The varying patterns of rail transit ridership and their relationships with fine-scale built environment factors: Big data analytics from Guangzhou
الگوهای مختلف تفریحی حمل و نقل ریلی و روابط آنها با عوامل محیطی ساخته شده در مقیاس خوب: تجزیه و تحلیل داده های بزرگ از گوانگژو-2020
Investigating the varying ridership patterns of rail transit ridership and their influencing factors at the station level is essential for station planning, urban planning, and passenger flow management. Although many studies have investigated the associations between rail transit ridership and built environment, few studies combined spatial big data to characterize the built environment factors at a fine scale and linked those factors with the varying patterns of rail transit ridership. In this study, we characterized the fine-scale built environment factors in the central urban area of Guangzhou, China, by integrating multi-source geospatial big data including Tencent user data, building footprint and stories, points of interest (POI) data and Google Earth high-resolution images. Six direct ridership models (DRMs) based on the backward stepwise regression method were built to compare the different effects between daily, temporal and directional ridership. The results indicated that number of station entrances/exits and transfer dummy, were positively associated with rail transit ridership, while connecting bus station sites and the parking lots were not significantly related to ridership. Population density and common residences land were found to be dominating factors in promoting morning boarding & evening alighting ridership, which implied that these two factors should be focused on to encourage commuting-purpose rail transit usage. However, the indistinct effect of urban villages on rail transit ridership suggested planners to pay more attentions on urban regeneration at the pedestrian catchment areas (PCAs) with urban villages. High employment density and a large FAR were suggested at the employment-oriented areas owing to their importance in promoting rail transit ridership, especially the morning alighting & evening boarding ridership. Moreover, educational research land use significantly affected weekday ridership while sports land use positively influenced weekend ridership, which suggested planners to pay more attention on the non-commuting trips. The different influencing mechanisms of various types of rail transit ridership highlighted the need to consider land use balance planning and trip demand optimization in highly urbanized metropolises in developing countries.
Keywords: Rail transit ridership | Big data | Fine-scale | Built environment | Guangzhou
The geography of human activity and land use: A big data approach
جغرافیای فعالیت های انسانی و استفاده از زمین: یک رویکرد داده های بزرگ-2020
The application of location-based social media big data in urban contexts offers new and alternative strategies for understanding city liveliness in developing countries where traditional census data are poor. This paper demonstrates how the spatial-temporal distribution of Chinas Tencent social media usage intensities can be effectively used as a proxy for modelling the geographic patterns of human activity at fine scales. Our results suggest that the spatially-temporally contextualized nature of human activity is dependent upon land use mixing characteristics. With billions of social media data being collected in the virtual world, findings of this study suggest that land use policies to delineating the density, orderly or disorderly geographic patterns of human activity are important for city liveliness.
Keywords: Big data | Human activity | Land use
A model for big spatial rural data infrastructure in Turkey: Sensor-driven and integrative approach
یک مدل برای زیرساخت های داده های بزرگ فضایی روستایی در ترکیه: رویکرد حسگر محور و یکپارچه-2020
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).
Keywords: Spatial data infrastructures | Big data | Internet of things | Rural land use | INSPIRE | LPIS
Nine-nine-six work system and people’s movement patterns: Using big data sets to analyse overtime working in Shanghai
سیستم کار نه-نه-شش و الگوهای حرکت مردم: استفاده از مجموعه داده های بزرگ برای تحلیل اضافه کاری در شانگهای-2020
Although topics regarding “996 work system” and overtime working have aroused hot arguments, there is scant literature that analyses the spatial distribution and movement patterns of people who work overtime. This article fills this gap by adopting big data analysis and examining the mobile phone signal data which allow the calculation of the approximate spatial position of the mobile-phone user, and the generation of transportation flows and individuals’ origin-destination (OD) flows. The findings show that no less than one third of employees in Shanghai work overtime, and that overtime workers face higher job-housing imbalance than workers who have normal work durations or flexible schedules. This corroborates David Harvey’s time-space compression theory. Going beyond that, we further discover the interchangeability between exploitation in the time dimension, and that in the spatial dimension, resulting in dual exploitation. This article has important policy implications for optimizing the urban spatial system of Shanghai, as it advocates that in addition to strengthening the enforcement of labor law, the government also needs to improve the public service such as strengthening the underground system’s capacity, and construct affordable houses, so as to alleviate the employees’ sufferings caused by temporal and spatial exploitation. Moreover, the research points out the necessity for Chinese cities to enhance the vertical mixing, in order to shorten the job-housing distance.
