prospect aggregation approach incorporating the risk preference of decision maker. The empirical results show the major findings: (a) serious imbalance exists regarding the investment of 22 SKL administrative departments; (b) there are great differences among the performance of 22 SKL administrative departments; (c) some SKL administrative departments face an insufficient situation regarding the outputs; (d) different risk attitudes of decision maker have the significant impact on the evaluation results of the 22 SKL administrative departments. Based on these findings, we provide several policy suggestions for the development of SKLs in China.     

Analyzing driving factors of land values in urban scale based on big data and non-linear machine learning techniques

Land value plays a vital role in the real estate market. It is a critical reference for urban planners to reallocate land resources and introduce valid policies. Studying the influential factors on land value can help better understand the spatial-temporal variation of land values and design effective control policies. This attracted a number of scholars to study the spatial and temporal relationships between land value and its possible influential factors from the perspective of macro and micro. However, the majority of the existing studies have the problems of linear assumption and multicollinearity in research models. Limited features and the lack of feature selection procedure are another two commonly seen limitations. To overcome the gaps, this paper adopts non-linear machine learning (ML) methods to investigate the influential factors on land values per square foot based on “big data” in New York City. More than one thousand potential factors are considered, covering from the land attribute, point of interest, demographics, housing, to economic, education, and social. They are further selected using a feature extraction model named Recursive Feature Elimination (RFE). Six ML algorithms, including Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Multi Linear Regression (MLR), Linear Support Vector Regression (SVR), Multilayer Perceptron (MLP) Regression, and K-Nearest Neighbor (KNN) Regression are evaluated and compared. The optimal one with an R-square value of 0.933 is used to calculate the feature importance further. Several important impact features are disclosed, including the number of newsstands, and the vacant housing percentage.     

Data-driven manufacturer-retailer collaboration under competition

This research employs game theoretic models to investigate how and when data-driven collaborations between manufacturers and retailers are beneficial. In the models, two symmetric retailers each offer two products from two different manufacturers. Each manufacturer may choose to collaborate with one or both retailers through data-driven initiatives in providing retail value to the consumers. The results show that the main incentive behind these collaborations arise from the efficient allocation of resources. Surprisingly, greater brand differentiation reduces profit margins and the incentive to collaborate. We also find that market leaders can endogenously arise through data-driven collaborations.     

The origins,development and future directions of data envelopment analysis approach in transportation systems

Over the last two decades, application of Data envelopment analysis (DEA) in transportation problems have gained considerable research attention. This paper presents a literature review and classification of the applications of DEA in transportation systems (TSs). First by classifying 40 papers from 2007 to 2018, the origins of DEA in transportation problems have been reviewed. Then the development and an overall view of DEA applications in TSs have been presented. We have classified the applications of DEA into six different contexts. In each context, published papers have deeply been analyzed. Content of analysis includes “Number of published papers during the time”, “target journals”, “countries”, “keyword frequency”, “most cited papers”, “map of most co-cited publications”. More important, we reported the “inputs and outputs variables” used in each paper. Further “a review of the selected papers” and “gaps/future research directions” have been given within each cluster. The results show that DEA is one of the most useful approach in evaluating TSs for policy makers. On the other hand, DEA can help the decision makers in transportation especially regarding environmental factors, sustainable development and eco-design. Finally, we proposed subjects for future researches including guidance for new studies in the field of DEA applications in TSs.     

Who is a tourist? Classifying international urban tourists using machine learning

A key issue in tourism management relates to the lack of consensus regarding a theoretical and practical definition of the term “tourist.” In turn, this results in a range of methods for counting tourists and measuring tourism. This paper presents a novel non-linear model for classifying international tourists in urban settings, based on machine learning classification methods. These methods utilize innovative feature engineering derived from photos posted on the Flickr social media platform combined with the specific urban destination street structure. The data science model that we developed for identifying international tourists produced an overall accuracy of 69% for Manhattan and 94% for Vienna and Prague, offering new tourism indicators such as repeat visits, travel distances, and short stays. The outcome of this study offers a better understanding of travel patterns among international tourists, which could improve international tourism management and promote a more practical and adaptable model for measuring and analyzing international tourism using machine learning and user-generated content.     

