Functional urban area delineations of cities on the Chinese mainland using massive Didi ride-hailing records
توصیف های کاربردی منطقه شهری از شهرها در سرزمین اصلی چین با استفاده از سوابق گسترده تگرگ سوار بر دیدنی Didi-2020
The problem associated with a citys administrative boundary being “under-” or “over-bounded” has become a global phenomenon. A citys administrative boundary city does not effectively represent the actual size and impact of its labor force and economic activity. While many existing case studies have investigated the functional urban areas of single cities, the problem of how to delineate urban areas in geographic space relating to large bodies of cities or at the scale of an entire country has not been investigated. This study proposed a method for FUA identification that relies on ride-hailing big data. In this study, over 43 million anonymized 2016 car-hailing records were collected from Didi Chuxing, the largest car-hailing online platform in the world (to the best of our knowledge). A core-periphery approach is then proposed that uses nationwide and fine-grained trips to understand functional urban areas in Mainland China. This study examined 4456 out of all 39,007 townships in an attempt to provide a new method for the definition of urban functional areas in Chinese Mainland. In addition, four types of cities are identified using a comparison of functional urban areas with their administrative limits, and a further evaluation is conducted using 23 Chinese urban agglomerations. With the rapidly increasing use of internet-based ride-hailing services, such as Didi, Grab, Lyft, and Uber, globally, this study provides a practical benchmark for the delineation of functional urban areas at larger scales..
Keywords: Functional urban area | Car-hailing records | |National level | Delineating standards | City system
Administrative decentralization and credit resource reallocation: Evidence from Chinas “Enlarging Authority and Strengthening Counties” reform
عدم تمرکز اداری و تخصیص مجدد منابع اعتبار: شواهدی اصلاحات "بزرگ سازمان و تقویت شهرستان" چین-2020
With the rapid development of county-level economy promoted by continuous administrative decentralization reform in China, the shortage of county-level credit resources has become an increasingly serious problem. Based on the quasi-experiment of Chinas “Enlarging Authority and Strengthening Counties” (EASC) reform, this paper investigates the reallocation effect of administrative decentralization on county-level credit resources for the first time. We take a big panel data set of Chinas 1981 counties (county-level cities) during the period of 1997–2012 as research sample. Combining the difference-in-differences (DID) model with the propensity score matching (PSM) method to correct the sample self-selection bias, we estimate the effect of the EASC reform on the financial agglomeration of both deposits and loans. The results show that the administrative decentralization represented by the EASC reform exerts different reallocation effects on county-level deposits and loans. The EASC reform enhances loan agglomeration but restrains deposit agglomeration, intensifying the contradiction between the supply and demand of credit resources. The reallocation effect of the EASC reform on loans shows an inverted Ushaped trend over time, indicating that the reform effect gradually weakens. The significantly positive effect of the EASC reform on deposit financial agglomeration lasts only two years, followed by a continuous negative effect.
Keywords: Administrative decentralization | Credit resource reallocation | “Enlarging Authority and Strengthening | Counties” reform | Financial agglomeration | Quasi-experiment | China
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
Land titling, land reallocation experience, and investment incentives: Evidence from rural China
عنوان بندی اراضی ، تجربه توزیع مجدد زمین و مشوق های سرمایه گذاری: شواهدی از روستای چین-2020
The impacts of land titling on investment incentives among farmers with different land reallocation experiences are studied in this work. Ordered Probit model and 2SLS are employed to estimate the survey data collected from 2704 households in rural countries in China. We find that, generally, land titling can substantially promote investment incentive among farmers. However, the impacts vary among farmers with different land reallocation experiences. Specifically, land titling positively affects farmers without land reallocation experience, but it negatively affects those farmers who experienced big reallocation. Land titling has an investment incentive effect on China’s special agricultural land system, where farmers only have contract rights of land. However, big reallocation should be heavily restricted to guarantee the investment incentive effect of land titling.
Keywords: Land titling | Land rights reallocation | Investment incentive | China
Intelligent decision-making of online shopping behavior based on internet of things
تصمیم گیری هوشمندانه از رفتار خرید آنلاین مبتنی بر اینترنت اشیا-2020
The development of big data and Internet of things (IoT) have brought big changes to e-commerce. Different kinds of information sources have improved the consumers’ online shopping performance and make it possible to realize the business intelligence. Grip force and eye-tracking sensors are applied to consumers online reviews search behavior by relating them to the research approaches in IoT. To begin with, public cognition of human contact degrees of recycled water reuses with grip force test was measured. According to the human contact degrees, 9 recycled water reuses presented by the experiment are classified into 4 categories. Based on the conclusion drawn from grip force test, purified recycled water and fresh vegetable irrigated with recycled water are regarded as the drinking for high-level human contact degree and the irrigation of food crops for low-level human contact degree respectively. Several pictures are designed for eye-tracking test by simulating an on-line shopping web page on Taobao (the most popular online shopping platform in China). By comparing the fixation time participants spent on the areas of interest (AOIs), we justify that consumers online reviews search behavior is substantially affected by human contact degrees of recycled products. It was found that consumers rely on safety perception reviews when buying high contact goods.
