Water management has become a global priority in recent decades. The demand for water resources is increasing in cities due to the increase in population and the intensive use of water in economic activities and ornamentation. The problem is exacerbated when cities are built on desert regions, this is the case of Lima which is the second largest city built on a desert after Cairo. In this type of cities, it is necessary to minimize water consumption in activities that do not cover the priority needs of the population. For this reason, one of the most important tasks in the management of water resources in Lima is the optimization of water use in irrigation of parks, malls and other public green areas, necessary to offer a good quality of life to citizens. This research develops a smart decision support system to optimize irrigation in city parks. The proposed methodology takes 4 variables: land area, temperature, park humidity and weather forecast. First, strategic segmentation of the total area of the park is carried out, followed by the use of low-cost sensors to construct real-time humidity and temperature maps of the land area. Afterwards, a fuzzy inference system (FIS) that incorporates the knowledge of agronomists to process vague information in terms of computer interpretable language, together with the data collected from the variables and humidity and temperature maps is built, to assess the need for irrigation of each segment of the park. A dashboard is made to facilitate the visualization of results, including humidity and temperature maps, the weather forecast for the area and the recommendation of the FIS, which supports decision-making on irrigation needs in each segment of the park. The methodology was applied in a case study that corresponds to a San Isidro park in the city of Lima. Significant expected savings were obtained in terms of water resources and monetary units, which demonstrates the viability of the application of this smart system oriented at supporting decision-making on smart irrigation in the city’s parks.

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PurposeThe purpose of this paper is to present a spatial decision support system (SDSS) to be used by the local authorities of a city in the planning and response phase of a disaster. The SDSS focuses on the management of public spaces as a resource to increase a vulnerable population’s accessibility to essential goods and services. Using a web-based platform, the SDSS would support data-driven decisions, especially for cases such as the COVID-19 pandemic which requires special care in quarantine situations (which imply walking access instead of by other means of transport).Design/methodology/approachThis paper proposes a methodology to create a web-SDSS to manage public spaces in the planning and response phase of a disaster to increase the access to essential goods and services. Using a regular polygon grid, a city is partitioned into spatial units that aggregate spatial data from open and proprietary sources. The polygon grid is then used to compute accessibility, vulnerability and population density indicators using spatial analysis. Finally, a facility location problem is formulated and solved to provide decision-makers with an adaptive selection of public spaces given their indicators of choice.FindingsThe design and implementation of the methodology resulted in a granular representation of the city of Lima, Peru, in terms of population density, accessibility and vulnerability. Using these indicators, the SDSS was deployed as a web application that allowed decision-makers to explore different solutions to a facility location model within their districts, as well as visualizing the indicators computed for the hexagons that covered the district’s area. By performing tests with different local authorities, improvements were suggested to support a more general set of decisions and the key indicators to use in the SDSS were determined.Originality/valueThis paper, following the literature gap, is the first of its kind that presents an SDSS focused on increasing access to essential goods and services using public spaces and has had a successful response from local authorities with different backgrounds regarding the integration into their decision-making process.

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Travel literature has captured humanity’s imagination ever since the emergence of famous works such as The Wonders of The World by Marco Polo and The Journal of Christopher Columbus. Authors in this genre must process large and diverse volumes of data (visual, sensory, and written) obtained on their trips, before synthesizing it humanly in such a way as to move and communicate personally with the reader, without losing the factual nature of the story. This is the ultimate goal of the natural language processing (NLP) field: to process and generate human–machine interaction as naturally as possible. Hence, this article’s purpose is to analyze and describe a nonfictional literary text, which is a type of documentary text that contains objective, qualitative, and quantitative information based on evidence. In this analysis, traditional methods will not be used. Instead, it will leverage NLP techniques to process and extract relevant information from the text. This literary analysis is a new kind of approach that encourages further discussions about the methodologies currently used. The proposed methodology enables exploratory analysis of both individual and unstructured corpus databases while also allowing geospatial data to complement the textual analysis by connecting the people in the text with real places.