scholarly journals Estimating Public Bicycle Trip Characteristics with Consideration of Built Environment Data

2021 ◽  
Vol 13 (2) ◽  
pp. 500
Author(s):  
De Zhao ◽  
Ghim Ping Ong ◽  
Wei Wang ◽  
Wei Zhou
Keyword(s):  
Built Environment ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Past Research ◽  
Trip Generation ◽  
Negative Effects ◽  
Trip Distribution ◽  
Metro Station ◽  
Positive Effects ◽  
Points Of Interest

A reliable estimation of public bicycle trip characteristics, especially trip distribution and duration, can help decision-makers plan for the relevant transport infrastructures and assist operators in addressing issues related to bicycle imbalance. Past research studies have attempted to understand the relationship between public bicycle trip generation, trip attraction and factors such as built environment, weather, population density, etc. However, these studies typically did not include trip distribution, duration, and detailed information on the built environment. This paper aims to estimate public bicycle daily trip characteristics, i.e., trip generation, trip attraction, trip distribution, and duration using points of interest and smart card data from Nanjing, China. Negative binomial regression models were developed to examine the effect of built environment on public bicycle usage. Totally fifteen types of points of interest (POIs) data are investigated and factors such as residence, employment, entertainment, and metro station are found to be statistically significant. The results showed that 300 m buffer POIs of residence, employment, entertainment, restaurant, bus stop, metro station, amenity, and school have significantly positive effects on public bicycle generation and attraction, while, counterintuitively, 300 m buffer POIs of shopping, parks, attractions, sports, and hospital have significantly negative effects. Specifically, an increase of 1% in the trip distance leads to a 2.36% decrease in the origin-destination (OD) trips or a 0.54% increase of the trip duration. We also found that a 1% increase in the number of other nearby stations can help reduce 0.19% of the OD trips. The results from this paper can offer useful insights to operators in better estimating public bicycle usage and providing reliable services that can improve ridership.

Household Trip Generation and the Built Environment: Does More Density Mean More Trips?

2019 ◽  
Vol 2673 (5) ◽  
pp. 596-606
Author(s):  
Qin Zhang ◽  
Kelly J. Clifton ◽  
Rolf Moeckel ◽  
Jaime Orrego-Oñate
Keyword(s):  
Built Environment ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Trip Generation ◽  
Transportation Demand ◽  
Home Based ◽  
Environment Variables ◽  
Binomial Regression ◽  
Demand Models ◽  
New Development

Trip generation is the first step in the traditional four-step trip-based transportation model and an important transport outcome used in evaluating the impacts of new development. There has been a long debate on the association between trip generation and the built environment, with mixed results. This paper contributes to this debate and approaches the problem with two hypotheses: 1) built environment variables have significant impacts on household total trip generation; and 2) built environment variables have different impacts on trip generation by purpose. This study relied on data from the Portland, Oregon, metropolitan area to estimate negative binomial regression models of household trip generation rates across all modes. Results show that the built environment does have significant and positive influences on trip generation, especially for total number of trips, total number of tours, and home-based shopping-related trips. Moreover, log likelihood ratio tests implied that adding built environment to the base model contributed significantly to improving model explanatory and predictability. These findings suggest that transportation demand models should be more sensitive to the effects of the built environment to better reflect the variations in trip making across regions.

How marketer-generated content characteristics affect consumer engagement? Empirical evidence from China's WeChat food marketing

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Si Tan ◽  
Weiping Chen
Keyword(s):  
Body Text ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Food Marketing ◽  
Consumer Engagement ◽  
Content Type ◽  
Positive Effects ◽  
Binomial Regression ◽  
Media Research ◽  
Text Content

Purpose Leveraging marketer-generated content (MGC) can increase firms' success. However, few studies uncover the effects of MGC-related attributes on consumer engagement in the context of food marketing. This paper aims to explore the influence of MGC characteristics (valence, content types, vividness and interactivity) on consumer engagement.Design/methodology/approachThis study uses WeChat official account data of seven food companies from China and conducts negative binomial regression models.FindingsThe findings indicate that different MGC-related characteristics have separate impacts on consumer WeChat engagement. Title valence, transactional title content and title with punctuation vividness negatively affect consumers' consuming engagement. Knowledgeable or entertaining title content and title with interactivity both positively affect consumers' consuming engagement. Moreover, transactional body text content negatively influences consumers' contributing engagement, whereas entertaining body text content shows positive effects. Vivid and interactive MGC body text attributes enhance consumers' contributing engagement behavior.Originality/value This study contributes to social media research in food marketplaces and sheds light on the effect of different WeChat MGC characteristics on separate consumer engagement.

scholarly journals Does Institutional Embeddedness Promote Regional Enterprises’ Migration? An Empirical Analysis Based on the “Double Transfer” Strategy in Guangdong, China

