Data-driven Decision Support Systems Research Articles

Data-driven decision-support system for expansions in retail stores: Dexter

ABSTRACT The expansion of retail stores is an important decision in the retail industry, involving significant investments and long-term consequences for customer service, satisfaction, and revenue growth. The decision-making process for store expansion is complex, and requires detailed information and a multilevel view of store operations over time. Yet, the literature shows a scarcity of data-driven decision-support systems for retail store expansions. To address this gap, we propose a data-driven decision support system called Dexter, which provides store expansion recommendations using a novel ensemble algorithm. Dexter was developed in collaboration with an industry partner, who provided access to various critical datasets. The results of the evaluation with domain experts show that Dexter has the potential to assist store owners in making informed decisions for retail store expansions. This work paves the way for further research and acts as an entry point to attract more works in this under-researched area.

Business intelligence and cognitive loads: Proposition of a dashboard adoption model

Decision makers in organizations strive to improve the quality of their decisions. One way to improve that process is to objectify the decisions with facts. Data-driven Decision Support Systems (data-driven DSS), and more specifically business intelligence (BI) intend to achieve this. Organizations invest massively in the development of BI data-driven DSS and expect them to be adopted and to effectively support decision makers. This raises many technical and methodological challenges, especially regarding the design of BI dashboards, which can be seen as the visible tip of the BI data-driven DSS iceberg and which play a major role in the adoption of the entire system. In this paper, the dashboard content is investigated as one possible root cause for BI data-driven DSS dashboard adoption or rejection through an early empirical research. More precisely, this work is composed of three parts. In the first part, the concept of cognitive loads is studied in the context of BI dashboards and the informational, the representational and the non-informational loads are introduced. In the second part, the effects of these loads on the adoption of BI dashboards are then studied through an experiment with 167 respondents and a Structural Equation Modeling (SEM) analysis. The result is a Dashboard Adoption Model, enriching the seminal Technology Acceptance Model with new content-oriented variables to support the design of more supportive BI data-driven DSS dashboards. Finally, in the third part, a set of indicators is proposed to help dashboards designers in the monitoring of the loads of their dashboards practically.

OntoMedRec: Logically-pretrained model-agnostic ontology encoders for medication recommendation

Recommending medications with electronic health records (EHRs) is a challenging task for data-driven clinical decision support systems. Most existing models learnt representations for medical concepts based on EHRs and make recommendations with the learnt representations. However, most medications appear in EHR datasets for limited times (the frequency distribution of medications follows power law distribution), resulting in insufficient learning of their representations of the medications. Medical ontologies are the hierarchical classification systems for medical terms where similar terms will be in the same class on a certain level. In this paper, we propose OntoMedRec, the logically-pretrained and model-agnostic medical Ontology Encoders for Medication Recommendation that addresses data sparsity problem with medical ontologies. We conduct comprehensive experiments on real-world EHR datasets to evaluate the effectiveness of OntoMedRec by integrating it into various existing downstream medication recommendation models. The result shows the integration of OntoMedRec improves the performance of various models in both the entire EHR datasets and the admissions with few-shot medications. We provide the GitHub repository for the source code. (https://github.com/WaicongTam/OntoMedRec)

Data-Driven Decision Support Systems for Drug Discovery and Development: A Case Study in Pharmaceutical Management

This paper covers the use and influence of data-driven decision support systems (DSS) on drug management, particularly in the areas of drug exploration and production. The study, which employed a mixed-methods approach, involving literature review, qualitative interviews, quantitative assessments, and case study analysis, reveals the AI, machine learning and big data analytics ability to drive drug discovery and development processes revolution in the pharmaceutical industry. The study shows that handling of DSS allows sound decision making, consequently resulting in improved efficiency, decreased expenses and better innovation throughout the drug development process. Areas like data integration, algorithm robustness, and regulation are identified as the major issues, which offers an insight into the importance of these areas of concern in the effective application of a data-driven approach in pharmaceutical management. Such contrast with the like works underlines the role of our findings as an essential link in the AI-driven healthcare innovations chain. The research provides a foundation for further improving the state of knowledge and understanding of DSS in drug management, guiding future research aiming at expanding conceptual frameworks and designing practical implementations.

