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Tribe 54 Group

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Travel To The Unknown

Wondering what we mean by off-the-beaten-track travel?From culture, festivals and wildlife to culinary, archaeology and trekking ...Take a glimpse of what you can expect on a Travel The Unknown tourand book your next small group or tailormade tour with us.Click here to see more destination videos.

Travel to the Unknown

At Explorer X, we handcraft deeper, culturally authentic, and truly immersive travel experiences that focus as much on WHY and HOW we travel as they do on WHERE and WHAT. We support our travelers in \"traveling with their lights on\" and using those experiences to transform their lives AND the lives of those they meet along and the way.

Isaac I Bogoch, Alexander Watts, Andrea Thomas-Bachli, Carmen Huber, Moritz U G Kraemer, Kamran Khan, Pneumonia of unknown aetiology in Wuhan, China: potential for international spread via commercial air travel, Journal of Travel Medicine, Volume 27, Issue 2, March 2020, taaa008,

There is currently an outbreak of pneumonia of unknown aetiology in Wuhan, China. Although there are still several unanswered questions about this infection, we evaluate the potential for international dissemination of this disease via commercial air travel should the outbreak continue.

It's easy to understand why the cross-continental trail is so quick to capture people's imaginations, and that's due to the story of the first European known to have walked its length: Portuguese sailor Aleixo Garcia. Shipwrecked in 1516 on the shores of southern Brazil after a failed Spanish mission to navigate the River Plate, Garcia and half a dozen other sailors were taken in by the amenable Guaranis. Eight years later, after hearing Guarani tales of a path that led all the way to an empire in the mountains rich in gold and silver, Garcia travelled with 2,000 Guarani warriors all the way to the Andes, nearly 3,000km away. According to Brazilian researcher Rosana Bond in her e-book The Saga of Aleixo Garcia, he became the first European known to have visited the Incan empire, in 1524, nearly a decade before the Spanish conquistador Francisco Pizarro who is widely believed to have made that "discovery".

I'd travelled to Peabiru to test one of them out: a forest trail that takes in seven waterfalls along the course of a river. The banks of the river would almost certainly have been part of the Caminho de Peabiru, my guide Arléto Rocha told me as we walked, climbing under and over fallen trees and then wading up to our knees in the river's cold water, washing the rotten fruit from my soles. Not content with just getting his boots wet, Rocha dived into a waterfall fully clothed. Further on, he pointed out spots where he'd found arrowheads, mortars, rock engravings and other archaeological gems over the past decade, now on display in the recently inaugurated Museu Municipal Caminhos de Peabiru.

To continue my search for remnants of the trail, I travelled down to the coast of the neighbouring state of Santa Catarina to Enseada dos Britos, a tranquil bay where historians believe Garcia lived and from where he would have departed on his mission to the Incan empire. This is the starting point for yet another hike inspired by the Caminho de Peabiru, a 25km route that takes in beaches, the sand dunes of a state park and a visit to two Guarani villages. Limbering up for the 25km hike, I tried to picture Garcia and his band of unshaven, sunburnt castaways, thousands of miles from home, settling into their new Guarani digs here after losing their ship.

We were both bitten by the travel bug back in 1996 whilst we were based in Germany as exchange students. Since then, we have travelled to over 70 countries being particularly drawn to remote places where local customs and cultures had not been impacted by tourism.

The Philippines is slowly showcasing its beauty one island after the other, with places like Boracay and Palawan becoming ever more popular. But, there are still many unknown spots to be discovered. Tucked in behind the rocky mountains of a small province called Surigao Del Sur lies this fairytale-like river. Dive into the caves of the enchanted river without being disturbed by crowds of people.

Using simulated data from New Hampshire and Arizona, we estimated travel times to nearest cancer centers by using: 1) geometric centroid of ZIP code polygons as origins, 2) population centroids as origin, 3) service area rings around each cancer center, assigning subjects to rings by assuming they are evenly distributed within their ZIP code, 4) service area rings around each center, assuming the subjects follow the population distribution within the ZIP code. We used travel times based on street addresses as true values to validate estimates. Population-based methods have smaller errors than geometry-based methods. Within categories (geometry or population), centroid and service area methods have similar errors. Errors are smaller in urban areas than in rural areas.

Population-based methods are superior to the geometry-based methods, with the population centroid method appearing to be the best choice for estimating travel time. Estimates in rural areas are less reliable.

Spatial accessibility is an important factor in assessing overall access to healthcare, and road-network-based travel time has become a popular way to measure this component of accessibility. As geographic information systems (GIS) become increasingly available, public health practitioners and researchers now have easy-to-use tools to calculate travel times that once were technically and computationally beyond the reach of most, especially for large datasets. A challenge remains, however, in how to estimate subjects' travel times when the exact address is not available. Due to confidentiality and/or data quality reasons, point location information of subjects is often aggregated to larger areal units. The ZIP code is often the finest granularity of geography available to health researchers, and studies of healthcare accessibility or distance are commonly based on travel times estimated from a subject's ZIP code to a known destination. While ZIP codes are actually collection of postal delivery routes that are modifiable at the level of the postmaster, they are still a commonly used geographic unit in health research. An important question that arises is how to measure a distance from a polygon (i.e. ZIP code area) to a point (i.e. exact, known address of a destination or a facility). Different strategies for handling this issue may result in varying outputs, which may in turn lead to different conclusions about accessibility. We are not aware of any formal comparison and validation of different methods in the public health literature.

Practical methods of estimating travel time from aggregate data can be based on point model or polygon models. The point model assumes that all subjects within an areal unit are concentrated on a single point, whereas the polygon model assumes that the subjects are spread across the unit. Both models can take either a geometric or population-based approach: (1) a geometric point method uses the geometric centroid of an areal unit as the origin for all the subjects from that unit when calculating travel times [1, 2]; (2) a population-based point method uses the population centroid of an areal unit as the origins for the subjects [3]; (3) a geometric polygon method creates travel time zones around facilities and assigns subjects assuming an even distribution across an aggregate unit [4, 5]; (4) a population-based polygon method assigns subjects to a travel time zones under the assumption that the locations of the subjects within a unit follows the distribution of the population in that unit.

The data of ZIP code areas, urban areas, road network, and Census Block centroids and their associated population were from data included in our GIS package (ArcGIS, ESRI, Redlands, CA). Source data for ZIP Code areas and Census Blocks were from TeleAtlas North America and ESRI, with source data for Urban Areas from the US Census and ESRI. The calculation of travel time was based on the 2006 ESRI street network dataset. These data are based on U.S. Census TIGER line data for street centerlines, with Census Feature Class Codes (CFCC) assigned to each line segment to provide speed limit information. CHCC codes classify road types and assign a corresponding speed limit (i.e. CFCC code 'A00' is classed as Road: Major and minor categories unknown and given the speed limit 40 mph). LandScan Global population data, for characterizing the spatial distribution of population within a ZIP code area, were obtained from the Oak Ridge National Laboratory, described below [9]. All data were transformed to their corresponding State Plane Coordinate Systems to minimize errors in distance and area calculations.

Use of roads by motorized vehicles is often the preferred method of travel to reach health facilities in developed countries [10]. We therefore calculated travel time based on the travel distance over the road network from an origin to a destination and speed limits for each segment of road using Network Analyst in ArcGIS. Details of the implementations of the four methods of approximation are as follows and illustrated in Figure 1.

For each ZIP code area, the geometric centroid (the point at the geometric center of the area) was used as the common origin of all the subjects falling into that ZIP code. The assumption of this method was that all the subjects live at the geometric center of the area, and consequently all have the same travel time and use the same nearest facility. 041b061a72


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