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Data science lacks a distinctive identity and a theory-informed approach, both for its own sake and to properly be applied conjointly to the social sciences. This paper’s purposes are twofold: to provide (1) data science an illustration of theory adoption, able to address explanation and support prediction/prescription capacities and (2) a rationale for identification of the key phenomena and properties of data science so that the data speak through a contextual understanding of reality, broader than has been usual.

A literature review and a derived conceptual research model for a push–pull approach (adapted for a data science study in the management field) are presented. A real location–allocation problem is solved through a specific algorithm and explained in the light of the adapted push–pull theory, serving as an instance for a data science theory-informed application in the management field.

This study advances knowledge on the definition of data science key phenomena as not just pure “data”, but interrelated data and datasets properties, as well as on the specific adaptation of the push-pull theory through its definition, dimensionality and interaction model, also illustrating how to apply the theory in a data science theory-informed research. The proposed model contributes to the theoretical strengthening of data science, still an incipient area, and the solution of the location-allocation problem suggests the applicability of the proposed approach to broad data science problems, alleviating the criticism on the lack of explanation and the focus on pattern recognition in data science practice and research.

The proposed algorithm requires the previous definition of a perimeter of interest. This aspect should be characterised as an antecedent to the model, which is a strong assumption. As for prescription, in this specific case, one has to take complementary actions, since theory, model and algorithm are not detached from in loco visits, market research or interviews with potential stakeholders.

This study offers a conceptual model for practical location–allocation problem analyses, based on the push–pull theoretical components. So, it suggests a proper definition for each component (the object, the perspective, the forces, its degrees and the nature of the movement). The proposed model has also an algorithm for computational implementation, which visually describes and explains components interaction, allowing further simulation (estimated forces degrees) for prediction.

First, this study identifies an overlap of push–pull theoretical approaches, which suggests theory adoption eventually as mere common sense, weakening further theoretical development. Second, this study elaborates a definition for the push–pull theory, a dimensionality and a relationship between its components. Third, a typical location–allocation problem is analysed in the light of the refactored theory, showing its adequacy for that class of problems. And fourth, this study suggests that the essence of a data science should be the study of contextual relationships among data, and that the context should be provided by the spatial, temporal, political, economic and social analytical interests.

A growing body of evidence points to the influence of location and allocation decisions on the structure of healthcare networks. The authors introduced a three-level hierarchical facility location model to minimize travel time in the healthcare system under uncertainty.

Most healthcare networks are hierarchical and, as a result, the linkage between their levels makes it difficult to specify the location of the facilities. In this article, the authors present a hybrid approach according to data envelopment analysis and robust programming to design a healthcare network. In the first phase, the efficiency of each potential location is calculated based on the non-radial range-adjusted measure considering desirable and undesirable outputs based on a number of criteria such as the target area's population, proximity to earthquake faults, quality of urban life, urban decrepitude, etc. The locations deemed suitable are then used as candidate locations in the mathematical model. In the second phase, based on the proposed robust optimization model, called light robustness, the location and allocation decisions are adopted.

The developed model is evaluated using an actual-world case study in District 1 of Tehran, Iran and relevant results and different sensitivity analyses were presented as well. When the percentage of referral parameters changes, the value of the robust model's objective function increases.

The contributions of this article are listed as follows: Considering desirable and undesirable criteria to selecting candidate locations, providing a robust programming model for building a service network and applying the developed model to an actual-world case study.

This paper aims to study a dynamic post-disaster emergency planning (PDEP) problem in an integrated network through the investigation of the selection of shelters, medical centers and distribution centers (DCs), and the allocation of evacuees and injured people. The resource and people assignment in multiple periods are considered.

A mathematical formulation is provided for the PDEP problem. The authors decompose the model into two sub-models as follows: the primary model is an integer programming model and the subproblem is a nonlinear programming model with continuous variables. The simulated annealing is used to solve the primary problem, and particle swarm optimization (PSO) mixed with beetle antennae search (BAS) is used to solve the subproblem.

