Foundational Codebook and Data:

Traffic camera images from the New York State Department of Transportation (511ny.org) are used to create a hand-labeled dataset of images classified into to one of six road surface conditions: 1) severe snow, 2) snow, 3) wet, 4) dry, 5) poor visibility, or 6) obstructed. Six labelers (authors Sutter, Wirz, Przybylo, Cains, Radford, and Evans) went through a series of four labeling trials where reliability across all six labelers were assessed using the Krippendorff’s alpha (KA) metric (Krippendorff, 2007). The online tool by Dr. Freelon (Freelon, 2013; Freelon, 2010) was used to calculate reliability metrics after each trial, and the group achieved inter-coder reliability with KA of 0.888 on the 4th trial. This process is known as quantitative content analysis, and three pieces of data used in this process are shared, including: 1) a PDF of the codebook which serves as a set of rules for labeling images, 2) images from each of the four labeling trials, including the use of New York State Mesonet weather observation data (Brotzge et al., 2020), and 3) an Excel spreadsheet including the calculated inter-coder reliability (ICR) metrics and other summaries used to asses reliability after each trial. The data are included in NYSDOT_quantitative_content_analysis.zip.

The broader purpose of this work is that the six human labelers, after achieving inter-coder reliability, can then label large sets of images independently, each contributing to the creation of larger labeled dataset used for training supervised machine learning models to predict road surface conditions from camera images. The xCITE lab (xCITE, 2023) is used to store camera images from 511ny.org, and the lab provides computing resources for training machine learning models.

Obstructed Class Variation:

There are many applications for labeling roadside camera images, and as a variation of the foundational codebook, an addendum codebook provides another version of labeling the obstructed class. Specifically, this variation prioritizes labeling an image as “obstructed” only in extreme circumstances where there is a camera- or image- specific problem that prevents the assessment of any road surfaces. For labelers who want to use this version of the obstructed class (in this document) and also the other five weather-related classes (in the foundational codebook), the guidance is to use both documents in tandem, making sure to use the obstructed rules/definitions in this document while disregarding the obstructed rules/definitions in the foundational codebook. Alternatively, this codebook may be used alone in applications where the goal is to solely classify obstructed vs not obstructed. To ensure reliability and quality of this variation, quantitative content analysis was conducted on this addendum codebook, just as it was for the foundational codebook. Two labelers were tested with a sample of 30 images and achieved inter-coder reliability with Krippendorff's Alpha of 0.934 after one trial. The data, including the addendum codebook and labeling trial data (images and results) are included in ObstructedVariation_quantitative_content_analysis.zip.

This material is based upon work supported by the U.S. National Science Foundation under Grant No. RISE-2019758.

The National Travel Survey (NTS) is a series of household surveys designed to provide regular, up-to-date data on personal travel and monitor changes in travel behaviour over time. The first NTS was commissioned by the Ministry of Transport in 1965. Further periodic surveys were carried out in 1972/73, 1975/76, 1978/79 and 1985/86 (the UK Data Service holds End-User Licence data from 1972 onwards and Special Licence and Secure Access data from 2002 onwards). Since July 1988 the NTS has been carried out as a continuous survey with field work being carried out in every month of the year, and an annual set sample of over 5,000 addresses. From 2002, the NTS sample was increased approximately threefold, to approximately 15,000 per year. The advantage of the continuous study is that users will be able to discern seasonal and cyclical movements as well as trend changes over time. The NTS is carried out primarily for the purposes of government. The most fundamental use of the National Travel Survey within the Department for Transport (DfT) is as core base data for key transport models. These are critical to the assessment and appraisal of transport scheme proposals (national and local), transport policy proposals, and contribute to the development of our long-term strategy. The NTS data is used to develop consistent sets of transport policies. Because it relates travel to travellers, it makes it possible to relate policies to people and to predict their impact. The survey provides detailed information on different types of travel: where people travel from and to, distance, purpose and mode. The NTS records personal and socio-economic information to distinguish between different types of people, and the differences in the way they travel and how often they do so. The NTS is the only source of national information on subjects such as walking which provide a context for the results of more local studies.

Further information may be found on the gov.uk National Travel Survey webpage.