Digital Participatory Model as Part of a Data-Driven Decision Support System for Urban Vibrancy

Digital participation relies on computational systems as the instruments for expert engagement, data-driven insight, and informed decision-making. This study aims to increase expert engagement with the Bayesian-based decision support model in evaluating urban vibrancy decisions. In this study, urban vibrancy parameters are defined using “economic, use, and image value” measures. This article focuses on the visual aspect of vibrancy, defined as the image value of place. The image value is evaluated through likability and likability features. The case study area is the Eminönü Central Business District in the Istanbul Historic Peninsula due to its distinctive urban dynamics derived from the duality of being a cultural and cosmopolitan city center. This research presents a method as a decision support system (DSS) model based on the Bayesian belief network (BBN) and spatial BBN for supporting urban vibrancy decisions. The spatial BBNs monitor spatial outcomes of variables’ dependencies that form through the BBN relationship network. Spatial BBN tools monitors the spatial impact of decisions for informed urban interventions. The results demonstrate that urban greening, pedestrianization, and human-scaled streetscapes should be prioritized to make streets more likable. The most significant intervention areas are Tahtakale for signboard regulation, Sultanahmet and Vefa for cultural landscape improvement, and Vefa and Mahmutpaşa for planning building enclosures. The participation is achieved by evaluating urban vibrancy with what-if scenarios using BBN. The developed DSS model addresses which parameters should be prioritized, and what are their spatial consequences. The use of spatial BBN tools presents certain limitations in terms of interoperability and user interaction. Overall, this research contributes to participatory urban planning by incorporating both conditional and spatial dependencies. This unique approach not only promotes a more holistic understanding of urban vibrancy but also contributes to the advancement of digital participation in urban planning decisions.

Data-Driven Decision Support Systems for Business Process Improvement

The accessibility of data is altering how businesses make decisions at different levels. Scholars and professionals are investigating the ways in which Business Process suppliers can profit from the availability and application of data, particularly in relation to decision-making concerning service provision. Business Process Improvement is one of the applications that is anticipated to gain the most from the accessibility of information. Suppliers of services can avoid failures by making prompt and well-informed decisions based on the evaluation of the resource's health state. Despite this, providing data-driven BPI services is not simple, and providers must set up their systems to correctly gather, process, and utilize past and current data. This study introduces a data-driven business intelligence framework to provide use full insights for improving business process activities. This framework offers a set of visualization tools that help interpret the relation between different factors that can improve the management of different business processes. Moreover, our framework provides successful integration of random forests to allow predictive modeling of sales, profits, and discounts across different regions.

Data-driven simulation-based decision support system for resource allocation in industry 4.0 and smart manufacturing

Data-driven simulation (DDS) is fundamental to analytical and decision-support technologies in Industry 4.0 and smart manufacturing. This study investigates the potential of DDS for resource allocation (RA) in high-mix, low-volume smart manufacturing systems with mixed automation levels. A DDS-based decision support system (DDS-DSS) is developed by incorporating two RA strategies: simulation-based bottleneck analysis (SB-BA) and simulation-based multi-objective optimization (SB-MOO). To enhance the performance of SB-MOO, a unique meta-learning mechanism featuring memory, dynamic orthogonal array, and learning rate is integrated into the NSGA-II, resulting in a modified version of the NSGA-II with meta-learning (i.e., NSGA-II-ML). The proposed DSS also benefits from a post-optimality analysis that leverages a clustering algorithm to derive actionable insights. A real-life marine engine manufacturing application study is presented to demonstrate the applicability and exhibit efficacy of the proposed DSS and NSGA-II-ML. To this aim, NSGA-II-ML was tested against the original NSGA-II and differential evolution (DE) algorithm across a set of test problems. The results revealed that NSGA-II-ML surpassed the other two in terms of the number of non-dominated solutions and hypervolume, particularly in medium and large-sized problems. Furthermore, NSGA-II-ML achieved a 24% improvement in the best throughput found in the real case problem, outperforming SB-BA, NSGA-II, and DE. The post-optimality analysis led to the extraction of valuable knowledge about the key, influencing decision variables on the throughput.