The paper finds that BAS can increase the stability of PSO and keep the advantages of PSO’s rapid convergence. By implementing these algorithms on emergency planning after the Wenchuan earthquake that happened in China in 2008, this paper finds that the priority of different levels of injured people is influenced by several factors. Even within the same disaster, the priority of different levels of injured can be inconsistent because of the differences in resource levels.

The authors integrate the shelters, medical centers and DCs as a system, and simultaneously, consider evacuees and injured people and different resource assignments. The authors divide the injured people into three levels and use survival rate function to simulate the survival conditions of different people. The authors provide an improved PSO algorithm to solve the problem.

The purpose of this paper is to study the optimization of the geographical location of a network of points of sale, so that each retailer can have access to a potential geographic market. In addition, the authors study the importance of the distance variable in the commercial viability of a point of sale and a network of points of sale, analysing if the best location for each point (local optimum) is always the best location for the whole (global optimum).

Location-allocation models are applied using p-median algorithms and spatial competition maximization to analyse the actual journeys of 64,740 car buyers in 1240 postal codes using a geographic information system (GIS) and geomarketing techniques.

The models show that the pursuit of individual objectives by each concessionaire over the collective provides poorer results for the whole network of points of sale when compared to coordinated competition. The solutions provided by the models considering geographic and marketing criteria permit a reduction in the length of journeys made by the buyers. GIS allows the optimal control of market demand coverage through the collaborative strategies of the supplying retailers, in this case, car dealerships.

The paper contributes to the joint research of geography and marketing from a theoretical and practical point of view. The main contribution is the use of information on actual buyer journeys for the optimal location of a network of points of sale. This research also contributes to the analysis of the correlation between the optimum local and optimum global locations of a commercial network and is a pioneering work in the application of these models to the automotive sector in the territorial area of the study.

A systems perspective of waste management allows an integrated approach not only to the five basic functional elements of waste management itself (generation, reduction, collection, recycling, disposal), but to the problems arising at the interfaces with the management of energy, nature conservation, environmental protection, economic factors like unemployment and productivity, etc. This monograph separately describes present practices and the problems to be solved in each of the functional areas of waste management and at the important interfaces. Strategies for more efficient control are then proposed from a systems perspective. Systematic and objective means of solving problems become possible leading to optimal management and a positive contribution to economic development, not least through resource conservation. India is the particular context within which waste generation and management are discussed. In considering waste disposal techniques, special attention is given to sewage and radioactive wastes.

The aim of this study was to determine the number of shelters, specify some optimal paths among building blocks towards shelters, and assign population to shelters.

Imperialist competition algorithm (ICA) and particle swarm optimization (PSO) were used to optimize the objectives of this study.

The optimal value for PSO objective function was with the number of function evaluations (NFE) of 5300 and the optimal value of ICA objective function was with NFE of 1062. Repetition test for both algorithms showed that imperialist competition algorithm enjoys better stability and constancy and higher speed of convergence compared to particle swarm algorithm. This has been also shown in larger environments. 92% of the existing populations have access to shelters at a distance of less than600 meters. This means that evacuation from the building blocks to shelters takes less than 8 minutes. The average distance from a block (for example, a residential complex) to an optimal shelter is approximately273meters. The greatest risk of route and shelter has been 239 and 121, respectively.

To address these goals, four following objective functions were considered: a) minimization of the distance for getting all the people to shelters b) the lowest total risk of the discharge path c) minimization of the total time required to transfer people to shelters or hospitals if necessary, and d) the lowest total risk in shelters.

Over the recent decades, the frequency of so-called ‘natural’ disasters has increased significantly worldwide and resulted in escalating human and economic losses. Among them, the earthquake is one of the major concerns of the various stakeholders related to urban planning.

In addition, the maximum time of discharge from the helter to the hospital has been 17 minutes, which means the presence of good access to selected shelters.

Increasingly complex societal challenges call for new, innovative solutions that social hybrid business models can provide. Social supermarkets (SSMs) are one example offering access to affordable food to people living in poverty while reducing food waste of nearby retailers. Finding the “right” location is an essential part of this retail marketing strategy. However, limited research has attempted to investigate the specific conditions of locational planning for hybrid and nonprofit retail organizations. This paper illustrates the case of Austria where SSMs are well established.