2020 ◽  
Vol 12 (7) ◽  
pp. 2908
Author(s):  
Rongjun Long ◽  
Wei Lang ◽  
Xun Li
Keyword(s):  
Negative Binomial ◽  
Institutional Environment ◽  
Negative Binomial Regression ◽  
Impact Factors ◽  
Policy Makers ◽  
Positive Effects ◽  
Institutional Embeddedness ◽  
Future Policy ◽  
Urban And Regional Development ◽  
The Impact

Against the background of globalization, institutional embeddedness has become an important theoretical tool to understand the changes in regional economic patterns. This paper starts by discussing the theory of location choice of enterprises and then uses the statistical method of negative binomial regression to analyze the impact factors of enterprises’ transfer from the perspective of institutional embeddedness by taking Guangdong Province, China, as a case study area. It was found that informal institutional factors such as the same language, the same industry, and geographical proximity have significant positive effects on the transfer of regional enterprises. Formal institutions such as counterpart assistance are the core driving force of enterprise transfer, while traditional economic factors such as cost comparative advantage have no significant impact on the transfer of regional enterprises. This research shows that the transfer of regional enterprises is greatly influenced by the current regional institutional environment. Therefore, it is important for future policy makers to consider the regional institutional environment and to deepen regional institutional embeddedness to advance urban and regional development.

scholarly journals Gendered mobility and violence in the São Paulo metro, Brazil

Urban Studies ◽  
2020 ◽  
Vol 58 (1) ◽  
pp. 203-222 ◽  
Author(s):  
Gustavo Carvalho Moreira ◽  
Vania Aparecida Ceccato
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Sao Paulo ◽  
Geographical Information ◽  
São Paulo ◽  
Metro Station ◽  
Late Night ◽  
Temporal Features ◽  
Time Patterns ◽  
Violent Victimisation

With about 12 million inhabitants, São Paulo, Brazil, is the largest city in South America. As in many other major southern hemisphere cities, this extreme concentration of people imposes a number of mobility and security challenges. The objective of this article was to investigate the space-time patterns of mobility and violent victimisation in São Paulo’s metro stations from a gender perspective. The methodology combines use of a Geographical Information System (GIS), statistical analysis through negative binomial regression modelling and hypothesis testing. Results indicate that mobility and the level of victimisation are gender dependent. Women are at higher risk of victimisation than men in São Paulo’s central metro station, while men run higher risk of violence at end stations – both notably during late night periods. The presence of employees reduces the risk of violence, except during the mornings. The article suggests that crime prevention initiatives need to be gender informed and sensitive to the particular spatial and temporal features of rapid transit environments.

scholarly journals The Effect of Existing Knowledge Assets on Inbound/Outbound Disruptive Innovation

2017 ◽  
Author(s):  
Chun-pei Lin ◽  
Chuan-peng Yu ◽  
Tung-Ju Wu ◽  
Yen-Chun Wu
Keyword(s):  
Technology Development ◽  
Past Research ◽  
Disruptive Innovation ◽  
Negative Effects ◽  
Knowledge Assets ◽  
Manufacturing Enterprises ◽  
Positive Effects ◽  
Key Factor ◽  
The Empirical Analysis ◽  
Innovation Behavior

Innovation is an essential key factor in the technology development history. Past research on innovation focused more on the innovation behavior of technology, but seldom described knowledge assets which also influence innovation behavior greatly. The effect of knowledge assets attribute and result on disruptive innovation is therefore regarded as the research topic in this study, where disruptive innovation is divided into outbound and inbound to combine combination-embeddednessandmajor business specificityof knowledge assets as the research model. Manufacturing enterprises in China are proceeded the questionnaire survey, and 173 valid copies are collected. The empirical analysis shows that combination-embeddedness of knowledge assets presents significantly positive effects on major business specificity and outbound innovationof an enterprise but reveals remarkably negative effects on inbound innovation. Enterprises are suggested to constantly accumulate knowledge assets with low major business specificity before disruptive innovation in order to reduce ineffective inbound innovation.

scholarly journals The Association between Age and Depressive Symptoms among Older Men and Women in Europe. Findings from SHARE

2011 ◽  
Vol 36 (1) ◽  
Author(s):  
Isabella Buber ◽  
Henriette Engelhardt
Keyword(s):  
Mental Health ◽  
Depressive Symptoms ◽  
Negative Binomial ◽  
Explanatory Power ◽  
Negative Binomial Regression ◽  
Negative Binomial Regression Model ◽  
Negative Effects ◽  
Symptoms Of Depression ◽  
Binomial Regression ◽  
Older Men And Women