End-User Licence, Special Licence and Secure Access NTS data
The UK Data Archive holds three versions of the NTS:

• the End User Licence (EUL) versions (SNs 5340 and 6108) contain a comprehensive range of NTS data at Government Office Region geographic level and should be sufficient for most research needs. EUL data are available to registered users of the UK Data Service (see the Administrative and Access section below for details).
• The Special Licence versions (SNs 7553 and 7804) contain more detailed travel (including accidents), demographic and socio-economic data, and the geographic level is Local Authority/Unitary Authority. Special Licence data are subject to more restricted access conditions.
• The Secure Access version (SN 7559) contains more detailed information and postcode sector geographies. Secure Access data are subject to further restricted access conditions, including the completion of a training course.

2020 and 2021 Disclaimer:
Due to changes in the methodology of data collection, changes in travel behaviour and a reduction of data collected during 2020 and 2021, as a result of the coronavirus (COVID-19) pandemic, care should be taken when interpreting this data and comparing to other years, due to the small sample sizes. Please see the background documentation for further details of these changes.

Latest edition information:
For the thirteenth edition (September 2024), data and documentation for 2023 have been added to the study.

Data labels
Users should note that the SPSS and Stata files for 2023 have been converted from CSV format and do not currently contain variable or value labels. Complete metadata information can be found in the Excel Lookup table files and the NTS Data Extract User Guide within the documentation.

The Travel Time to Work indicator compares the mean, or average, commute time for Champaign County residents to the mean commute time for residents of Illinois and the United States as a whole. On its own, mean travel time of all commuters on all mode types could be reflective of a number of different conditions. Congestion, mode choice, changes in residential patterns, changes in the location of major employment centers, and changes in the transit network can all impact travel time in different and often conflicting ways. Since the onset of the COVID-19 pandemic in 2020, the workplace location (office vs. home) is another factor that can impact the mean travel time of an area. We don’t recommend trying to draw any conclusions about conditions in Champaign County, or anywhere else, based on mean travel time alone.

However, when combined with other indicators in the Mobility category (and other categories), mean travel time to work is a valuable measure of transportation behaviors in Champaign County.

Champaign County’s mean travel time to work is lower than the mean travel time to work in Illinois and the United States. Based on this figure, the state of Illinois has the longest commutes of the three analyzed areas.

The year-to-year fluctuations in mean travel time have been statistically significant in the United States since 2014, and in Illinois in 2021 and 2022. Champaign County’s year-to-year fluctuations in mean travel time were statistically significant from 2021 to 2022, the first time since this data first started being tracked in 2005.

Mean travel time data was sourced from the U.S. Census Bureau’s American Community Survey (ACS) 1-Year Estimates, which are released annually.

As with any datasets that are estimates rather than exact counts, it is important to take into account the margins of error (listed in the column beside each figure) when drawing conclusions from the data.

Due to the impact of the COVID-19 pandemic, instead of providing the standard 1-year data products, the Census Bureau released experimental estimates from the 1-year data in 2020. This includes a limited number of data tables for the nation, states, and the District of Columbia. The Census Bureau states that the 2020 ACS 1-year experimental tables use an experimental estimation methodology and should not be compared with other ACS data. For these reasons, and because data is not available for Champaign County, no data for 2020 is included in this Indicator.

For interested data users, the 2020 ACS 1-Year Experimental data release includes a dataset on Travel Time to Work.

Sources: U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using data.census.gov; (16 October 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using data.census.gov; (10 October 2023).; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using data.census.gov; (17 October 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using data.census.gov; (29 March 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using data.census.gov; (29 March 2021).; U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (14 September 2017).; U.S. Census Bureau; American Community Survey, 2015 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (19 September 2016).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2012 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).

This paper reports the methods and results of the manual annotation of visual features in two corpora of tourism photography on travel boards' digital channels with a tailored tagging model based on the Grammar of Visual Design and adapted to tourism discourse. Computational analysis and statistical modeling show how the testing of theoretical assumptions through categorized data may lead to evidence-based interpretations of patterns of data clustering and to the detection of new communicative aims and conventions across digital media. Preliminary findings reveal indeed significant differences in the frequency of tag (co)patternings and use of visual strategies across channels that are related to the role and aim of each channel in the marketing funnel of persuasion and journey toward purchase (AIDA). Instagram imagery was demonstrated to foster a pre-consumption of the travel experience and emotionally charged reactions by representing perceptive and emotive expectations. While both channels play on postmodern tourists' desire for the uncontaminated, remote and the authentic, Instagram favors aerial views of pristine, aesthetically pleasant settings, often complemented with rear views of solitary individuals performing static processes of contemplation of natural wonders. This suggests a focus on attracting the attention and providing instant gratification of the senses by representing what stands in contrast to everyday life and traditional tourist experiences, both avoiding cognitive effort in a pervasive digital sphere with endless sources of information and encouraging further exploration on websites.

