by Martina Tenzer
International charters and agendas for heritage management
The European Landscape Convention (Council of Europe 2000) states that the landscape is: “… an area as perceived by people…”, but how strong is the voice of the people in landscape planning? How easily can the lived experience of local people be integrated into a framework of decision-making by local authorities?
The Faro Convention (Council of Europe 2005) and the Burra Charter (ICOMOS 2013) emphasise the importance of cultural heritage for society and encourage the participation of the public in the heritage decision-making process. However, practical solutions for the inclusive management of heritage and cultural landscapes have still to be developed.
Historic Landscape Characterisation and other approaches
Landscapes are never passive, a point reflected in the creation of Historic Landscape Characterisation (HLC) (Fig. 1). HLC was created as a general, value-free tool for landscape management. However, it did not intend to create an abstract interpretation of a landscape excluding people but instead to include people’s perceptions in the characterisation process, adopt a common language, and create a tool for community engagement (Fairclough and Barnatt 1999; Turner 2018). Nevertheless, the historic environment often appears largely devoid of people, as reflected in the aerial image of the historic landscape in Figure 2.
Figure 1: Example of the HLC of the Peak District National Park
Figure 2: Aerial view of the landscape in the Peak District National Park. Image forms part of the archive of Historic England’s aerial investigation collection.
Creative approaches to understanding people’s connection to place can involve various modes of artistic expression (e.g., visual arts or literary representations), which, due to the data format, are difficult to integrate into the planning and decision-making process (Crouch and Matless 1996; Nardi 2014). The challenge is to translate such qualitative data that seems “soft”, subjective and particular into a format that can be understood and articulated in a way that aligns with the planning process, i.e., that can be used in synergy with the “hard”, objective facts and quantitative data provided by landscape history and archaeology. An additional layer of local perceptions and lived experiences of the landscapes would provide local expertise that an outsider could not gain by assessing social values (Schofield 2014).
AI as an aid to analyse qualitative data for landscape management
While algorithms can analyse quantitative data in a resource- and time-efficient way, the solutions to qualitative data analysis are now being adapted for heritage management and planning. Artificial Intelligence (AI) can speed up the processes involved in qualitative data collection, analysis and visualisation. The “spatial turn” of the 1990s introduced GIS techniques to archaeology and heritage management but, more importantly, led to a new way of thinking of “landscape, perceived as spatial and temporal palimpsest of memories and meaning” (Heriland n.a.).
The “digital turn” at the beginning of the 21st century allowed the storage and analysis of “Big Data”. The revolution of AI has yet to arrive fully in the field of heritage management. Creative approaches have the potential to advance the methods and tools that will help make qualitative data analysis more efficient and effective. Automatic transcription of interviews, social media analysis, Natural Language Processing (NLP), and Machine Learning (ML) are just some methods that will help to understand and process qualitative data in a similar way as quantitative data.
Case study: Tweets in the Peak
Creativity in data analysis and visualisation is one objective of my approach to bringing AI and people’s perceptions closer together. With my research, I want to present people’s perceptions in a form that can, on the one hand, interpret locally held meanings and values and, on the other hand, conduct automated analysis of these data using NLP and ML for the creation of time-efficient methods. The example of an emoji cloud (Fig. 3) shows the variation of people’s emotional connection to places in the Peak District National Park expressed in tweets before, during and after the Covid-19 pandemic (Tenzer 2022).
Figure 3: Emoji cloud showing emojis used in tweets that mention places in the Peak District National Park. The emotions show good vibes in 2019, angry and swearing faces during severe lockdown restrictions in 2020, and a relaxation after restrictions were eased in 2021.
Furthermore, a method for fine-grained, area-specific location detection based on Named Entity Recognition can extract locations from tweets and automatically place them on a map. Figure 4 shows the places mentioned in connection to the emotions expressed in the emoji map. This approach to public perception interpretation is founded on two basic principles. Firstly, social media analysis offers a wealth of information, and AI offers a tool to analyse large amounts of data in a time-efficient way. Secondly, heritage conversation should start by finding a common language – visuals that do not need any explanation or description but instantly convey their content. This novel data analysis and visualisation approach can provide a vital basis for discussion and spark interest in the landscape as a social space.
IFigure 4: Locations mentioned in tweets on three Bank Holiday weekends from 2019-2021, showing places mentioned in the Peak District National Park.
