Water 2014, 6(6), 1601-1641; https://doi.org/10.3390/w6061601
Water Relationships in the U.S. Southwest: Characterizing Water Management Networks Using Natural Language Processing
1
Computation Institute, University of Chicago, 5375 S. Ellis Avenue, Chicago, IL 60637, USA
2
Institute of Archaeology, University College London, 31-34 Gordon Square, London WC1H 0PY, UK
3
Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, 8 College Road, Durham, NH 03824-3525, USA
4
Center for Resilient Rural Communities, University of Idaho, 875 Perimeter Drive, Moscow, ID 83844, USA
5
Resilience and Adaptive Management Group, University Alaska Anchorage, 3211 Providence Drive, Anchorage, AK 99508, USA
*
Author to whom correspondence should be addressed.
Received: 17 January 2014 / Revised: 14 May 2014 / Accepted: 15 May 2014 / Published: 3 June 2014
Abstract
Natural language processing (NLP) and named entity recognition (NER) techniques are applied to collections of newspaper articles from four cities in the U.S. Southwest. The results are used to generate a network of water management institutions that reflect public perceptions of water management and the structure of water management in these areas. This structure can be highly centralized or fragmented; in the latter case, multiple peer institutions exist that may cooperate or be in conflict. This is reflected in the public discourse of the water consumers in these areas and can, we contend, impact the potential responses of management agencies to challenges of water supply and quality and, in some cases, limit their effectiveness. Flagstaff, AZ, Tucson, AZ, Las Vegas, NV, and the Grand Valley, CO, are examined, including more than 110,000 articles from 2004–2012. Documents are scored by association with water topics, and phrases likely to be institutions are extracted via custom NLP and NER algorithms; those institutions associated with water-related documents are used to form networks via document co-location. The Grand Valley is shown to have a markedly different structure, which we contend reflects the different historical trajectory of its development and its current state, which includes multiple institutions of roughly equal scope and size. These results demonstrate the utility of using NLP and NER methods to understanding the structure and variation of water management systems. View Full-TextKeywords:
water management; institutions; local vs. regional scale; local water suppliers; natural language processing; data mining; named entity recognition; network analysis
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Murphy, J.T.; Ozik, J.; Collier, N.T.; Altaweel, M.; Lammers, R.B.; Kliskey, A.; Alessa, L.; Cason, D.; Williams, P. Water Relationships in the U.S. Southwest: Characterizing Water Management Networks Using Natural Language Processing. Water 2014, 6, 1601-1641.