An Overview of the NOAA ENC Re-Scheming Plan

The scheme (or footprints) of NOAA Electronic Navigational Charts (ENCs) are based on traditional paper/raster charts from which they were derived. As a result, modernizing current ENC coverage to improve the way they are displayed in a digital environment, increase their level of detail, and incorporate additional survey data outside of the existing bounds is complex. As part of NOAA’s “ENC-First” effort, a re-scheming approach was developed to provide a seamless, tiled coverage that can easily be segmented or extended based on geographic location, available data and scale. In this new regular gridded ENC coverage approach, fewer than a dozen chart scales are used (down from the current 130 scales used in the paper chart scheme). The re-scheme plan also aims to improve products for mariners who prefer paper charts. The uniform scales will enable mariners to create customized charts with a new online application. Additionally, NOAA has created one production line for both ENC and Raster Navigational Charts (RNCs) products that will reduce production resources for maintaining two chart products.El esquema (o el bosquejo) de las Cartas Náuticas Electrónicas (ENCs) de la NOAA se basa en las cartas tradicionales de papel/ráster de las que se derivaron. Como resultado, la modernización de la cobertura ENC actual para mejorar el modo en el que se visualizan en un entorno digital, aumentar su nivel de detalle e incorporar datos de levantamientos adicionales fuera de los límites existentes, es compleja. Como parte del esfuerzo de la NOAA «ENC-First», se desarrolló un enfoque en materia de restructuración para proporcionar una cobertura sin interrupciones y en forma de mosaico, que puede ser fácilmente segmentada o ampliada basándose en la ubicación geográfica, los datos disponibles y la escala. En este nuevo enfoque de cobertura ENC reticulada regular se utilizan menos de una docena de escalas de cartas (por debajo de las 130 escalas actuales utilizadas en el esquema de cartas de papel). El plan de restructuración también tiene por objeto mejorar los productos para los navegantes que prefieran las cartas de papel. Las escalas uniformes permitirán a los navegantes crear cartas personalizadas con una nueva aplicación en línea. Además, la NOAA ha creado una línea de producción para ambos productos, las cartas ENCs y Ráster (RNCs), que reducirá los recursos de producción para el mantenimiento de dos productos cartográficos.Les schémas (ou empreintes) des cartes électroniques de navigation (ENC) de la NOAA sont basés sur les cartes traditionnelles papier/raster à partir desquelles ils ont été tirés. En conséquence, la modernisation de la couverture en ENC actuelle afin d’améliorer la manière dont ces dernières sont affichées dans un environnement numérique, d’accroître leur niveau de détail, et d’incorporer des données de levés supplémentaires au-delà des limites existantes, se révèle compliquée. Dans le cadre de l’initiative « ENC-First » de la NOAA, une approche de reschématisation a été développée afin de fournir une couverture maillée continue pouvant aisément être segmentée ou étendue en se basant sur la localisation géographique, les données disponibles et l’échelle.  Dans le cadre de cette nouvelle approche d’une couverture en ENC maillée de manière régulière, moins d’une douzaine d’échelles cartographiques sont utilisées (bien moins que les 130 échelles actuellement utilisées dans le schéma de cartes papier). Le plan de re-schématisation vise également à améliorer les produits pour les navigateurs qui préfèrent les cartes papier. L’uniformité des échelles permettra aux navigateurs de créer des cartes personnalisées avec une nouvelle application en ligne. En outre, la NOAA a créé une ligne de production à la fois pour les produits ENC et pour les cartes de navigation raster (RNC), ce qui réduira les ressources de production pour la tenue à jour de deux produits cartographiques

The effect of intra-urban mobility flows on the spatial heterogeneity of social media activity: investigating the response to rainfall events

Although it is acknowledged that urban inequalities can lead to biases in the production of social media data, there is a lack of studies which make an assessment of the effects of intra-urban movements in real-world urban analytics applications, based on social media. This study investigates the spatial heterogeneity of social media with regard to the regular intra-urban movements of residents by means of a case study of rainfall-related Twitter activity in São Paulo, Brazil. We apply a spatial autoregressive model that uses population and income as covariates and intra-urban mobility flows as spatial weights to explain the spatial distribution of the social response to rainfall events in Twitter vis-à-vis rainfall radar data. Results show high spatial heterogeneity in the response of social media to rainfall events, which is linked to intra-urban inequalities. Our model performance (R2=0.80) provides evidence that urban mobility flows and socio-economic indicators are significant factors to explain the spatial heterogeneity of thematic spatiotemporal patterns extracted from social media. Therefore, urban analytics research and practice should consider not only the influence of socio-economic profile of neighborhoods but also the spatial interaction introduced by intra-urban mobility flows to account for spatial heterogeneity when using social media data

Method for Determining Appropriate Clustering Criteria of Location-Sensing Data

Large quantities of location-sensing data are generated from location-based social network services. These data are provided as point properties with location coordinates acquired from a global positioning system or Wi-Fi signal. To show the point data on multi-scale map services, the data should be represented by clusters following a grid-based clustering method, in which an appropriate grid size should be determined. Currently, there are no criteria for determining the proper grid size, and the modifiable areal unit problem has been formulated for the purpose of addressing this issue. The method proposed in this paper is applies a hexagonal grid to geotagged Twitter point data, considering the grid size in terms of both quantity and quality to minimize the limitations associated with the modifiable areal unit problem. Quantitatively, we reduced the original Twitter point data by an appropriate amount using Töpfer’s radical law. Qualitatively, we maintained the original distribution characteristics using Moran’s I. Finally, we determined the appropriate sizes of clusters from zoom levels 9–13 by analyzing the distribution of data on the graphs. Based on the visualized clustering results, we confirm that the original distribution pattern is effectively maintained using the proposed method

Using social media data to understand the urban green space use before and after a pandemic

Urban green spaces (UGSs) are essential components of urban ecosystems that provide considerable benefits to residents, including recreational opportunities, improved air and water quality, and mental and physical health benefits. The COVID-19 pandemic and related restriction measures have affected people's daily lives in numerous ways, such as remote working and learning, online shopping, social distancing, travel restrictions, and outdoor activities. During the COVID-19 pandemic, UGSs have become the main places for outdoor activities. Understanding human-environment interactions in UGSs is an important research field that has broad implications for improving policies in response to a social crisis and informing urban planning strategies. The main challenges of investigating human-environment interactions lie in effectively collecting research datasets that can reflect or reveal human behaviour patterns within UGSs. Volunteered Geographical Information (VGI) and social media can provide better information about real-time perceptions, attitudes and behaviours than traditional datasets such as surveys and questionnaires. This provides great opportunities to investigate human-environment interactions in UGS in real-time. Additionally, Twitter is one of the most popular social networks, and it can provide more comprehensive and unbiased datasets through a new academic research Application Programming Interface (API). The overall aim of this thesis is to evaluate the contributions of UGS to human well-being, during a time of crisis, by investigating the characteristics and spatial-temporal patterns of UGS use across three periods: pre-, during- and after the COVID-19 pandemic. The thesis will document the process of examining spatial-temporal changes in UGS use associated with COVID-19 related pandemic, by using Twitter datasets incorporating approaches including text mining, topic modelling and spatial-temporal analysis. This is the first study to examine social media data over consistent time period before, during and after the lockdown in relation to UGS. The results show that the findings and method can potentially inform policy makers in their management and planning of UGS, especially in a period of social crisis like the COVID-19 pandemic. This research has great potential to help improve urban green space planning and management in urban areas