Empirical evidence of the effects of age on depressive symptoms is mixed, ranging from positive to zero to negative effects, depending on the modelling of the age-depression profile. This paper uses internationally comparative data to analyse the association between age and the prevalence of symptoms of depression, controlling for well-known determinants of mental health. Based on the first wave of the Survey of Health, Ageing and Retirement in Europe (SHARE), depressive symptoms of 28,538 persons aged 50 to 89 from eleven European countries and Israel are analysed using a negative binomial regression model. The results indicate that the number of depressive symptoms measured by EURO-D scores increase with age and are higher among women than among men. When including socio-demographic characteristics, health conditions and economic strains, the association between depressive symptoms and age vanishes for men, and even reverses for women. Thus, the association between age and mental health is mediated by the health and living conditions of older persons; age by itself has no explanatory power.

Colleges and Community Crime: An Analysis of Campus Proximity and Neighborhood Crime Rates

2020 ◽  
pp. 001112872097431
Author(s):  
Kelsey Cundiff
Keyword(s):  
College Students ◽  
Negative Binomial ◽  
Structural Characteristics ◽  
Property Crime ◽  
Negative Binomial Regression ◽  
Past Research ◽  
College Campuses ◽  
Neighborhood Crime ◽  
Binomial Regression ◽  
Using Data

Property and violent crime have been associated in past research with many of the same lifestyle features typical of college students and the structural characteristics of the neighborhoods where students generally cluster. According to opportunity theory, individuals with the lifestyles and routine activities similar to college students are vulnerable to victimization. Therefore, higher rates of crime can be expected in the neighborhoods that surround college campuses. Using data from the National Neighborhood Crime Survey, this study uses multilevel negative binomial regression to analyze the relationship between proximity to a college campus and rates of violent and property crime. Results show that bordering tracts have higher rates of larceny, burglary, and robbery, controlling for other neighborhood- and city-level indicators of crime.

scholarly journals Built environment correlates of walking for transportation: Differences between commuting and non-commuting trips

2021 ◽  
Vol 14 (1) ◽  
pp. 1129-1148
Author(s):  
Jixiang Liu ◽  
Jiangping Zhou ◽  
Longzhu Xiao
Keyword(s):  
Built Environment ◽  
Regression Models ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Demographic Variables ◽  
Tobit Regression ◽  
Environment Variables ◽  
Binomial Regression ◽  
Health Related ◽  
Walking For Transportation

As a sustainable mode of travel, walking for transportation has multiple environmental, social, and health-related benefits. In existing studies, however, such walking has rarely been differentiated between commuting and non-commuting trips. Using multilevel zero-inflated negative binomial regression and multilevel Tobit regression models, this study empirically examines the frequency and duration of commuting and non-commuting walking and their correlates in Xiamen, China. It finds that (1) non-commuting walking, on average, has a higher frequency and longer duration than commuting walking; (2) most socio-demographic variables are significant predictors, and age, occupation, and family size have opposite-direction effects on commuting and non-commuting walking; and (3) different sets of built environment variables are correlated with commuting and non-commuting walking, and the built environment collectively influences the latter more significantly than the former. The findings provide useful references for customized interventions concerning promoting commuting and non-commuting walking.

The Influence of Network Positions on Exploratory Innovation: An Empirical Evidence from China’s Patent Analysis

2020 ◽  
Vol 25 (1) ◽  
pp. 184-207 ◽  
Author(s):  
Ding Ma ◽  
Ya-Rui Zhang ◽  
Fan Zhang
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Network Density ◽  
Low Carbon ◽  
Negative Effects ◽  
Structural Holes ◽  
Technological Field ◽  
Binomial Regression ◽  
Low Carbon Energy ◽  
Exploratory Innovation

Existing studies find that exploratory innovation requires both access to heterogeneous resources for the establishment of novel knowledge combinations and high-quality cooperation to strengthen the absorption of heterogeneous knowledge. Therefore, structural holes and prominent positions exert critical effects on exploratory innovation. Few existing studies have investigated the influences of the two positions on exploratory innovation simultaneously. This study aims to identify the influence of network positions on exploratory innovation and the contingency mechanism. Basing on joint patent filing relationships identification belong to IPC Green Inventory, R&D collaboration networks in China consisted of 215 firms in the technological field of low-carbon energy were constructed. Negative binomial regression was used to analyse the influences of networks positions on exploratory innovation and the moderating effect of network density. Results reveal that network prominence and structural holes have inverted U-shaped effects on exploratory innovation. The simultaneous occupation of prominent positions and structural holes hinders exploratory innovation. Network density exerts positive and negative effects on the relationships between network prominence and exploratory innovation and between structural holes and exploratory innovation, respectively. The conclusions drawn in this study provide a reference for R&D network structural optimization toward exploratory innovation.

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