Matching Items (5)

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Promoting Crowdsourcing for Urban Research: Cycling Safety Citizen Science in Four Cities

Description

People generate massive volumes of data on the Internet about cities. Researchers may engage these crowds to fill data gaps and better understand and inform planning decisions. Crowdsourced tools for

People generate massive volumes of data on the Internet about cities. Researchers may engage these crowds to fill data gaps and better understand and inform planning decisions. Crowdsourced tools for data collection must be supported by outreach; however, researchers typically have limited experience with marketing and promotion. Our goal is to provide guidance on effective promotion strategies. We evaluated promotion efforts for BikeMaps.org, a crowdsourced tool for cycling collisions, near misses, hazards, and thefts. We analyzed website use (sessions) and incidents reported, and how they related to promotion medium (social, traditional news, or in-person), intended audience (cyclists or general), and community context (cycling mode share, cycling facilities, and a survey in the broader community). We compared four Canadian cities, three with active promotion, and one without, over eight months. High-use events were identified in time periods with above average web sessions. We found that promotion was essential for use of the project. Targeting cycling specific audiences resulted in more data submitted, while targeting general audiences resulted in greater age and gender diversity. We encourage researchers to use tools to monitor and adapt to promotion medium, audience, and community context. Strategic promotion may help achieve more diverse representation in crowdsourced data.

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Created

Date Created
  • 2017-06-21

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Intertidal resource use over millennia enhances forest productivity

Description

Human occupation is usually associated with degraded landscapes but 13,000 years of repeated occupation by British Columbia’s coastal First Nations has had the opposite effect, enhancing temperate rainforest productivity. This

Human occupation is usually associated with degraded landscapes but 13,000 years of repeated occupation by British Columbia’s coastal First Nations has had the opposite effect, enhancing temperate rainforest productivity. This is particularly the case over the last 6,000 years when intensified intertidal shellfish usage resulted in the accumulation of substantial shell middens. We show that soils at habitation sites are higher in calcium and phosphorous. Both of these are limiting factors in coastal temperate rainforests. Western redcedar (Thuja plicata) trees growing on the middens were found to be taller, have higher wood calcium, greater radial growth and exhibit less top die-back. Coastal British Columbia is the first known example of long-term intertidal resource use enhancing forest productivity and we expect this pattern to occur at archaeological sites along coastlines globally.

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Created

Date Created
  • 2016-08-30

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An Equity Analysis of Phoenix Bicyclist and Pedestrian Involved Crash Rates

Description

Walking and bicycling bring many merits to people, both physically and mentally.

However, not everyone has an opportunity to enjoy healthy and safe bicycling and

walking. Many studies suggested that

Walking and bicycling bring many merits to people, both physically and mentally.

However, not everyone has an opportunity to enjoy healthy and safe bicycling and

walking. Many studies suggested that access to healthy walking and bicycling is heavily

related to socio-economic status. Low income population and racial minorities have

poorer transportation that results in less walking and bicycling, as well as less access to

public transportation. They are also under higher risks of being hit by vehicles while

walking and bicycling. This research quantifies the relationship between socioeconomic

factors and bicyclist and pedestrian involved traffic crash rates in order to establish an

understanding of how equitable access to safe bicycling and walking is in Phoenix. The

crash rates involving both bicyclists and pedestrians were categorized into two groups,

minor crashes and severe crashes. Then, the OLS model was used to analyze minor and

severe bicycle crash rates, and minor and severe pedestrian crash rates, respectively.

There are four main results, (1) The median income of an area is always negatively

related to the crash rates of bicyclists and pedestrians. The reason behind the negative

correlation is that there is a very small proportion of people choosing to walk or ride

bicycles as their commuting methods in the high-income areas. Consequently, there are

low crash rates of pedestrians and bicyclists. (2) The minor bicycle crash rates are more

related to socio-economic determinants than the severe crash rates. (3) A higher

population density reduces both the minor and the severe crash rates of bicyclists and

pedestrians in Phoenix. (4) A higher pedestrian commuting ratio does not reduce bicyclist

and pedestrian crash rates in Phoenix. The findings from this study can provide a

reference value for the government and other researchers and encourage better future

decisions from policy makers.

