Urban Sensing by Crowdsourcing: Analysing Urban Trip behaviour in Zurich

The research on which this essay is based sought to provide a deeper understanding of live urban activities. The main idea was to obtain in‐depth trip information by integrating sensing technology with crowdsourcing‐based methods. Location and acceleration sensors embedded in smartphones can deliver useful trip information. Research had previously been conducted to find an accurate data analysis algorithm for high‐level data mining, an efficient sensing method for power saving. As a proof of concept, this essay presents a case study of Zurich that successfully implemented previous research achievements in the real environment. It established that urban travel behaviour can, in fact, be collected by sensors embedded in mobile devices, and that the data thus obtained can be used to measure the characteristics of trip behaviour in cities by means of an advanced classifying algorithm and analysis. The strength of this research is its pioneering role in clearing the ground for future urban data collection methods and planning strategies. Specifically, this study could provide answers to the following key questions: How can crowdsourcing be applied to the collection of urban transportation data? What kinds of information can be extracted from the crowdsourced mobile sensing platform? What kinds of knowledge, within the transportation domain, can be derived from the above?     

Comparing efficiency of holding business model and individual management model of airports

This research compares the efficiency of holding business model to individual management model of airports, employing some robust non-parametric partial frontier-based methods to compare the statistical distributions of efficiency, under different scenarios, to find out which group of airports yields better global performance. The comparison between groups will follow a Malmquist index decomposition, which seems to be the most appropriate tool for within- and inter-group performance comparison. For this purpose, a sample of 145 airports from three continents is utilized. The results provide evidence that European airports are the most productive ones, and within this cluster, the individual management model presented a significant frontier shift with respect the holding cluster frontier, meaning that the former is much more productive than the latter.     

Data mining framework based on rough set theory to improve location selection decisions: A case study of a restaurant chain

Location selection plays a crucial role in the retail and service industries. A comprehensive location selection model and appropriate analytical technique can improve the quality of location decisions, attracting more customers and substantially impacting market share and profitability. This study developed a data mining framework based on rough set theory (RST) to support location selection decisions. The proposed framework consists of four stages: (1) problem definition and data collection; (2) RST analysis; (3) rule validation; and (4) knowledge extraction and usage. An empirical study focused on a restaurant chain to demonstrate the validity of the proposed approach. Twenty location variables relevant to five location aspects were examined, and the results indicated that latent knowledge can be identified to support location selection decisions.     

大数据时代下电子商务平台的探索和研究——以淘宝网为例

随着网络用户的日益增长,互联网记录着大量的用户个人信息和由智能终端产生的图片和信息,这些信息爆炸性的增长,并不断涌入网络海洋,产生海量的数据信息。在电子商务环境中大数据处理将会发展出更多强大和多元的功能。本文以淘宝网为例,分析研究如何合理利用数据,为企业的经营模式做出相应调整,并且在变化中不断进行企业新技术、新方法和新思路的探索。同时指出数据泄露的隐患,并给出相应建议。     

The Post-entry Performance of Cohorts of Export Starters in German Manufacturing Industries☆

This paper investigates four cohorts of firms from German manufacturing industries that started to export between 1998 and 2002, and follows them for five years after the start. Export starters are a rare species and small in Germany. Around 30–40% of those starters studied became continuous exporters. The share of total exports contributed by export starters of a cohort is tiny in the start year, and it remains so over the years that follow. Contrary to the market selection hypothesis, there is no evidence that productivity in the start year is systematically related to survival in the export market. There is no evidence of a negative impact of a smaller firm size in the start year on the chance of surviving in the export market. Starting with a higher share of exports in total sales, however, tends to increase the probability of continuing to export.     

理性与良知：“大跃进”时期的县级官员

“大跃进”时期的县级官员在整个国家的行政结构中处于一个夹层位置，一方面其命运取决于上级的好恶，另一方面对下级和民众的权力基本不受到制约，因此他们从理性的角度发出，只能对上惟命是从，但是他们中的一些人，对民众所遭到的痛苦，表现出良知的一面。有一部分县级官员为上级的错误和自己的过失承受了惩处，不过最根本的原因是行政体制所造成的，在一定程度上说，县级官员也是行政体制问题的牺牲品。     

含有中间变量的农村信用社可持续经营效率评价

本文借助含有中间变量的两阶段DEA评价方法对陕西省涉农地区不同信用社的可持续经营效率进行了评价。得出了大部分涉农地区基层信用社存在存款严重不足、发放贷款效率比吸收存款效率高、非利息支出过多、非利息收入不足等结论。     

数据生产理论——数据资源权利配置的基础理论

人类正在进入数据驱动的时代,数据成为社会的基础资源,但数据一直被认为处于公共领域并妨碍着数据权利化,困扰着数据资源利用秩序的建立。描述特定对象的数据并不是天然存在的,而是被生产出来的,并将数据价值（预测分析、发现新知）的实现过程界分为原始数据生产（采集）、数据集生产（汇集性处理）和数据分析（分析性处理）三种行为,并将前两个行为称为数据生产,提出数据生产理论。数据生产理论首先应区分数据生产和数据分析,原始数据的生产是建立在分析原材料提供者基础之上,应承认其价值并配置适当权利,以满足各种分析目的的数据集的生产。同时,数据生产还应区别数据来源,来源于个人的数据并不一定是个人生产的,只有个人在提供或创制了数据时才是数据的生产者。因此,数据生产理论是在将数据视为一种资源的情形下为数据上权利配置提供理论支撑,通过配置相应权利,构筑从原始数据生产者到数据集生产者,再到数据分析者的数据利用秩序。     

后危机时代政府干预与银行信贷—基于国有经济视角

本文利用我国2007-2016年的省级面板数据,对后危机时代政府干预与银行信贷以 及不良贷款率进行了实证研究。本文发现：地方政府干预显著带来地区信贷增长以及不良贷款 率下降;地方政府通过对国有经济进行干预,可以显著带来地区信贷增长,但对降低地区不良 贷款率不显著。进一步研究发现：东部地区政府干预有助于提高地区信贷增长并降低不良贷款 率;西部地区政府通过干预国有经济能带来地区信贷增长;无论是东部、中部还是西部,政府 通过对国有经济的干预降低地区不良贷款率均不会产生太大效果。     

制度、增长与收敛——一个基于索洛模型的分析框架

标准的经济增长理论把制度看作是既定不变的并且不存在交易费用的,所以无法认识到制度对经济增长的重要性.将制度作为一个变量引入索洛模型,可以构造一个包含制度的、技术内生的增长模型.通过模型的推导可以得出这样的结论:制度质量越好,人均产出增长率越高;人均产出存在关于制度的条件收敛.并进一步得出这样的推论:欠发达国家想获得"后发优势"赶超发达国家,前提条件是具备良好的制度或能够持续改善其制度.