Methodological Challenges and Institutional Barriers in the Use of Experimental Method for the Evaluation of Business Incubators: Lessons from the US, EU and China

Despite their worldwide adoption by policy makers as the Holy Grail for entrepreneurship and business development, the effectiveness of business incubation programs remain elusive, primarily plagued by untenable evaluation methods. This paper develops an in-depth analysis on those methodological and institutional factors that prohibit the use of theoretically sound solutions such as the Experimental Method in evaluation practice

Toward the Geography of Business Incubator Formation in the United States

The geography of business incubators has seldom been examined against the public aspirations and beliefs that incubators should either inhabit economically distressed areas to alleviate unemployment and poverty (in the case of empowerment business incubators) or proliferate in technologically capable regions to adequately unleash and exploit local high-technology potentials (in the case of technology business incubators). In this paper, the geographic distribution of 719 U.S. business incubators, which are located in 465 out of the 3,141 counties, is examined drawing upon a newly built incubator population database. In addition, the location factors underlying the formation of business incubators are also identified and analyzed, which leads to the discovery of a dichotomy between rural and urban incubators in their locational determinants

Geography of Business Incubator Formation in the United States

The geography of business incubators has seldom been examined against the public aspirations and beliefs that incubators should either inhabit economically distressed areas to alleviate unemployment and poverty (in the case of empowerment business incubators) or proliferate in technologically capable regions to adequately unleash and exploit local high-technology potentials (in the case of technology business incubators). In this paper, the geographic distribution of 719 U.S. business incubators, which are located in 465 out of the 3,141 counties, is examined drawing upon a newly built incubator population database. In addition, the location factors underlying the formation of business incubators are also identified and analyzed, which leads to the discovery of a dichotomy between rural and urban incubators in their locational determinants

UrbanFM: Inferring Fine-Grained Urban Flows

Urban flow monitoring systems play important roles in smart city efforts around the world. However, the ubiquitous deployment of monitoring devices, such as CCTVs, induces a long-lasting and enormous cost for maintenance and operation. This suggests the need for a technology that can reduce the number of deployed devices, while preventing the degeneration of data accuracy and granularity. In this paper, we aim to infer the real-time and fine-grained crowd flows throughout a city based on coarse-grained observations. This task is challenging due to two reasons: the spatial correlations between coarse- and fine-grained urban flows, and the complexities of external impacts. To tackle these issues, we develop a method entitled UrbanFM based on deep neural networks. Our model consists of two major parts: 1) an inference network to generate fine-grained flow distributions from coarse-grained inputs by using a feature extraction module and a novel distributional upsampling module; 2) a general fusion subnet to further boost the performance by considering the influences of different external factors. Extensive experiments on two real-world datasets, namely TaxiBJ and HappyValley, validate the effectiveness and efficiency of our method compared to seven baselines, demonstrating the state-of-the-art performance of our approach on the fine-grained urban flow inference problem