Limitations of AI
AI is by no means a magic box that presents us with perfect results. Analysing language is an inherently different field for data scientists and linguists. For example, in my social media research, the algorithm did not pick up sarcasm, and this was particularly apparent in the Covid year of 2020. Even for humans, understanding sarcasm can be very difficult, especially in electronic communication. This was the reason for introducing emojis in the first place – to understand the undertone of a message without reading the sender’s facial expression (
word of the year 2015 in the Oxford Languages) or body language. Also, word ambiguities, especially with location recognition, proved challenging. AI struggles, for example, with the word “hope”. This word was used to express “hope that the pandemic is over soon”, but it can also represent the village Hope in the Peak District. Despite the limitations, AI has huge advantages and is a constantly evolving field. It can deal with large amounts of data, learn and improve through training, and yield sometimes surprising results.
People’s perceptions as a management tool
There is room for people’s perceptions in heritage management if researchers and practitioners are willing to embrace the new technologies and are given the resources to do so. In reality it shouldn’t take much, and applications will get easier as the technologies improve. The need to manage landscapes is a worldwide phenomenon, as is the urge to give people a voice in how their neighbourhoods and environments are managed. New tools and approaches, which can be used in landscapes crossing borders and countrywide, will enable automated processes to help create practical, applicable solutions for inclusive and transparent heritage and landscape management that facilitate sustainable change and acknowledge and appreciate that landscapes are living, dynamic, socially constructed and natural spaces “as perceived by people”.
References
Australia ICOMOS and International Council on Monuments and Sites. (2013). The Burra Charter: the Australia ICOMOS charter for places of cultural significance 2013. [Online]. Available at: http://australia.icomos.org/wp-content/uploads/The-Burra-Charter-2013-Adopted-31.10.2013.pdf [Accessed 16 October 2020].
Common Ground. (1996). Parish Maps. [Online]. Available at: https://www.commonground.org.uk/parish-maps/ [Accessed 25 September 2020].
Council of Europe. (2000). European Landscape Convention (Florence Convention). Strasbourg: Council of Europe. [Online]. Available at: https://www.coe.int/en/web/conventions/full-list [Accessed 10 December 2020].
Council of Europe. (2005). Framework Convention on the Value of Cultural Heritage for Society. [Online]. Available at: https://www.coe.int/en/web/conventions/full-list/-/conventions/treaty/199 [Accessed 4 February 2021].
Fairclough, G. J. et al. (1999). Yesterday’s world, tomorrow’s landscape: the English Heritage Historic Landscape Project 1992-94. London: English Heritage.
Heriland. Heriland. College of heritage planning. Amsterdam: Vrije Universiteit.
Nardi, S. D. (2014). Senses of Place, Senses of the Past: Making Experiential Maps as Part of Community Heritage Fieldwork. Journal of Community Archaeology & Heritage, 1 (1), Routledge., pp.5–22. [Online]. Available at: doi:10.1179/2051819613Z.0000000001.
Schofield, J. (2014). Who needs experts? Counter-mapping cultural heritage. Abingdon: Routledge.
Tenzer, M. (2022). Tweets in the Peak: Twitter Analysis – the impact of Covid-19 on cultural landscapes. Internet Archaeology, (59), Internet Archaeology. [Online]. Available at: doi:10.11141/ia.59.6 [Accessed 19 August 2022].
Turner, S. (2018). Historic Landscape Characterisation. An archaeological approach to landscape heritage. [Online]. Available at: doi:10.4324/9781315753423-3 [Accessed 31 January 2021].
Figures
Figure 1: HLC map created by the author, based on data provided by https://archaeologydataservice.ac.uk/archives/view/peaks_hlc_2016/downloads.cfm?archive=GIS. Basemap Map tiles by Stamen Design, under CC BY 3.0. Data by OpenStreetMap, under ODbL)
Figure 2: https://historicengland.org.uk/images-books/archive/collections/aerial-photos/
Figure 3: Tenzer, M. (2022). Tweets in the Peak: Twitter Analysis – the impact of Covid-19 on cultural landscapes. Internet Archaeology, (59), Internet Archaeology. [Online]. Available at: doi:10.11141/ia.59.6. (Fig. 5).
Figure 4: Tenzer, M. (2022). Tweets in the Peak: Twitter Analysis – the impact of Covid-19 on cultural landscapes. Internet Archaeology, (59), Internet Archaeology. [Online]. Available at: doi:10.11141/ia.59.6. (Fig. 13). Map created by the author, basemap Map tiles by Stamen Design, under CC BY 3.0. Data by OpenStreetMap, under ODbL).
About the author
Martina Tenzer is a PhD scholar at the University of York, United Kingdom. This blog post is based on her PhD research and inspired by her participation in the Heriland Blended Intensive Programme “Cultural Heritage and the Planning of European Landscapes”.
Contact Martina: martina.tenzer@york.ac.uk
About the Heriland Blended Intensive Programme
Are you interested in participating in the next iteration of the Heriland Blended Intensive Programme “Cultural Heritage and the Planning of European Landscapes”?
Contact Niels van Manen: n.van.manen@vu.nl.