Towards sustainable UAV operations: Balancing economic optimization with environmental and social considerations in path planning

Unmanned Aerial Vehicles (UAVs), i.e., drones, are expected to be widely used in various applications, such as parcel delivery and passenger transport, with the benefits of mitigating traffic congestion and reducing carbon emissions. In this paper, we study a UAV path planning problem under uncertain weather conditions, and design a data-driven dynamic decision support system for multiple types of UAVs. To this end, we categorize all relevant costs into three types, namely, economic, environmental, and social costs, and formulate a nonlinear two-stage stochastic programming model to establish optimal paths for UAV missions under weather uncertainty. We then discretize the nonlinear model and propose a tight linear approximation for the discretized problem to allow for a near real-time implementation. To quantify weather uncertainty, we propose a weather scenario generation algorithm to map ensemble-based weather forecast information to airspace blockage maps. With comprehensive computational studies through simulations, we show that our proposed stochastic approach can lower operating costs by an average of around 6%, where the savings increase as weather conditions become more severe and complex. We also find that, for missions operated by small UAVs, it is not sufficient to determine a path solely based on economic cost minimization, but it should rather be through total cost minimization, which involves environmental and social costs. Considering only the economic cost in the optimization may lead to much higher non-economic costs. However, for missions operated by large UAVs, it is sufficient to determine paths through economic cost optimization, as including environmental and social costs in the optimization process does not result in solutions that are much different from those obtained by considering only the economic costs. For both small and large UAVs, a path established solely through environmental or social cost minimization may not be economically sustainable, as doing so would imply very high economic costs.

Analytic Approach in Accessing Trends and Impacts of Medicaid-Medicare Dual Enrollment in the United States

The landscape of Medicaid and Medicare enrollment in the United States is undergoing dynamic changes, driven by intricate policies, shifting demographic trends, and evolving healthcare access criteria. This publication serves as a beacon, illuminating the multifaceted terrain of Medicaid and Medicare dual enrollment and offering a comprehensive understanding of its complexities and challenges. The primary objective is to advocate for the adoption of a centralized data-driven decision support system, recognizing its transformative potential. By harnessing the power of data, we can revolutionize enrollment management, streamline administrative processes, and facilitate the timely adjustment of policies, ensuring more efficient and effective healthcare access. Empowerment is at the heart of our mission. We aim to equip all healthcare stakeholders, from government agencies and insurance providers to healthcare institutions and enrollees, with knowledge and insights. Informed decisions driven by data will lead to improved healthcare access, ultimately catalyzing positive change within the Medicaid and Medicare landscape. This publication represents a call to action, urging all players in the healthcare ecosystem to embrace data-driven solutions, adapt to the evolving landscape, and work collaboratively to advance the cause of accessible and effective healthcare for all.

THU163 Traffic Light Alerting System For Adherence Risk Management To Improve Catch-Up Growth In Patients Receiving Recombinant Human Growth Hormone Therapy

Abstract Disclosure: S. Loche: Advisory Board Member; Self; SL has received advisory board fees from the healthcare business of Merck KGaA, Darmstadt, Germany. Consulting Fee; Self; SL has received consultancy fees from the healthcare business of Merck KGaA, Darmstadt, Germany. Speaker; Self; SL has received lecture fees from the healthcare business of Merck KGaA, Darmstadt, Germany. P. van Dommelen: Consulting Fee; Self; PvD has a consultancy agreement with the healthcare business of Merck KGaA, Darmstadt, Germany. E. Koledova: Employee; Self; EK is an employee of the healthcare business of Merck KGaA, Darmstadt, Germany. Stock Owner; Self; EK holds shares in the healthcare business of Merck KGaA, Darmstadt, Germany. Background: In our previous study, a data-driven clinical decision support system based on “traffic light” visualizations for adherence risk management in patients receiving recombinant human growth hormone (r-hGH) treatment was developed.1 Two “traffic lights” were most promising: mean adherence and standard deviation (SD) of hours to next injection (administering injections at around the same time each day). We aimed to study the effect of these two adherence-based traffic lights on catch-up growth in the first year of treatment in patients with growth hormone deficiency (GHD) and those born small for gestational age (SGA). Data and Methods: Height and adherence data were extracted from the easypod™ connect ecosystem and taken from the easypod™ connect observational study.2 Patients with: height standard deviation scores (HSDS) of ≤-2 at treatment start; age 2-15 years at treatment start; and ≥1 measurement and adherence data available in the first year of treatment, were selected. Mean adherence was classified as high (≥85%), intermediate (>56%-<85%), or low (≤56%); high (≥15.9 [P95]), intermediate (≥10.1 [P85]), or low (<10.1) were used for SD of hours to next injection. Linear regression analysis was performed with ΔHSDS between treatment start and first year of treatment as dependent variables and the adherence-based values as independent variables, all adjusted for age and HSDS at treatment start. Results: In total, data for 1,045 patients (776 and 269 with GHD and those born SGA, respectively) were available. Only some of the adherence-based categories had a significant and independent effect on ΔHSDS (P=0.004). Adjusted for age and HSDS at treatment start, patients with high/intermediate mean adherence had, on average, a +0.15 SD higher ΔHSDS (P=0.004), while this was +0.09 SD for patients with a low SD of hours to the next injection (P=0.003). Therefore, patients with both high/intermediate mean adherence and a low SD of hours to the next injection had, on average, a +0.24 SD higher ΔHSDS compared with patients with both low mean adherence and a high SD of hours to the next injection. Conclusions: Our research shows that good adherence and administering injections around the same time each day play an essential role in optimizing catch-up growth. Adherence-based traffic lights can alert clinicians to have discussions with patients/caregivers to mitigate the risk of sub-optimal adherence and, consequently, improve catch-up growth.

