title: Logistics sprawl and the change in freight transport activity: A comparison of three measurement methodologies

authors: Trent, Nadia M.

subjects: Social Sciences

abstract: Logistics sprawl has been observed in many urban areas around the world, but the assumed link between increased logistics sprawl and increased freight transport activity has little empirical evidence. Because this link could influence policy-making to either fight or facilitate logistics sprawl, this paper investigates the implications of using different methodologies to quantify the relationship. Three different methodologies are compared. The first two methodologies are from well-respected sources in literature and the third is proposed in this paper. The methodologies measure transport activity related to logistics sprawl in three urban areas in South Africa between 2010 and 2014. The results from the methodologies are contradicting with the methodology proposed in this paper questioning the link between logistics sprawl and freight transport activity altogether. The comparison of the methodologies also shows that it is essential to include empirical data of actual vehicle movement when investigating logistics sprawl's impact on transport activity.

text: Logistics sprawl and the change in freight transport activity: A comparison of three measurement methodologies Logistics sprawl has been observed in many urban areas around the world, but the assumed link between increased logistics sprawl and increased freight transport activity has little empirical evidence. Because this link could influence policy-making to either fight or facilitate logistics sprawl, this paper investigates the implications of using different methodologies to quantify the relationship. Three different methodologies are compared. The first two methodologies are from well-respected sources in literature and the third is proposed in this paper. The methodologies measure transport activity related to logistics sprawl in three urban areas in South Africa between 2010 and 2014. The results from the methodologies are contradicting with the methodology proposed in this paper questioning the link between logistics sprawl and freight transport activity altogether. The comparison of the methodologies also shows that it is essential to include empirical data of actual vehicle movement when investigating logistics sprawl's impact on transport activity.

year: 2022

title: Transferring sentiment knowledge between words and tweets

authors: Bravo-Marquez, Felipe

subjects: computer science

abstract: Message-level and word-level polarity classification are two popular tasks in Twitter sentiment analysis. They have been commonly addressed by training supervised models from labelled data. The main limitation of these models is the high cost of data annotation. Transferring existing labels from a related problem domain is one possible solution for this problem. In this paper, we study how to transfer sentiment labels from the word domain to the tweet domain and vice versa by making their corresponding instances compatible. We model instances of these two domains as the aggregation of instances from the other (i.e., tweets are treated as collections of the words they contain and words are treated as collections of the tweets in which they occur) and perform aggregation by averaging the corresponding constituents. We study two different setups for averaging tweet and word vectors: 1) representing tweets by standard NLP features such as unigrams and part-of-speech tags and words by averaging the vectors of the tweets in which they occur, and 2) representing words using skip-gram embeddings and tweets as the average embedding vector of their words. A consequence of our approach is that instances of both domains reside in the same feature space. Thus, a sentiment classifier trained on labelled data from one domain can be used to classify instances from the other one. We evaluate this approach in two transfer learning tasks: 1) sentiment classification of tweets by applying a word-level sentiment classifier, and 2) induction of a polarity lexicon by applying a tweet-level polarity classifier. Our results show that the proposed model can successfully classify words and tweets after transfer.

text: Transferring sentiment knowledge between words and tweets Message-level and word-level polarity classification are two popular tasks in Twitter sentiment analysis. They have been commonly addressed by training supervised models from labelled data. The main limitation of these models is the high cost of data annotation. Transferring existing labels from a related problem domain is one possible solution for this problem. In this paper, we study how to transfer sentiment labels from the word domain to the tweet domain and vice versa by making their corresponding instances compatible. We model instances of these two domains as the aggregation of instances from the other (i.e., tweets are treated as collections of the words they contain and words are treated as collections of the tweets in which they occur) and perform aggregation by averaging the corresponding constituents. We study two different setups for averaging tweet and word vectors: 1) representing tweets by standard NLP features such as unigrams and part-of-speech tags and words by averaging the vectors of the tweets in which they occur, and 2) representing words using skip-gram embeddings and tweets as the average embedding vector of their words. A consequence of our approach is that instances of both domains reside in the same feature space. Thus, a sentiment classifier trained on labelled data from one domain can be used to classify instances from the other one. We evaluate this approach in two transfer learning tasks: 1) sentiment classification of tweets by applying a word-level sentiment classifier, and 2) induction of a polarity lexicon by applying a tweet-level polarity classifier. Our results show that the proposed model can successfully classify words and tweets after transfer.

year: 2018

title: Ōpōtiki Mai Tawhiti

authors: Maxwell, Te Kahautu

abstract: Ōpōtiki Mai Tawhiti competed at the Mātaatua Regional Kapa Haka Competition 2014 to qualify for the Te Matatini World Championship Kapa Haka Competition 2015, Christchurch. Ōpōtiki Mai Tawhiti placed first overall, 1st mōteatea, 1st, Haka, 1st Whakawātea, 1st Female Leader, 1st Male Leader, 1st Waiata Tira, 1st Te Reo, 1st Original Composition, 1st Costume, 2nd Waiata a Ringa, 2nd, Poi, 2nd Whakaeke.

text: Ōpōtiki Mai Tawhiti Ōpōtiki Mai Tawhiti competed at the Mātaatua Regional Kapa Haka Competition 2014 to qualify for the Te Matatini World Championship Kapa Haka Competition 2015, Christchurch. Ōpōtiki Mai Tawhiti placed first overall, 1st mōteatea, 1st, Haka, 1st Whakawātea, 1st Female Leader, 1st Male Leader, 1st Waiata Tira, 1st Te Reo, 1st Original Composition, 1st Costume, 2nd Waiata a Ringa, 2nd, Poi, 2nd Whakaeke.

year: 2014-02-22