Annotating climate data with commentary: the CHARMe project

The CHARMe project enables the annotation of climate data with key pieces of supporting information that we term “commentary”. Commentary reflects the experience that has built up in the user community, and can help new or less-expert users (such as consultants, SMEs, experts in other fields) to understand and interpret complex data. In the context of global climate services, the CHARMe system will record, retain and disseminate this commentary on climate datasets, and provide a means for feeding back this experience to the data providers. Based on novel linked data techniques and standards, the project has developed a core system, data model and suite of open-source tools to enable this information to be shared, discovered and exploited by the community.

Capturing and sharing our collective expertise on climate data: the CHARMe project

For users of climate services, the ability to quickly determine the datasets that best fit one's needs would be invaluable. The volume, variety and complexity of climate data makes this judgment difficult. The ambition of CHARMe ("Characterization of metadata to enable high-quality climate services") is to give a wider interdisciplinary community access to a range of supporting information, such as journal articles, technical reports or feedback on previous applications of the data. The capture and discovery of this "commentary" information, often created by data users rather than data providers, and currently not linked to the data themselves, has not been significantly addressed previously. CHARMe applies the principles of Linked Data and open web standards to associate, record, search and publish user-derived annotations in a way that can be read both by users and automated systems. Tools have been developed within the CHARMe project that enable annotation capability for data delivery systems already in wide use for discovering climate data. In addition, the project has developed advanced tools for exploring data and commentary in innovative ways, including an interactive data explorer and comparator ("CHARMe Maps") and a tool for correlating climate time series with external "significant events" (e.g. instrument failures or large volcanic eruptions) that affect the data quality. Although the project focuses on climate science, the concepts are general and could be applied to other fields. All CHARMe system software is open-source, released under a liberal licence, permitting future projects to re-use the source code as they wish.

Communicating thematic data quality with web map services

Geospatial information of many kinds, from topographic maps to scientific data, is increasingly being made available through web mapping services. These allow georeferenced map images to be served from data stores and displayed in websites and geographic information systems, where they can be integrated with other geographic information. The Open Geospatial Consortium’s Web Map Service (WMS) standard has been widely adopted in diverse communities for sharing data in this way. However, current services typically provide little or no information about the quality or accuracy of the data they serve. In this paper we will describe the design and implementation of a new “quality-enabled” profile of WMS, which we call “WMS-Q”. This describes how information about data quality can be transmitted to the user through WMS. Such information can exist at many levels, from entire datasets to individual measurements, and includes the many different ways in which data uncertainty can be expressed. We also describe proposed extensions to the Symbology Encoding specification, which include provision for visualizing uncertainty in raster data in a number of different ways, including contours, shading and bivariate colour maps. We shall also describe new open-source implementations of the new specifications, which include both clients and servers.

Adaptive climate change governance for urban resilience

Climate change poses new challenges to cities and new flexible forms of governance are required that are able to take into account the uncertainty and abruptness of changes. The purpose of this paper is to discuss adaptive climate change governance for urban resilience. This paper identifies and reviews three traditions of literature on the idea of transitions and transformations, and assesses to what extent the transitions encompass elements of adaptive governance. This paper uses the open source Urban Transitions Project database to assess how urban experiments take into account principles of adaptive governance. The results show that: the experiments give no explicit information of ecological knowledge; the leadership of cities is primarily from local authorities; and evidence of partnerships and anticipatory or planned adaptation is limited or absent. The analysis shows that neither technological, political nor ecological solutions alone are sufficient to further our understanding of the analytical aspects of transition thinking in urban climate governance. In conclusion, the paper argues that the future research agenda for urban climate governance needs to explore further the links between the three traditions in order to better identify contradictions, complementarities or compatibilities, and what this means in practice for creating and assessing urban experiments.

CHP toolkit: case study of LAIe sensitivity to discontinuity of canopy cover in fruit plantations

This paper presents an open-source canopy height profile (CHP) toolkit designed for processing small-footprint full-waveform LiDAR data to obtain the estimates of effective leaf area index (LAIe) and CHPs. The use of the toolkit is presented with a case study of LAIe estimation in discontinuous-canopy fruit plantations. The experiments are carried out in two study areas, namely, orange and almond plantations, with different percentages of canopy cover (48% and 40%, respectively). For comparison, two commonly used discrete-point LAIe estimation methods are also tested. The LiDAR LAIe values are first computed for each of the sites and each method as a whole, providing “apparent” site-level LAIe, which disregards the discontinuity of the plantations’ canopies. Since the toolkit allows for the calculation of the study area LAIe at different spatial scales, between-tree-level clumpingcan be easily accounted for and is then used to illustrate the impact of the discontinuity of canopy cover on LAIe retrieval. The LiDAR LAIe estimates are therefore computed at smaller scales as a mean of LAIe in various grid-cell sizes, providing estimates of “actual” site-level LAIe. Subsequently, the LiDAR LAIe results are compared with theoretical models of “apparent” LAIe versus “actual” LAIe, based on known percent canopy cover in each site. The comparison of those models to LiDAR LAIe derived from the smallest grid-cell sizes against the estimates of LAIe for the whole site has shown that the LAIe estimates obtained from the CHP toolkit provided values that are closest to those of theoretical models.

In the mood: the dynamics of collective sentiments on Twitter

We study the relationship between the sentiment levels of Twitter users and the evolving network structure that the users created by @-mentioning each other. We use a large dataset of tweets to which we apply three sentiment scoring algorithms, including the open source SentiStrength program. Specifically we make three contributions. Firstly we find that people who have potentially the largest communication reach (according to a dynamic centrality measure) use sentiment differently than the average user: for example they use positive sentiment more often and negative sentiment less often. Secondly we find that when we follow structurally stable Twitter communities over a period of months, their sentiment levels are also stable, and sudden changes in community sentiment from one day to the next can in most cases be traced to external events affecting the community. Thirdly, based on our findings, we create and calibrate a simple agent-based model that is capable of reproducing measures of emotive response comparable to those obtained from our empirical dataset.