A data-driven decision support system for service completion prediction in last mile logistics

A data-driven decision support system for service completion prediction in last mile logistics

Temporal characterization of acute pain and toxicity kinetics during radiation therapy for head and neck cancer. A retrospective study.

Pain during Radiation Therapy (RT) for oral cavity/oropharyngeal cancer (OC/OPC) is a clinical challenge due to its multifactorial etiology and variable management. The objective of this study was to define complex pain profiles through temporal characterization of pain descriptors, physiologic state, and RT-induced toxicities for pain trajectories understanding. Using an electronic health record registry, 351 OC/OPC patients treated with RT from 2013 to 2021 were included. Weekly numeric scale pain scores, pain descriptors, vital signs, physician-reported toxicities, and analgesics were analyzed using linear mixed effect models and Spearman's correlation. Area under the pain curve (AUCpain) was calculated to measure pain burden over time. Median pain scores increased from 0 during the weekly visit (WSV)-1 to 5 during WSV-7. By WSV-7, 60% and 74% of patients reported mouth and throat pain, respectively, with a median pain score of 5. Soreness and burning pain peaked during WSV-6/7 (51%). Median AUCpain was 16% (IQR (9.3-23)), and AUCpain significantly varied based on gender, tumor site, surgery, drug use history, and pre-RT pain. A temporal increase in mucositis and dermatitis, declining mean bodyweight (-7.1%; P < 0.001) and mean arterial pressure (MAP) 6.8 mmHg; P < 0.001 were detected. Pulse rate was positively associated while weight and MAP were negatively associated with pain over time (P < 0.001). This study provides insight on in-depth characterization and associations between dynamic pain, physiologic, and toxicity kinetics. Our findings support further needs of optimized pain control through temporal data-driven clinical decision support systems for acute pain management.

Design an efficient data driven decision support system to predict flooding by analysing heterogeneous and multiple data sources using Data Lake

Floods are the most common natural disaster in several countries throughout the world. Flooding has a major impact on people's lives and livelihoods. The impact of flood disasters on human lives can be mitigated by developing effective flood forecasting and prediction models. The majority of flood prediction models do not take all flood-causing factors into account when they are designed. It is difficult to collect and handle some of these flood-causing variables since they are heterogeneous in nature. This paper presents a new big data architecture called Data Lake, which can ingest and store all important flood-causing heterogeneous data sources in their raw format for machine learning model creation. The statistical relevance of important flood producing factors on flood prediction outcome is determined utilizing inferential statistical approaches. The outcome of this research is to create flood warning systems that can alert the public and government officials so that they can make decisions in the event of a severe flood, reducing socioeconomic loss.•Flood causing factors are from heterogeneous sources, so there is no big data architecture for handling variety of data sources.•To provide data architectural solution using data lake for collecting and analysing heterogeneous flood causing factors.•Uses inferential statistical approach to determine importance of different flood causing factors in design of efficient flood prediction models.