Big data analysis adaptation and enterprises’ competitive advantages: the perspective of dynamic capability and resource-based theories

Big data analysis (BDA) adaptation has been spreading unprecedentedly fast among Chinese enterprises to gain a competitive advantage. Based on the resource-based theory (RBT) and dynamic capability theory (DCT), this study aims to propose a conceptual model to identify the sources of competitive advantages, interrelationship of their components, and the mechanism of obtaining competitive advantage. The results reveal several important findings. 1) Different dimensions of dynamic capabilities all have effects on competitive advantage, but they have different paths to influence these. 2) Two dimensions of dynamic capabilities have direct effects on the competitive advantages, and strategy flexibility has indirect effects on these. 3) Three dimensions of resources all indirectly and positively influence competitive advantages by affecting dynamic capabilities. 4) The path of resources and dynamic capabilities affecting competitive advantage are identified. The findings have important implications for managers that leverage BDA to achieve comparative advantages in business.     

Big Data Algorithmic Trading Systems Based on Investors’ Mood

Traditional automated trading systems use rules and filters based on Chartism to send orders to the market, aiming to beat the market and obtain positive returns in bullish or bearish contexts. However, these systems do not consider the investors’ mood that many studies have demonstrated its effects over the evolution of financial markets. The authors describe 2 "big data" algorithmic trading systems over Ibex 35 future. These systems send orders to the market to open long or short positions, based on an artificial intelligence model that uses investors’ mood. To measure the investors' mood, the authors use semantic analysis algorithms that qualify as good, bad, or neutral any communication related to Ibex 35 made on social media (Twitter) or news media. After 1.5 years of research, conclusions are: First, the authors observe positive returns, demonstrating that investors’ mood has predictive capacity on the evolution of the Ibex 35. Second, these systems have beaten the Ibex 35 index, showing the imperfect efficiency of the financial markets. Third, big data algorithmic trading systems numbers are better in Sharpe ratio, success rate, and profit factor than traditional trading systems on the Ibex 35, listed in the Trading Motion platform.     

投资风格、意见分歧与股票流动性——来自股票网络社区数据挖掘的证据

文章挖掘网络平台上投资者发帖的文本数据并使用Python语言编写情感分析程序，在区分价值型投资和技术型投资风格的基础上，建立了投资者的群体内意见分歧、跨群体意见分歧指标，并分析意见分歧程度对股票流动性的影响效应。实证表明：不同投资风格的投资者群体内意见分歧的程度有显著差异，价值型投资者群内意见分歧程度要高于技术型投资者；投资者的群体内意见分歧和跨群体意见分歧都与个股流动性水平呈现显著的负相关关系，也即投资者对个股看法的对立程度越高则个股的流动性水平越低，而且两者对流动性的影响具有较强的独立性；进一步的，意见分歧对流动性的影响效应会受到卖空限制的影响，随着卖空限制的减轻，这种影响效应显著增强。这些结论表明，单纯增加信息量并提高信息传播效率，并不一定能完全改善股票流动性水平，投资者的意见分歧既是现实情境，又可能会导致股票流动性的异动，进而影响股票市场的稳定。     

Efficiency analysis of emergency departments in metropolitan areas

The demand in the healthcare industry is increasing exponentially due to aging population of the world and this is leading to a rapid increase in the cost of healthcare. The emergency departments of the hospitals are the frontline of health care systems and play an additional critical role in providing an efficient and high-quality response for patients. The overcrowding at the emergency departments due to growing demand results in a situation where the demand for ED services exceeds the ability to provide care in a reasonable amount of time. This has led countries to reconsider their health policies in a way to increase their efficiency in their healthcare systems in general and in emergency departments, in particular. As in many countries, there has been a steady and significant increase in the number of patients that seek health services at the emergency departments of state hospitals of Turkey, due to the significant structural reforms in health services since 2003. While meeting this increasing demand, it is ever more important to provide these critical health services efficiently. Therefore, the efficiency of the emergency departments of seven general hospitals run by Istanbul's Beyoglu State Hospitals Association have been analyzed using categorical Data Envelopment Analysis (DEA) models. The analysis of DEA results is supported by a set of statistical methods to make it easier for the hospital administrators to interpret the analysis and draw conclusions. The analysis shows that less-equipped EDs are supported by better equipped, larger EDs, resulting in a hub-and-spoke type of structure among the EDs where “satellite” EDs serve an important referral function and thus evaluating their efficiency without taking the interoperability among these units into account would not be an accurate assessment of their performance.