Understanding happiness in cities using Twitter: Jobs, children, and transport

Smart Cities and Healthcare: A Systematic Review

“…In terms of emotion surveillance, six articles were explored [17,[32][33][34][35][36]: [17] proposed a web-based portal through which individuals could provide personal details (e.g., age, gender, or household income) together with their feelings of wellbeing; [32] provided an overview of the relevant affective states and showed how they could be detected individually and then aggregated into a global model of affect, which could be used to promote an affect-aware city; [33] presented a smartphone application that analyzed individuals' emotions and their relation to different city areas; [34] attempted to map and correlate large-scale sentiment data to urban geography features, and consequently endeavored to understand the main sources of happiness in the city landscape; [35] explored various pre-processing methods to assess how they affected the performance of Twitter sentiment classifiers; and [36] aimed to present an ambient geographic information (AGI) approach to assemble geo-tagged data related to an individuals' perception and feelings about a city from Twitter, Flickr, Instagram, and Facebook.…”

“…Data from smart city infrastructure [16,19,25,[29][30][31]48,49,52] Data provided by sensors inside vehicles [28,51] Data provided by video cameras [50,56] Data provided by gas sensors [37] Geo-tagged social media data [34][35][36]56] Data collected by online questionnaire [17,33] Data provided by lifestyle monitoring devices: Location [16,21,30,31,39,40,43,45,[48][49][50]55] Activity [18,20,24,31,41,42,48,49 [47] Finally, two articles reported on the use of social media [38,60] and one article, [22], reported on the development of an ambulance robot equipped with an AED. Using multiple sensors for navigation (vision and range sensors) this robot might be able to navigate from a point to a given destination without losing the correct path or hitting obstacles [22].…”

“…In terms of maturity level, different development stages were identified (Figure 4): eight articles proposed concepts for further development [18,25,29,32,36,40,46,50]; two articles [32,41] related to the same project reported on the ongoing activities to elicit the requirements using an interactive design approach (i.e., 35 care receivers were actively involved in all phases of the project to validate the data detection and intervention services); eight articles proposed theoretical models and applied simulation techniques to validate them [19,23,35,44,52,53,58,59] (in some cases, real data were used such as from the London Ambulance Service [53] or data available for the 27,775 citizens living in Copenhagen [58]); eight articles defined architectures [24,27,31,40,48,49,55,56], and some of them were validated (e.g., some of the components of the architecture were implemented for its validation [24] or a simulation of a use case was performed for the conceptual validation of the architecture [56]); 14 articles presented prototypes that were developed to demonstrate the feasibility of the concepts [16,17,21,22,28,30,33,34,37,…”

“…Copenhagen [58]); eight articles defined architectures [24,27,31,40,48,49,55,56], and some of them were validated (e.g., some of the components of the architecture were implemented for its validation [24] or a simulation of a use case was performed for the conceptual validation of the architecture [56]); 14 articles presented prototypes that were developed to demonstrate the feasibility of the concepts [16,17,21,22,28,30,33,34,37,47,51,54,57,60]; and four articles reported on prototypes that were assessed by real users [20,38,43,45].…”

Caracterização de eventos de exceção e de seus respectivos impactos no sistema de transporte público por ônibus da cidade de São Paulo

“…Outro estudo que contribui com o planejamento do transporte público, é o realizado em (GKIOTSALITIS;STATHOPOULOS, 2015STATHOPOULOS, , 2016, no qual tweets foram processados para identificar a disposição dos usuários para realizar viagens relacionadas ao lazer (pontos de interesse), sugerindo a eles atividades com menor tempo de percurso e probabilidade de atrasos. Além do tempo de percurso, outro ponto relevante considerado foi o de bom nível de acesso ao transporte público, o qual quando existente impacta positivamente na felicidade das pessoas e se correlaciona com sentimentos positivos, segundo a análise de sentimentos realizada por(GUO et al, 2016), utilizando tweets publicados na Grande Londres.3.5.3 Paradigmas de processamento (QP3)Nesta seção, encontram-se a seguir apenas os paradigmas de processamento extraídos dos estudos primários analisados:A. Processamento em lote (batch, offline processing)(ANANTHARAM et al, 2015; WEN; LIN; PELECHRINIS, 2016;FARSEEV et al, 2015;GUTEV;NENKO, 2016; MATA; CLARAMUNT, 2015;CHEN et al, 2016;ABBASI et al, 2015;BENDLER et al, 2014; YOUSAF et al, 2014;FRIAS-MARTINEZ;FRIAS-MARTINEZ, 2014;STEIGER et al, 2015;GAL-TZUR et al, 2014; GKI- OTSALITIS;STATHOPOULOS, 2016; LORENZO et al, 2013;ITOH et al, 2016; CHANIOTAKIS; ANTONIOU, 2015);B. processamento em quase tempo real (Near real time) (MUKHERJEE et al, 2015); C. processamento em tempo real (Real time processing) (SOOMRO; KHAN; HASHAM, 2016; LéCUé et al, 2014). 3.5.4 Eventos de exceção relacionados ao transporte público (QP4) Nesta seção, encontram-se a seguir os eventos de exceção que podem ser relacionados ao transporte público, extraídos dos estudos primários: A. Acidentes (ITOH et al, 2016): a) acidentes nas estações transporte; b) incêndio.…”