A data-driven intelligent decision support system that combines predictive and prescriptive analytics for the design of new textile fabrics

In this paper, we propose an Intelligent Decision Support System (IDSS) for the design of new textile fabrics. The IDSS uses predictive analytics to estimate fabric properties (e.g., elasticity) and composition values (% cotton) and then prescriptive techniques to optimize the fabric design inputs that feed the predictive models (e.g., types of yarns used). Using thousands of data records from a Portuguese textile company, we compared two distinct Machine Learning (ML) predictive approaches: Single-Target Regression (STR), via an Automated ML (AutoML) tool, and Multi-target Regression, via a deep learning Artificial Neural Network. For the prescriptive analytics, we compared two Evolutionary Multi-objective Optimization (EMO) methods (NSGA-II and R-NSGA-II) when optimizing 100 new fabrics, aiming to simultaneously minimize the physical property predictive error and the distance of the optimized values when compared with the learned input space. The two EMO methods were applied to design of 100 new fabrics. Overall, the STR approach provided the best results for both prediction tasks, with Normalized Mean Absolute Error values that range from 4% (weft elasticity) to 11% (pilling) in terms of the fabric properties and a textile composition classification accuracy of 87% when adopting a small tolerance of 0.01 for predicting the percentages of six types of fibers (e.g., cotton). As for the prescriptive results, they favored the R-NSGA-II EMO method, which tends to select Pareto curves that are associated with an average 11% predictive error and 16% distance.

Data-driven decision support system for degrading assets and its application under the perspective of a railway component

Transportation plays a crucial role in facilitating economic and social interactions, but also significantly contributes to global pollution. To address this sustainability challenge, smart use of resources and transport modes is needed. To contribute to more efficient mobility in worldwide transportation, railways should have better maintenance. Within railway vehicles, the wheelset component is essential for the safety of the train, preventing derailment. However, degradation resulting from the wheel-rail contact during operation may compromise the proper functioning of the components, requiring maintenance to be carried out. Nowadays, the accumulation in the last years of information and the technological ease to generate large amounts of data in inspection and maintenance activities in railway companies allow for implementing robust and reliable data-driven approaches to improve the maintenance of the components. With this scenario in the background, this paper proposes a data-driven decision support system for maintenance actions in degrading assets. Predictive degradation models of wear and damage are developed. The uncertainty of degradation data obtained in measurement condition monitoring activities is quantified and used to evaluate the impact on optimal maintenance strategies. Results obtained indicate that optimized maintenance actions can extend the lifespan of the wheelset and rail components and reduce their long-term maintenance costs. Outputs from the decision support system suggest better maintenance practices to be performed in railway companies.

A data-driven decision support system for sustainable supplier evaluation in the Industry 5.0 era: A case study for medical equipment manufacturing

Effective supplier management is critical for an enterprise’s success, as supplier procurement accounts for up to approximately 70% to 80% of total manufacturing costs. Correct supplier selection can ensure the competitiveness of the enterprise and contribute to supply chain integration and innovation. In recent years, supplier evaluation frameworks based on Industry 4.0 concepts have contributed to the development of the industry. However, the novel concepts of Industry 5.0 require examination from a people-oriented and sustainable perspective. Unfortunately, at the present time, supplier evaluation frameworks based on Industry 5.0 are lacking. Therefore, the primary task of this study is to develop a novel and comprehensive supplier evaluation framework for the Industry 5.0 era. This study proposes a data-driven decision support system to execute the supplier evaluation process. First, variable precision- dominance-based rough set approach (VC-DRSA) is applied to extract the core criteria, to remove the noise factors and to generate decision rules for the decision-makers’ reference. Second, the criterion importance through intercriteria correlation (CRITIC) approach is adopted to obtain the dependency weights of the core criteria and their ranking. Finally, a modified classifiable technique for order preference by similarity to ideal solution (CTOPSIS) is used to integrate the final performance values of suppliers when new alternative suppliers are added. The research concept is in line with the conception of data-driven decision support in business intelligence and does not rely on the subjective judgments and opinions of experts. Data provided by a multinational medical equipment manufacturer are used as an example to demonstrate the proposed model. VC-DRSA retains nine core criteria from the original twenty criteria, which greatly reduces the labor and cost of supplier audits. In addition, the CRITIC results show that digital transformation, real-time information sharing, and organizational culture transformation are the three main factors affecting the development of enterprises towards Industry 5.0. The results show that CTOPSIS can be used to quickly assess the ratings of new alternative suppliers are listed.

