A Logit Model for Budget Allocation Subject to Multi Budget Sources

2011 ◽  
Vol 2 (3) ◽  
pp. 1-17 ◽  
Author(s):  
Saeed A. Bagloee ◽  
Christopher G. Reddick
Keyword(s):  
Logit Model ◽  
Large Scale ◽  
Budget Allocation ◽  
Solution Algorithm ◽  
Influential Factors ◽  
Programming Formulation ◽  
Extended System ◽  
Multiple Sources ◽  
Unknown Parameters ◽  
Allocation Process

In a complex and extended system such as a government, the proper allocation of the budget to its sub-entities is always a major challenge. As such for cases like governments, a situation in which multiple budget sources with different concerns available to the sub-entities is common. This study develops an applicable model for large-scale cases in which identifying the flow of capital or budget from (multiple) sources to the sub-entities is sought. Since the influential factors to the allocation process may be mingled with some unknown parameters (as well as known factors) a logit model is developed from past panel data. The logit model is based on the concept of utility, which quantifies the advantage of approaching budget-sources for the sub-entities. Then the budget allocation problem of logit form is written as a mathematical programming formulation for which Successive Coordinate Descent (SCD) method is proposed as the solution algorithm. In this paper, the proposed methodology is tested numerically. The results of this study show there is strong evidence that some of the entities’ properties can be altered in order to achieve a better budget allocation.

A Logit Model for Budget Allocation Subject to Multi Budget Sources

2012 ◽  
pp. 311-327
Author(s):  
Saeed A. Bagloee ◽  
Christopher G. Reddick
Keyword(s):  
Mathematical Programming ◽  
Logit Model ◽  
Large Scale ◽  
Budget Allocation ◽  
Solution Algorithm ◽  
Influential Factors ◽  
Programming Formulation ◽  
Multiple Sources ◽  
Unknown Parameters ◽  
Allocation Process

In a complex and extended system such as a government, the proper allocation of the budget to its sub-entities is always a major challenge. As such for cases like governments, a situation in which multiple budget sources with different concerns available to the sub-entities is common. This study develops an applicable model for large-scale cases in which identifying the flow of capital or budget from (multiple) sources to the sub-entities is sought. Since the influential factors to the allocation process may be mingled with some unknown parameters (as well as known factors) a logit model is developed from past panel data. The logit model is based on the concept of utility, which quantifies the advantage of approaching budget-sources for the sub-entities. Then the budget allocation problem of logit form is written as a mathematical programming formulation for which Successive Coordinate Descent (SCD) method is proposed as the solution algorithm. In this paper, the proposed methodology is tested numerically. The results of this study show there is strong evidence that some of the entities’ properties can be altered in order to achieve a better budget allocation.

scholarly journals Accessible Routes Integrating Data from Multiple Sources

2020 ◽  
Vol 10 (1) ◽  
pp. 7
Author(s):  
Miguel R. Luaces ◽  
Jesús A. Fisteus ◽  
Luis Sánchez-Fernández ◽  
Mario Munoz-Organero ◽  
Jesús Balado ◽  
...  
Keyword(s):  
Information System ◽  
Data Model ◽  
Large Scale ◽  
Heterogeneous Data ◽  
Multiple Sources ◽  
Heterogeneous Data Sources ◽  
Different Types ◽  
Software Sensors ◽  
The City

Providing citizens with the ability to move around in an accessible way is a requirement for all cities today. However, modeling city infrastructures so that accessible routes can be computed is a challenge because it involves collecting information from multiple, large-scale and heterogeneous data sources. In this paper, we propose and validate the architecture of an information system that creates an accessibility data model for cities by ingesting data from different types of sources and provides an application that can be used by people with different abilities to compute accessible routes. The article describes the processes that allow building a network of pedestrian infrastructures from the OpenStreetMap information (i.e., sidewalks and pedestrian crossings), improving the network with information extracted obtained from mobile-sensed LiDAR data (i.e., ramps, steps, and pedestrian crossings), detecting obstacles using volunteered information collected from the hardware sensors of the mobile devices of the citizens (i.e., ramps and steps), and detecting accessibility problems with software sensors in social networks (i.e., Twitter). The information system is validated through its application in a case study in the city of Vigo (Spain).

scholarly journals PEDRERA. Positive Energy District Renovation Model for Large Scale Actions

Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2833
Author(s):  
Paolo Civiero ◽  
Jordi Pascual ◽  
Joaquim Arcas Abella ◽  
Ander Bilbao Figuero ◽  
Jaume Salom
Keyword(s):  
Simulation Model ◽  
Performance Indicators ◽  
Large Scale ◽  
Key Performance Indicators ◽  
Positive Energy ◽  
Design Phase ◽  
Multiple Sources ◽  
Reliable Prediction ◽  
Sensitive Analysis ◽  
Web Platform

In this paper, we provide a view of the ongoing PEDRERA project, whose main scope is to design a district simulation model able to set and analyze a reliable prediction of potential business scenarios on large scale retrofitting actions, and to evaluate the overall co-benefits resulting from the renovation process of a cluster of buildings. According to this purpose and to a Positive Energy Districts (PEDs) approach, the model combines systemized data—at both building and district scale—from multiple sources and domains. A sensitive analysis of 200 scenarios provided a quick perception on how results will change once inputs are defined, and how attended results will answer to stakeholders’ requirements. In order to enable a clever input analysis and to appraise wide-ranging ranks of Key Performance Indicators (KPIs) suited to each stakeholder and design phase targets, the model is currently under the implementation in the urbanZEB tool’s web platform.

scholarly journals Developing a vocabulary and ontology for modeling insect natural history data: example data, use cases, and competency questions

2019 ◽  
Vol 7 ◽  
Author(s):  
Brian Stucky ◽  
James Balhoff ◽  
Narayani Barve ◽  
Vijay Barve ◽  
Laura Brenskelle ◽  
...  
Keyword(s):  
Natural History ◽  
Large Scale ◽  
Use Cases ◽  
Data Systems ◽  
Multiple Sources ◽  
History Data ◽  
Insect Ecology ◽  
Many Sources ◽  
Multicellular Organisms ◽  
Initial Results

Insects are possibly the most taxonomically and ecologically diverse class of multicellular organisms on Earth. Consequently, they provide nearly unlimited opportunities to develop and test ecological and evolutionary hypotheses. Currently, however, large-scale studies of insect ecology, behavior, and trait evolution are impeded by the difficulty in obtaining and analyzing data derived from natural history observations of insects. These data are typically highly heterogeneous and widely scattered among many sources, which makes developing robust information systems to aggregate and disseminate them a significant challenge. As a step towards this goal, we report initial results of a new effort to develop a standardized vocabulary and ontology for insect natural history data. In particular, we describe a new database of representative insect natural history data derived from multiple sources (but focused on data from specimens in biological collections), an analysis of the abstract conceptual areas required for a comprehensive ontology of insect natural history data, and a database of use cases and competency questions to guide the development of data systems for insect natural history data. We also discuss data modeling and technology-related challenges that must be overcome to implement robust integration of insect natural history data.

Discrete Dynamic Shortest Path Problems in Transportation Applications: Complexity and Algorithms with Optimal Run Time

1998 ◽  
Vol 1645 (1) ◽  
pp. 170-175 ◽  
Author(s):  
Ismail Chabini
Keyword(s):  
Large Scale ◽  
Shortest Paths ◽  
Transportation Systems ◽  
Solution Algorithm ◽  
Shortest Path Problems ◽  
Discrete Dynamic ◽  
Solution Algorithms ◽  
Dynamic Network Models ◽  
Early Results ◽  
Run Time

A solution is provided for what appears to be a 30-year-old problem dealing with the discovery of the most efficient algorithms possible to compute all-to-one shortest paths in discrete dynamic networks. This problem lies at the heart of efficient solution approaches to dynamic network models that arise in dynamic transportation systems, such as intelligent transportation systems (ITS) applications. The all-to-one dynamic shortest paths problem and the one-to-all fastest paths problems are studied. Early results are revisited and new properties are established. The complexity of these problems is established, and solution algorithms optimal for run time are developed. A new and simple solution algorithm is proposed for all-to-one, all departure time intervals, shortest paths problems. It is proved, theoretically, that the new solution algorithm has an optimal run time complexity that equals the complexity of the problem. Computer implementations and experimental evaluations of various solution algorithms support the theoretical findings and demonstrate the efficiency of the proposed solution algorithm. The findings should be of major benefit to research and development activities in the field of dynamic management, in particular real-time management, and to control of large-scale ITSs.