A GIS-based white space analysis was carried out to identify potential neighborhoods or rural areas for new social supermarkets with sufficient nearby demand, supply and no existing SSMs. The empirical parameters for this spatial analysis can be transferred to European countries with similar ecosystems. The authors collected a unique data set of 79 (2014) and 88 SSMs (2019) and 4,665 (2014) and 4,211 (2019) food retailers as (potential) suppliers to SSMs. To determine demand, the authors relied on small-scale integrated wage and income tax data and unemployment rates (2011) from Statistics Austria.

Overall, Austria has very good spatial access to grocery stores, including to SSMs. SSM access increased especially in the capital of Vienna between 2014 and 2019. The GIS-based white space analysis identified several other regions where residents have a high demand for affordable food with sufficient potential suppliers of surplus food but no SSM yet. Neighborhood-level findings are released as part of a publicly accessible spatial decision support system.

The methodology allowed a specific definition of the key areas of relevance by matching the demand for SSMs, calculated as the number of people with low incomes in the respective regions in Austria, with the supply of SSMs, calculated as the amount of potential food loss prevention by nearby retail stores. These parameters have proven to help in identifying the white spaces and therefore can be used in Austria and other European countries with similar ecosystems.

Resilience concepts in integrated urban transport refer to the performance of dealing with external shock and the ability to continue to provide transportation services of all modes. A robust transportation resilience is a goal in pursuing transportation sustainability. Under this specified context, while before the perturbations, robustness refers to the degree of the system’s capability of functioning according to its design specifications on integrated modes and routes, redundancy is the degree of duplication of traffic routes and alternative modes to maintain persistency of service in case of perturbations. While after the perturbations, resourcefulness refers to the capacity to identify operational problems in the system, prioritize interventions and mobilize necessary material/ human resources to recover all the routes and modes, rapidity is the speed of complete recovery of all modes and traffic routes in the urban area. These “4R” are the most critical components of urban integrated resilience.

The trends of transportation resilience's connotation, metrics and strategies are summarized from the literature. A framework is introduced on both qualitative characteristics and quantitative metrics of transportation resilience. Using both model-based and mode-free methodologies that measure resilience in attributes, topology and system performance provides a benchmark for evaluating the mechanism of resilience changes during the perturbation. Correspondingly, different pre-perturbation and post-perturbation strategies for enhancing resilience under multi-mode scenarios are reviewed and summarized.

Cyber-physic transportation system (CPS) is a more targeted solution to resilience issues in transportation. A well-designed CPS can be applied to improve transport resilience facing different perturbations. The CPS ensures the independence and integrity of every child element within each functional zone while reacting rapidly.

This paper provides a more comprehensive understanding of transportation resilience in terms of integrated urban transport. The fundamental characteristics and strategies for resilience are summarized and elaborated. As little research has shed light on the resilience concepts in integrated urban transport, the findings from this paper point out the development trend of a resilient transportation system for digital and data-driven management.

The rapid development of urban areas in Sleman District, Indonesia, has created new challenges for firefighting response services. One of the primary challenges is to identify the optimal locations for new fire stations, to improve service quality and maximize service coverage within the specified time.

This paper proposes a method for precisely calculating travel time that integrates delay time caused by traffic lights, intersections and congestion. The study highlights the importance of precise calculation of travel time in order to provide a more accurate understanding of the service area covered by the fire stations. The proposed method utilizes network analysis in ArcGIS, the analytical hierarchy process (AHP) and simple additive weighting (SAW) to accurately calculate travel time and to identify the best locations for new fire stations. The identification of new site was based on service safety, service quality, service costs and demographic factors and applied to the Sleman district in Indonesia.

The results showed that the total area covered by old and new fire stations decreased from 61% to 31.8% of the study area when the adjusted default speed scenario was implemented.

The results indicated that the default speed scenario could provide misleading information about the service area, while the adjusted default speed scenario improved service quality and maximized service coverage.

The proposed method provides decision-makers with an effective tool to make informed decisions on optimal locations for new fire stations and thus enhance emergency response and public safety.