Keywords: Overtime working | Human activity patterns | Big data | Mobile phone Signal data | Shanghai | OD | Time-space compression | Vertical mixing of land use
Improving high-tech enterprise innovation in big data environment: A combinative view of internal and external governance
بهبود نوآوری شرکتهای پیشرفته در محیط داده های بزرگ: نمای ترکیبی از حاکمیت داخلی و خارجی-2020
The emergence of big data brings both opportunities and challenges to high-tech enterprises. How to keep competitive advantages and improve innovation performance is important for enterprises in big data environment. Except from organizational learning ability and the use of advanced technology, the corporate governance also plays an important role in the process of enterprise’s innovation practice. This article creatively combines with the insights of internal and external governance, and explores how the managerial power and network centrality affects enterprise’s innovation performance in big data environment. Considering about the differences among distinct regional big data environment (strong/weak), this paper also takes classification research on it. The research findings show that managerial power has a significant positive impact on innovation performance, managerial power could enhance enterprise’s centrality in network, and the enterprise which located in network central position has more advantages in obtaining resources and significantly improves firm’s innovation performance. Network centrality plays a mediating role on managerial power and innovation performance. Further research finds that the positive effects of managerial power and network centrality are more significantly in the strong big data environment. These findings enrich the research of high-tech enterprise innovation from a combinative governance view, and contribute to the literatures on enterprise innovation in big data environment
Keywords: Big data environment | High-tech enterprises | Innovation performance | Managerial power | Network centrality
Big Data Analytics for Venture Capital Application：Towards Innovation Performance Improvement
تجزیه و تحلیل داده های بزرگ برای برنامه های سرمایه گذاری: به سمت بهبود عملکرد نوآوری-2020
By using the panel date of Chinese enterprises, this paper analyzes the influence of venture capital on innovation performance. In this paper, the number of patent application and the patent quality(invention patent applications, number of effective patents, IPC number of international patent classification, and patent claims) are used to measure the innovation performance of enterprises, and the regression results show that the innovation performance is significantly promoted by the venture capital; for industries with higher dependence on external financing and high technology intensity and areas with better protection of property rights, venture capital promotes innovation performance more significantly. In this paper, it further distinguishes the characteristics of venture capital institutions, and finds that the promotion effect of non-state-owned venture capital on innovation performance is significantly greater than that of state-owned venture capital; the venture capital institutions with high reputation and high network capital play a more significant role in promoting innovation performance.
Keywords: Data panel model | Big data | Innovation performance
Prescriptive analytics: Literature review and research challenges
تجزیه و تحلیل تجربی: مرور ادبیات و چالش های تحقیقاتی-2020
Business analytics aims to enable organizations to make quicker, better, and more intelligent decisions with the aim to create business value. To date, the major focus in the academic and industrial realms is on descriptive and predictive analytics. Nevertheless, prescriptive analytics, which seeks to find the best course of action for the future, has been increasingly gathering the research interest. Prescriptive analytics is often considered as the next step towards increasing data analytics maturity and leading to optimized decision making ahead of time for business performance improvement. This paper investigates the existing literature pertaining to prescriptive analytics and prominent methods for its implementation, provides clarity on the research field of prescriptive analytics, synthesizes the literature review in order to identify the existing research challenges, and outlines directions for future research.
Keywords: Analytics | Prescriptive analytics | Business analytics | Big data | Literature review
Does government information release really matter in regulating contagionevolution of negative emotion during public emergencies? From the perspective of cognitive big data analytics
آیا انتشار اطلاعات دولتی در تنظیم تکامل منفی احساسات منفی در مواقع اضطراری عمومی اهمیت دارد؟ از منظر تجزیه و تحلیل داده های بزرگ شناختی-2020
The breeding and spreading of negative emotion in public emergencies posed severe challenges to social governance. The traditional government information release strategies ignored the negative emotion evolution mechanism. Focusing on the information release policies from the perspectives of the government during public emergency events, by using cognitive big data analytics, our research applies deep learning method into news framing framework construction process, and tries to explore the influencing mechanism of government information release strategy on contagion-evolution of negative emotion. In particular, this paper first uses Word2Vec, cosine word vector similarity calculation and SO-PMI algorithms to build a public emergenciesoriented emotional lexicon; then, it proposes a emotion computing method based on dependency parsing, designs an emotion binary tree and dependency-based emotion calculation rules; and at last, through an experiment, it shows that the emotional lexicon proposed in this paper has a wider coverage and higher accuracy than the existing ones, and it also performs a emotion evolution analysis on an actual public event based on the emotional lexicon, using the emotion computing method proposed. And the empirical results show that the algorithm is feasible and effective. The experimental results showed that this model could effectively conduct fine-grained emotion computing, improve the accuracy and computational efficiency of sentiment classification. The final empirical analysis found that due to such defects as slow speed, non transparent content, poor penitence and weak department coordination, the existing government information release strategies had a significant negative impact on the contagion-evolution of anxiety and disgust emotion, could not regulate negative emotions effectively. These research results will provide theoretical implications and technical supports for the social governance. And it could also help to establish negative emotion management mode, and construct a new pattern of the public opinion guidance.