Digital Mega-Studies as a New Research Paradigm: Governing the Health Research of the Future

2021 ◽  
pp. 155626462110414
Author(s):  
Jessica Bell ◽  
Megan Prictor ◽  
Lauren Davenport ◽  
Lynda O’Brien ◽  
Melissa Wake
Keyword(s):  
Large Scale ◽  
Group Processes ◽  
Research Paradigm ◽  
Multiple Sources ◽  
Individual Level ◽  
Key Characteristics ◽  
Governance Challenges ◽  
Technological Developments ◽  
Multi Stakeholder ◽  
New Research

‘Digital Mega-Studies’ are entirely or extensively digitised, longitudinal, population-scale initiatives, collecting, storing, and making available individual-level research data of different types and from multiple sources, shaped by technological developments and unforeseeable risks over time. The Australian ‘Gen V’ project exemplifies this new research paradigm. In 2019, we undertook a multidisciplinary, multi-stakeholder process to map Digital Mega-Studies’ key characteristics, legal and governance challenges and likely solutions. We conducted large and small group processes within a one-day symposium and directed online synthesis and group prioritisation over subsequent weeks. We present our methods (including elicitation, affinity mapping and prioritisation processes) and findings, proposing six priority governance principles across three areas—data, participation, trust—to support future high-quality, large-scale digital research in health.

scholarly journals Technology Acceptance in Healthcare: A Systematic Review

2021 ◽  
Vol 11 (22) ◽  
pp. 10537
Author(s):  
Adi A. AlQudah ◽  
Mostafa Al-Emran ◽  
Khaled Shaalan
Keyword(s):  
Systematic Review ◽  
Technology Acceptance ◽  
Large Scale ◽  
Empirical Studies ◽  
Influential Factors ◽  
Full Potential ◽  
Eligibility Criteria ◽  
Factors Affecting ◽  
The Usa ◽  
Healthcare Technologies

Understanding the factors affecting the use of healthcare technologies is a crucial topic that has been extensively studied, specifically during the last decade. These factors were studied using different technology acceptance models and theories. However, a systematic review that offers extensive understanding into what affects healthcare technologies and services and covers distinctive trends in large-scale research remains lacking. Therefore, this review aims to systematically review the articles published on technology acceptance in healthcare. From a yield of 1768 studies collected, 142 empirical studies have met the eligibility criteria and were extensively analyzed. The key findings confirmed that TAM and UTAUT are the most prevailing models in explaining what affects the acceptance of various healthcare technologies through different user groups, settings, and countries. Apart from the core constructs of TAM and UTAUT, the results showed that anxiety, computer self-efficacy, innovativeness, and trust are the most influential factors affecting various healthcare technologies. The results also revealed that Taiwan and the USA are leading the research of technology acceptance in healthcare, with a remarkable increase in studies focusing on telemedicine and electronic medical records solutions. This review is believed to enhance our understanding through a number of theoretical contributions and practical implications by unveiling the full potential of technology acceptance in healthcare and opening the door for further research opportunities.

scholarly journals Estimation of the Terrestrial Water Budget over Northern Eurasia through the Use of Multiple Data Sources

2011 ◽  
Vol 24 (13) ◽  
pp. 3272-3293 ◽  
Author(s):  
Tara J. Troy ◽  
Justin Sheffield ◽  
Eric F. Wood
Keyword(s):  
Water Budget ◽  
Large Scale ◽  
Cold Season ◽  
Northern Eurasia ◽  
Past Century ◽  
Multiple Sources ◽  
Multiple Data ◽  
Terrestrial Water ◽  
The Mean

Abstract Northern Eurasia has experienced significant change in its hydrology during the past century. Much of the literature has focused on documenting and understanding the trends rather than documenting the uncertainty that exists in current estimates of the mean hydroclimatology. This study quantifies the terrestrial water budget with reanalysis, hydrologic modeling, remote sensing, and in situ observations and shows there is significant uncertainty in the estimates of precipitation, evapotranspiration, runoff, and terrestrial water storage changes. The spread among the various datasets highlights the scientific community's inability to accurately characterize the hydroclimatology of this region, which is problematic because much attention has focused on hydrologic trends using these datasets. The largest relative differences among estimates exist in the terrestrial storage change, which also is the least studied variable. Seasonally, the spread in estimates relative to the mean is largest in winter, when uncertainty in cold-season processes and measurements causes large differences in the estimates. A methodology is developed that takes advantage of multiple sources of data and observed discharge to improve estimates of precipitation, evapotranspiration, and storage changes. The method also provides a framework to evaluate the errors in datasets for variables that have no large-scale in situ measurements, such as evapotranspiration.

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