The National Travel Survey (NTS) is a series of household surveys designed to provide regular, up-to-date data on personal travel and monitor changes in travel behaviour over time. The first NTS was commissioned by the Ministry of Transport in 1965. Further periodic surveys were carried out in 1972/73, 1975/76, 1978/79 and 1985/86 (the UK Data Service holds End User Licence data from 1972 onwards and Special Licence and Secure Access data from 2002). Since July 1988 the NTS has been carried out as a continuous survey with field work being carried out in every month of the year, and an annual set sample of over 5,000 addresses. From 2002, the NTS sample was increased approximately threefold, to approximately 15,000 per year. The advantage of the continuous study is that users will be able to discern seasonal and cyclical movements as well as trend changes over time. The NTS is carried out primarily for the purposes of government. The most fundamental use of the National Travel Survey within the Department for Transport (DfT) is as core base data for key transport models. These are critical to the assessment and appraisal of transport scheme proposals (national and local), transport policy proposals, and contribute to the development of our long-term strategy. The NTS data is used to develop consistent sets of transport policies. Because it relates travel to travellers, it makes it possible to relate policies to people and to predict their impact. The survey provides detailed information on different types of travel: where people travel from and to, distance, purpose and mode. The NTS records personal and socio-economic information to distinguish between different types of people, and the differences in the way they travel and how often they do so. The NTS is the only source of national information on subjects such as walking which provide a context for the results of more local studies.

Further information may be found on the gov.uk National Travel Survey web page.

End-User Licence, Special Licence and Secure Access NTS data
The UK Data Archive also holds End User Licence (EUL) NTS data from 1972 onwards (see SNs 2852, 2853, 2855, 3288, 4108, 4583-4585, 6108 and 5340) and Special Licence (SL) NTS data from 1995 onwards (SNs 7804 and 7553). The EUL versions contain a comprehensive range of NTS data at Government Office Region geographic level and should be sufficient for most research needs. The SL versions contain more detailed travel (including accidents), demographic and socio-economic data, and the geographic level is Local Authority/Unitary Authority. These data are subject to more restricted access conditions than EUL. The Secure Access version contains more detailed information and postcode sector geographies. Secure Access data are subject to further restricted access conditions, including the completion of a training course. For full details of the differences between the EUL, SL and Secure Access NTS, see the document '7559_nts_table_structures.xls' in the documentation. Users should always check whether the EUL and SL versions are suitable for their research needs before considering making an application for the Secure Access version.

Latest edition information:
For the eleventh edition (September 2024), data and documentation for 2023 have been added to the study.

Data labels
Users should note that the SPSS and Stata files for 2023 have been converted from CSV format and do not currently contain variable or value labels. Complete metadata information can be found in the Excel Lookup table files and the NTS Data Extract User Guide within the documentation.

In attempting to increase the environmental awareness in the aviation sector and to eliminate the green washing phenomenon, an investigation was done into the development and definition of an ecolabel for aircraft. Based on life cycle assessment it was found that aviation affects the environment most with the impact categories resource depletion and global warming (both due to fuel consumption), local air pollution (due to the nitrogen oxide emissions in the vicinity of airports) and noise pollution. For each impact category a calculation method was developed based solely on official, certified and publicly available data to meet the stated requirements of the ISO standards about environmental labeling. To ensure that every parameter is evaluated independently on aircraft size, which allows comparison between different aircraft, normalizing factors such as number of seats, rated thrust and noise level limits are used. Additionally, a travel class weighting factor is derived in order to account for the space occupied per seat in first class, business class and economy class. To finalize the ecolabel, the overall environmental impact is determined by weighting the contribution of each impact category. For each category a rating scale from A to G is developed to compare the performance of the aircraft with that of others. The harmonization of the scientific and environmental information, presented in an easy understandable label, enables the traveling customers to make a well informed and educated choice when booking a flight, selecting among airline offers with different types of aircraft and seating arrangements.

Hot spring businesses in the jurisdiction of Taichung City with hot spring quality mark.