Keywords: Government information release | Cognitive big data analytics | E-government | Sentiment analysis | Public emergency events
Self-interest and data protection drive the adoption and moral acceptability of big data technologies: A conjoint analysis approach
منافع شخصی و محافظت از داده ها باعث پذیرش اخلاقی و تطبیقی فناوری های داده بزرگ می شوند: یک رویکرد تجزیه و تحلیل مشترک-2020
Big data technologies have both benefits and costs which can influence their adoption and moral acceptability. Prior studies look at people’s evaluations in isolation without pitting costs and benefits against each other. We address this limitation with a conjoint experiment (N ¼ 979), using six domains (criminal investigations, crime prevention, citizen scores, healthcare, banking, and employment), where we simultaneously test the relative influence of four factors: the status quo, outcome favorability, data sharing, and data protection on decisions to adopt and perceptions of moral acceptability of the technologies. We present two key findings. (1) People adopt technologies more often when data is protected and when outcomes are favorable. They place equal or more importance on data protection in all domains except healthcare where outcome favorability has the strongest influence. (2) Data protection is the strongest driver of moral acceptability in all domains except healthcare, where the strongest driver is outcome favorability. Additionally, sharing data lowers preference for all technologies, but has a relatively smaller influence. People do not show a status quo bias in the adoption of technologies. When evaluating moral acceptability, people show a status quo bias but this is driven by the citizen scores domain. Differences across domains arise from differences in magnitude of the effects but the effects are in the same direction. Taken together, these results highlight that people are not always primarily driven by selfinterest and do place importance on potential privacy violations. The results also challenge the assumption that people generally prefer the status quo.
Keywords: Moral acceptability | Big data | Conjoint analysis | Outcome favorability | Data protection | Data sharing
Can the development of a patient’s condition be predicted through intelligent inquiry under the e-health business mode? Sequential feature map-based disease risk prediction upon features selected from cognitive diagnosis big dat
آیا می توان از طریق استعلام هوشمند تحت شرایط تجارت الکترونیکی ، وضعیت یک بیمار را پیش بینی کرد؟ پیش بینی خطر ابتلا به بیماری مبتنی بر ویژگی های توالی بر ویژگی های انتخاب شده از تشخیص شناختی داده های بزرگ-2020
The data-driven mode has promoted the researches of preventive medicine. In prediction of disease risks, physicians’ clinical cognitive diagnosis data can be used for early prevention of diseases and, therefore, to reduce medical cost, to improve accessibility of medical services and to lower medical risk. However, researches involved no physicians’ cognition of patients’ conditions in intelligent inquiry under e-health business mode, offered no diagnosis big data, neglected the values of the fused text information generated by joint activities of online and offline medical data, and failed to thoroughly analyze the phenomenon of redundancy-complementarity dispersion caused by high-order information shortage from the online inquiry data-driven perspective. Besides, the risk prediction simply based on offline clinical cognitive diagnosis data undoubtedly reduces prediction precision. Importantly, relevant researches rarely considered temporal relationships of different medical events, did not conduct detailed analysis on practical problems of pattern explosion, did not offer a thought of intelligent portrayal map, and did not conduct relevant risk prediction based on the sub-maps obtained from the map. In consequence, the paper presents a disease risk prediction method with the model for redundancy-complementarity dispersion-based feature selection from physicians’ online cognitive diagnosis big data to realize features selection from the cognitive diagnosis big data of online intelligent inquiry; the obtained features were ranked intelligently for subsequent high-dimensional information shortage compensation; the compensated key feature information of the cognitive diagnosis big data was fused with offline electronic medical record (EMR) to form the virtual electronic medical record (VEMR). The formed VEMR was combined with the method of the sequential feature map for modelling, and a sequential feature map-based model for disease risk prediction was presented to obtain online users’ medical conditions. A neighborhood-based collaborative prediction model was presented for prediction of an online intelligent medical inquiry user’s possible diseases in the future and to intelligently rank the risk probabilities of the diseases. In the experiments, the online intelligent medical inquiry users’ VEMRs were used as the foundation of the simulation experiments to predict disease risks in chronic obstructive pulmonary disease (OCPD) population and rheumatic heart disease (RHD) population. The experiments demonstrated that the presented method showed relatively good metric performances in the VEMR and improved disease risk prediction.
Keywords: Cognitive diagnosis big data | Online intelligent inquiry | Sequential feature map | Disease risk prediction | Redundancy and complementarity dispersion