Digital heritage research re-theorised: ontologies and epistemologies in a world of big data

ABSTRACT

This article provides the first theoretical treatment of the ontologies and epistemologies of digital heritage research at the time of the interconnected and social web, based on extensive empirical and analytical investigation. We draw on observations and concepts developed while conducting the first study of public experiences of the past that utilised big data – over 1.4 million Facebook posts, comments and replies – to revisit or generate new theory from the ground up. We expect that this will help scholars from a range of fields in the humanities, social and computing sciences who are interested in undertaking digital heritage research to understand the deeper implications of their work, the complexities and limitations of the knowledge they create, and its value in exposing the processes via which heritage is made and assessed.     

Discovering the Language of Data: Personal Pattern Languages and the Social Construction of Meaning from Big Data

Abstract

This paper attempts to address two issues relevant to the sense-making of Big Data. First, it presents a case study for how a large dataset can be transformed into both a visual language and, in effect, a ‘text’ that can be read and interpreted by human beings. The case study comes from direct observation of graduate students at the IIT Institute of Design who investigated task-switching behaviours, as documented by productivity software on a single user’s laptop and a smart phone. Through a series of experiments with the resulting dataset, the team effects a transformation of that data into a catalogue of visual primitives — a kind of iconic alphabet — that allow others to ‘read’ the data as a corpus and, more provocatively, suggest the formation of a personal pattern language. Second, this paper offers a model for human-technical collaboration in the sense-making of data, as demonstrated by this and other teams in the class. Current sense-making models tend to be data- and technology-centric, and increasingly presume data visualization as a primary point of entry of humans into Big Data systems. This alternative model proposes that meaningful interpretation of data emerges from a more elaborate interplay between algorithms, data and human beings.     

李鸿章集外文考论

李鸿章系晚清重臣,其个人文集是学者研究晚清历史的重要史料。安徽教育出版社于2008年1月出版的《李鸿章全集》,为学术界全面深入研究李鸿章带来了极大方便。然因载籍甚众,全集难免有遗珠之憾。笔者在查阅相关资料的过程中,发现李鸿章的七篇集外文,兹加以迻出,并略作考述,以供再版时参考。     

Assessing Recovery from the 2004 Indian Ocean Tsunami: An Application of Night‐time Light Data and Vegetation Index

It has been 10 years since the Indian Ocean Tsunami caused serious damage to the coastal areas in South and Southeast Asia. The effects on vegetation and human settlements in the affected areas were enormous. This study presents the results of an analysis estimating the long‐term recovery using two longitudinal remotely sensed dataset: 1. Moderate Resolution Imaging Spectroradiometer enhanced vegetation index (MODIS EVI), a dataset accounting for change in the landscape and vegetation; and 2. Defense Meteorological Satellite Program‐Optical Line Scanner (DMSP‐OLS) night‐time light data in order to estimate the effects on human and economic activities. It is evident from the results of this study that the night‐time light and vegetation index datasets can both be beneficial in identifying changes caused by natural disasters and can be used to track recovery. The results using night‐time light indicates a large loss of lighted area but also a rapid recovery of night‐time light after the tsunami. Already in year 2005–2006, the levels of lighted area and sum of the lighting (SOL) intensity reached the same levels as pre‐tsunami. For MODIS vegetation index, a drop can be observed in 2005/2006 on locations close to the coastline using 1 year temporal resolution; however, when utilizing the 16 day temporal resolution, the impact of the tsunami is illustrated as a dramatic drop, mostly in pixels located within 3km from the coast. Following the drop in vegetation index due to the tsunami, it was observed that most pixels exhibited at least some level of recovery in 2 years after the event.     

When open data and data activism meet: An analysis of civic participation in Cape Town,South Africa

Municipal open data projects are motivated by a desire to democratize data access and knowledge production, strengthen transparency, and advance cities socially and economically. However, their effects and implications are insufficiently analyzed. This paper examines civic engagement in open data in Cape Town, South Africa, the continent's first municipal-level open data initiative. Findings reveal how local civil society organizations have been driving engagement with municipal open data as part of their recent turn towards technology and data-driven forms of public engagement and activism. This analysis highlights the important role of the “smart civil society organization”—occupying a position between the smart city and smart citizen—that is developing significant capacity to produce and share data about the city's informal settlements with stakeholders in government, the private sector, and wider society. Minimal engagement with or recognition of civil society efforts illustrates the limits to the city's philosophy of data openness, which is largely restricted to releasing selected government datasets to the public. The notion of “bi-directional open data” is developed here to characterize emerging possibilities for data openness between governments and the public. This may be particularly relevant for cities like Cape Town with a highly active, capable, and data-literate civil society.     

A theatre of machines: Automata circuses and digital bread in the smart city of Toronto

In this paper, the policies, projects, and promises of “smart” initiatives at the City of Toronto are evaluated, as they manifest through a technological convergence between local government services and an increased focus on citizen services through data-driven mediums. Through direct participant observation and formal interviews, a robust understanding of the internal institutional dynamics, the perspectives citizens in the “smart city,” and the operational disconnects in governance, policy, and practice has been gained. Our case study on the City of Toronto provides an account of how and from where these smart motivations for increasing a data-driven engagement with the public have arisen over the past several years. In doing so, we identify key characteristics that both enable and hinder the existing smart city in the forms of access to open data, the use of increased computational methods, and the engagement of public services through digital space as requirements for the future of participatory governance. We argue that instituting appropriate policies and engaging citizens to co-design and participate in the planning processes are essential to ensuring an inclusive, modern, and open smart city.     

民俗资料学的建立与意义

民俗学是一门特别重视资料的学科,但民俗学者在传统上往往将民俗资料与文本类的民俗志和民俗文献互为等同。广义的民俗资料概念与此不同,意指一切能够为民俗学研究提供有效信息的文字与非文字资料,包括文本、图像、文物和影音资料等。民俗资料学借鉴史料学、文献学、文物与博物馆学、图书馆学、信息科学等的理论与方法,从民俗学的学科需求出发,着重研究民俗资料的搜集整理、分类属性、保存利用等问题。开拓这一学术研究方向的目的,在于坐实民俗学"资料之学"的名义,对内促进民俗学的学科发展,对外与历史学、文献学、人类学、计算机科学等形成对话,并为政府、社会团体与个人提供民俗资料方面的咨询服务。     

An Australian geography of unhealthy housing

Housing problems, such as affordability, poor quality of condition, or damp, are key determinants of health and wellbeing. Importantly, though, a growing body of research has shown that unhealthy housing is the combined result of multiple housing problems acting together. Although the spatial distribution of discrete housing problems is well established, little is known of Australia's geography of unhealthy housing. We have previously defined and validated an Australian Index of Housing Insults, which captures the multiple ways in which housing adversely influences individual health—including, but not limited to, people's tenure security, affordability, quality, and neighbourhood characteristics. Using the Household Income and Labour Dynamics in Australia (HILDA) dataset, a nationally representative longitudinal survey of Australian households, this paper describes Australia's geography of unhealthy housing. The analysis examines the prevalence, characteristics, and distribution of the population who are vulnerable to unhealthy housing. Our findings reveal both a worsening landscape of households at risk because of their accommodation and a changing pattern of unhealthy housing in Australia over time. The paper considers how these findings may impact future policy settings and the potential to improve the health of Australia's population through targeted housing interventions.