Spatial patterns and demographic indicators of effective social media content during theHorsethief Canyon fire of 2012

Kent, Joshua; Capello, Henry T.

doi:10.1080/15230406.2013.776727

Cited by 98 publications

(48 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moran's index for BKT is 0.22, with a z-score of 22.08, (p ¼0.0000), indicating a less than 1% likelihood that its spatial clustering is random. When spatial autocorrelation is present, it can lead to mis-specified models that do not account for this type of clustering, and geographically weighted regression (GWR) may help control for autocorrelation and nonstationarity (Kent and Capello, 2013;Miller, 2012 spatial distribution of bicycle volumes demonstrates nonstationarity and spatial autocorrelation, and pass checks of regularity for global analysis, geographically weighted regression can be used to improve the model with localized coefficients.…”

Section: Resultsmentioning

confidence: 99%

Where does bicycling for health happen? Analysing volunteered geographic information through place and plexus

Griffin

Jiao

2015

Journal of Transport & Health

View full text Add to dashboard Cite

a b s t r a c tResearch on the role of bicycling for health through physical activity has been limited by the lack of information on where bicyclists ride. New big data sources available through smartphone-based applications provide a rich source to provide bicycle volume data more comparable to the scale of information available for automotive and public transit modes. In the case of smartphone apps for fitness tracking, results of this data can be used similar to the growing application of global positioning systems for automotive travel surveying. The authors evaluate data from Travis County, Texas for the purpose of determining where bicyclists ride, primarily for fitness purposes. Ride trip volumes are evaluated with residential and employment density, land use diversity, bicycle facilities and terrain to characterize places chosen for bicycling for health. Though limited to bicycle rides and routes voluntarily logged using the smartphone app, this method provides promise for applications in multi-modal transportation planning and health impact assessment studies.

show abstract

Section: Resultsmentioning

confidence: 99%

Where does bicycling for health happen? Analysing volunteered geographic information through place and plexus

Griffin

Jiao

2015

Journal of Transport & Health

View full text Add to dashboard Cite

show abstract

“…Li, Goodchild, and Xu (2013) show that the digital data footprint is very much related to various variables derived from the US census. Kent and Capello (2013) show that, even with a small number of available tweets on a wildfire in Wyoming, careful handling of this data can result in useful, hyper-local, insights. Similarly, in their study of a Lexington, KY riot through Twitter data, Crampton and et al (2013) show both the place-based nature as well as the scale-jumping that online social networks can exhibit.…”

Section: Understanding the Geography Of Twittermentioning

confidence: 98%

Follow thy neighbor: Connecting the social and the spatial networks on Twitter

Stephens

Poorthuis

2015

Computers, Environment and Urban Systems

View full text Add to dashboard Cite

“…Although spatial patterns in WEP harvests could be associated with spatial patterns in household, farm, and access explanatory variables, they might also suggest the presence of spatially structured factors not considered in the study such as environmental conditions that could affect WEP availability. A spatial analysis was thus conducted to determine the presence, or not, of spatial autocorrelation, i.e., clusters or hot-spots of higher WEP values (Kelly-Hope et al 2009, Kent andCapello 2013). Spatial analysis techniques mentioned in previous studies (Foody 2004, Pineda Jaimes et al 2010) were used and included, for each study area, a correlogram based on the global Moran's I statistic calculated at different distance lags using the Incremental Spatial Autocorrelation tool of ArcGIS and the estimation of the Getis-Ord Gi* statistic.…”

Section: Spatial Analysismentioning

confidence: 99%

Understanding the contribution of wild edible plants to rural social-ecological resilience in semi-arid Kenya

Shumsky¹,

Hickey²,

Pelletier³

et al. 2014

E&S

View full text Add to dashboard Cite

ABSTRACT. Wild edible plants (WEPs) are known to make important contributions to food baskets and livelihoods in the smallholder and subsistence farming communities of sub-Saharan Africa. As a result, protecting and promoting the sustainable use of WEPs in concert with more mainstream agricultural innovation efforts has the potential to build household resilience to food insecurity. There is, however, a need to better understand how WEPs contribute to rural livelihoods on a daily basis and act as emergency safety nets during periods of hunger. Focusing on two villages in rural eastern Kenya, we sought to determine which household conditions are correlated with household reliance on WEPs as a coping strategy during times of food insecurity, while also investigating the role of access restrictions on adaptive capacity and the ability to obtain these important food resources. Results reveal that reliance on WEPs is greater in households that report food insecurity, lack off-farm income, and have lower asset levels. Access to WEPs is also a major factor in consumption frequency, with smaller farm sizes and increased distance to harvest areas significantly correlated with lower levels of WEP use. By combining vulnerability and adaptive capacity measures for each household, we created a more complete accounting of the factors that influence WEP consumption frequency, with implications for policy. This study represents an important first step in taking a more holistic view of the subsistence value of WEPs and the myriad factors that influence households' reliance on, and ability to obtain, uncultivated natural resources.

show abstract

Spatial patterns and demographic indicators of effective social media content during theHorsethief Canyon fire of 2012

Cited by 98 publications

References 4 publications

Where does bicycling for health happen? Analysing volunteered geographic information through place and plexus

Where does bicycling for health happen? Analysing volunteered geographic information through place and plexus

Follow thy neighbor: Connecting the social and the spatial networks on Twitter

Understanding the contribution of wild edible plants to rural social-ecological resilience in semi-arid Kenya

Contact Info

Product

Resources

About