Keywords: Online reviews search behavior | Recycled products | Grip force sensor | Eye-tracking sensor | Internet of Things (IoT)
The transnational Sowa Rigpa industry in Asia: New perspectives on an emerging economy
صنعت فراملی Sowa Rigpa در آسیا: چشم اندازهای جدید در مورد اقتصاد نوظهور-2020
This article advances the hypothesis that “traditional” Asian pharmaceutical industries are rapidly growing in size and prominence in contemporary Asia, and identifies a lack of empirical data on the phenomenon. Addressing this gap, the article provides a quantitative outline and analysis of the Sowa Rigpa (Tibetan, Mongolian and Himalayan medicine) pharmaceutical industry in China, India, Mongolia and Bhutan. Using original data gathered through multi-sited ethnographic and textual research between 2014 and 2019, involving 232 industry representatives, policy makers, researchers, pharmacists and physicians, it assembles a bigger picture on this industrys structure, size and dynamics. Revealing a tenfold growth of the Sowa Rigpa pharmaceutical industry in Asia between 2000 and 2017, the study supports its initial hypothesis. In 2017, the industry had a total sales value of 677.5 million USD, and constituted an important economic and public health resource in Tibetan, Mongolian and Himalayan regions of Asia. China generates almost 98 percent of the total sales value, which is explained by significant state intervention on the one hand, and historical and sociocultural reasons on the other. India has the second largest Sowa Rigpa pharmaceutical industry with an annual sales value of about 11 million USD, while sales values in Mongolia and Bhutan are very low, despite Sowa Rigpas domestic importance for the two nations. The article concludes with a number of broader observations emerging from the presented data, arguing that the Sowa Rigpa pharmaceutical industry has become big enough to exert complex transformative effects on Tibetan, Mongolian and Himalayan medicine more generally. The quantitative and qualitative data presented here provide crucial foundations for further scholarly, regulatory, and professional engagement with contemporary Sowa Rigpa.
Keywords: Sowa Rigpa | Tibetan medicine | Pharmaceutical industry | Government policy | China | India | Mongolia | Bhutan
Dynamic occupant density models of commercial buildings for urban energy simulation
مدلهای چگالی اشغال پویا ساختمانهای تجاری برای شبیه سازی انرژی شهری-2020
The number of occupants and its changing pattern over time are key information for building and urban energy simulation. However, the commonly used assumption and simplification of a fixed occupancy schedule does not reflect the complicated reality, leading to significant errors in energy simulation. Therefore, dynamic occupant density models which describe the real-world situation more accurately should be developed. This paper presents a methodology to develop such a model for commercial buildings and expand it from the building level to urban level. First, a total of 2275 commercial buildings in Nanjing, a major city in China, are identified and classified into three sub-categories using Points of Interest and logistic regression. Then field measurement is conducted to obtain the hourly occupant density for 12 sample commercial buildings. The building-level dynamic occupant density model is developed by fitting normal distribution functions into the measured data. Finally, transportation accessibility and population level, two urban parameters, are defined and used to expand the buildinglevel occupant density model to the urban-level one. The dynamic urban-level occupant density model is verified for all three sub-categories of commercial buildings and the overall results are acceptable.
Keywords: Big data | Commercial buildings | Urban-level | Dynamic occupant density models
Impact factors of the real-world fuel consumption rate of light duty vehicles in China
عوامل مؤثر بر میزان مصرف سوخت در دنیای واقعی از وسایل نقلیه سبک وزن در چین-2020
Measuring real-world fuel consumption of light duty vehicles can be challenging due to the limited collection of actual data. In this paper, we use big data retrieved from the record of real-world fuel consumptions of different brands of vehicles in different areas (n ¼ 106,809 samples from 201 brands of vehicles and 34 cities) in China to build up a real-world fuel consumption rate (RFCR) model to estimate the fuel consumption given the driving conditions and figure out the main factors that affect actual fuel consumption in the real world.We find the average deviation of actual fuel consumptions and the fitting results of RFCR model is 4.22% , which does not significantly differ from zero, and the fuel consumptions calculated by RFCR model tend to be 1.40 L/100 km (about 25%) higher than the official reported data. Furthermore, we find that annual average temperature and altitude factors significantly influence the fuel consumption rate. The results indicate that there is a real world performance discrepancy between the theoretical fuel consumption released by authorities and that in the real world, and some green behaviors (choose light duty vehicles, reduce the use of air conditioning and change to manual transmission type) can reduce energy consumption of vehicles.
Keywords: Real-world fuel consumption rate | Energy consumption | Private passenger vehicles | Big data | China
Achieving energy conservation targets in a more cost-effective way: Case study of pulp and paper industry in China
دستیابی به اهداف حفظ انرژی به شیوه ای مقرون به صرفه تر: مطالعه موردی صنعت کاغذ و خمیر کاغذ در چین-2020
Accurate understanding of the marginal energy-saving costs of energy-using entities is critical for regulatory authorities to formulate and implement energy-saving policies. This article brings out an easy-tooperate analytical framework to measure the marginal energy-saving cost and construct energy conservation supply curve with the help of data envelopment analysis and quantitative model. Furthermore, taking China’s pulp and paper industry as an example, the authors discuss the difference of economic costs between industry energy-saving task assignment with and without considering the difference of marginal energy-saving costs of different energy using entities with the help of linear programming model. The main empirical results are as follow. First, there is a big difference in the industrial marginal energy-saving costs in different provinces. The difference between the highest and lowest energy-saving costs is twice as large. Second, the optimal energy-saving task assignment based on an accurate energy conservation supply curve can help to save nearly 35% of total economic cost.
Keywords: Energy conservation | Marginal energy-saving cost | Energy conservation supply curve