Pay-for-performance programs effectiveness in healthcare: the case of the end-stage renal disease quality incentive program.

This paper focuses on Medicare's End-Stage Renal Disease Quality Incentive Program (QIP). QIP aims to promote high-quality services in outpatient dialysis facilities by tying their payments to their performance on pre-specified quality measures. In this paper, employing principal-agent theory, we examine the effectiveness of QIP by exploring the changes in various clinical/operational measures when they become a part of the program as a performance measure. We study five QIP quality measures; two are operational: hospitalization and readmission. And three others are clinical: blood transfusion, hypercalcemia, and dialysis adequacy. Overall, we observe a significant improvement in all QIP quality measures after being included in the program, except for readmission. We recommend adjusting the weight and redesigning the readmission measure for Medicare to incentivize providers to reduce readmission. We also discuss establishing care coordination and employing data-driven clinical decision support systems as opportunities for dialysis facilities to improve the care delivery process.

Assessment of Micronutrient Content and Strategies for Improvement of Micronutrient Status in the Soils of Bhandara Tehsil, Maharashtra, India

The study was undertaken to determine the available micro-nutrient status of the Bhandara tehsil of Maharashtra with the help of GPS-based one hundred-and-five soil samples during the period of 2021-2022 from paddy growing fields. Samples were analyzed at the Soil Science and Agricultural Chemistry Section, College of Agriculture, Nagpur. The results revealed that soils were clay loam to sandy clay loam in texture. The study area was slightly acidic to slightly alkaline and non-saline in reaction. The mean values of 0.42 mg kg-1, 5.44 mg kg-1, 6.39 mg kg-1, 0.98 mg kg-1, and 0.90 mg kg-1 for Zn, Fe, Mn, Cu and B respectively, were recorded. The soil nutrient index values showed that available Zn was low; available Fe was moderate; available Mn and B were moderately high; and available Cu was high. The farmers of the study area mostly adopt improper nutrient management practices which might have resulted in poor fertility status in the area.  Taking these things into consideration, a data-driven Decision Support System (DSS) on micronutrient management in soil has been given. To address this problem, farmers of Bhandara tehsil may use micronutrient fertilizers or implement soil management practices that promote the uptake and availability of micronutrients to plants.

Towards an Integrated Approach for Preserving Data Utility, Privacy and Fairness

Data reusability has become a distinct characteristic of scientific, commercial, and administrative practices nowadays. However, an unlimited and careless reuse of data may lead to privacy breaches and unfair impacts on individuals and vulnerable groups. Data content adaption is a key aspect of preserving data privacy and fairness. Often, such content adaption affects data utility adversely. Further, the interaction between privacy protection and fairness protection can be subject to making trade-offs because mitigating privacy risks may adversely affect detecting unfairness and vice versa. Therefore, there is a need for research on understanding the interactions between data utility, privacy and fairness. To this end, in this contribution, we use concepts from causal reasoning and argue for adopting an integrated view on data content adaption for data driven decision support systems. This asks for considering the operation context wholistically. By means of two cases, we illustrate that, in some situations, local data content adaption may lead to low data quality and utility. An integrated wholistic approach, however, may result in reuse of the original data (i.e., without content adaption, thus in higher data utilization) without adversely affecting privacy and fairness. We discuss some implications of this approach and sketch a few directions for future research.

A decision support model to evaluate liveability in the context of urban vibrancy

Liveability can be accepted as an umbrella term covering all the factors that make a place to live. We recognize the versatility of urban liveability and focus on the vibrancy aspect. Regarding the literature, we compile variables affecting urban liveability under the economic, image, and use value of place. This article aims to present a data-driven decision support system to evaluate different dimensions of vibrancy-focused liveability. We adopt a knowledge discovery process to handle the complexity of the liveability concept. This study develops a conditional-based relationship network of vibrancy parameters through the Bayesian Belief Network (BBN). Then, we assess the BBN's correlations with statistics and causal relations with the survey in this study.These results mostly agree with the findings of the relevant literature. The economic value results show that the high density, diversity and accessibility add a premium to the land value of properties. The use value results also demonstrate that the diversity and density of activities, cultural attributes, and high accessibility support place attractiveness. The selected streetscape variables improve image value, except for building enclosure and condition. The study has the potential for urban planners to vitalize neighborhoods by considering urban activities and urban physical attributes.