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Created

Date Created
  • 2020

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A new era of spatial interaction: potential and pitfalls

Description

As urban populations become increasingly dense, massive amounts of new 'big' data that characterize human activity are being made available and may be characterized as having a large volume of

As urban populations become increasingly dense, massive amounts of new 'big' data that characterize human activity are being made available and may be characterized as having a large volume of observations, being produced in real-time or near real-time, and including a diverse variety of information. In particular, spatial interaction (SI) data - a collection of human interactions across a set of origins and destination locations - present unique challenges for distilling big data into insight. Therefore, this dissertation identifies some of the potential and pitfalls associated with new sources of big SI data. It also evaluates methods for modeling SI to investigate the relationships that drive SI processes in order to focus on human behavior rather than data description.

A critical review of the existing SI modeling paradigms is first presented, which also highlights features of big data that are particular to SI data. Next, a simulation experiment is carried out to evaluate three different statistical modeling frameworks for SI data that are supported by different underlying conceptual frameworks. Then, two approaches are taken to identify the potential and pitfalls associated with two newer sources of data from New York City - bike-share cycling trips and taxi trips. The first approach builds a model of commuting behavior using a traditional census data set and then compares the results for the same model when it is applied to these newer data sources. The second approach examines how the increased temporal resolution of big SI data may be incorporated into SI models.

Several important results are obtained through this research. First, it is demonstrated that different SI models account for different types of spatial effects and that the Competing Destination framework seems to be the most robust for capturing spatial structure effects. Second, newer sources of big SI data are shown to be very useful for complimenting traditional sources of data, though they are not sufficient substitutions. Finally, it is demonstrated that the increased temporal resolution of new data sources may usher in a new era of SI modeling that allows us to better understand the dynamics of human behavior.

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Created

Date Created
  • 2017

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Understanding Mobility and Active Transportation in Urban Areas Through Crowdsourced Movement Data

Description

Factors that explain human mobility and active transportation include built environment and infrastructure features, though few studies incorporate specific geographic detail into examinations of mobility. Little is understood, for example,

Factors that explain human mobility and active transportation include built environment and infrastructure features, though few studies incorporate specific geographic detail into examinations of mobility. Little is understood, for example, about the specific paths people take in urban areas or the influence of neighborhoods on their activity. Detailed analysis of human activity has been limited by the sampling strategies employed by conventional data sources. New crowdsourced datasets, or data gathered from smartphone applications, present an opportunity to examine factors that influence human activity in ways that have not been possible before; they typically contain more detail and are gathered more frequently than conventional sources. Questions remain, however, about the utility and representativeness of crowdsourced data. The overarching aim of this dissertation research is to identify how crowdsourced data can be used to better understand human mobility. Bicycling activity is used as a case study to examine human mobility because smartphone apps aimed at collecting bicycle routes are readily available and bicycling is under studied in comparison to other modes. The research herein aimed to contribute to the knowledge base on crowdsourced data and human mobility in three ways. First, the research examines how conventional (e.g., counts, travel surveys) and crowdsourced data correspond in representing bicycling activity. Results identified where the data correspond and differ significantly, which has implications for using crowdsourced data for planning and policy decisions. Second, the research examined the factors that influence cycling activity generated by smartphone cycling apps. The best predictors of activity were median weekly rent, percentage of residential land, and the number of people using two or more modes to commute in an area. Finally, the third part of the dissertation seeks to understand the impact of bicycle lanes and bicycle ridership on residential housing prices. Results confirmed that bicycle lanes in the neighborhood of a home positively influence sale prices, though ridership was marginally related to house price. This research demonstrates that knowledge obtained through crowdsourced data informs us about smaller geographic areas and details on where people bicycle, who uses bicycles, and the impact of the built environment on bicycling activity.

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Agent

Created

Date Created
  • 2018