Topic Editors
Spatial Decision Support Systems for Urban Sustainability
Topic Information
Dear Colleagues,
Spatial Decision Support Systems (SDSSs) are designed around geospatial data, models, and analytical tools that collectively support human planning and decision-making procedures in multiple application areas. These areas are constantly evolving to better address existing real-world challenges and find innovative ways forward such as in enabling and facilitating urban sustainability.
In this Topic Issue, we focus on the theory and methods of SDSSs and their implementation in the context of urban sustainability. We are interpreting sustainability broadly to mean the understanding and improvement of inputs and processes that optimize the distribution of output patterns. We welcome contributions from research directions that focus on data-oriented approaches (e.g., spatial multicriteria methods and remote sensing), intelligence-based approaches (e.g., machine learning and artificial intelligence methods), model-based approaches (e.g., analytics and simulation methods), and participatory approaches (e.g., citizen science and volunteer GIS methods). In addition, the interoperability between the data, systems, and people can yield innovative contributions. We anticipate these ideas will be developed around the pressing urban sustainability challenges that deal with land use and land cover change, climate change adaptation, and population growth, among others.
The topic "Spatial Decision Support Systems for Urban Sustainability” provides an outlet to publish original research and application papers. Join us as we re-examine existing pathways and explore new ground in the science and applications of SDSSs. We look forward to your contributions.
Dr. Shivanand Balram
Dr. Raja Sengupta
Dr. Jorge Rocha
Topic Editors
Keywords
- Spatial Decision Support Systems (SDSS)
- climate change adaptation
- Geographic Information Systems (GIS)
- land use planning
- remote sensing
- urban informatics
- urban sustainability
Participating Journals
| Journal Name | Impact Factor | CiteScore | Launched Year | First Decision (median) | APC | |
|---|---|---|---|---|---|---|
Geographies
|
- | 1.7 | 2021 | 23.5 Days | CHF 1000 | Submit |
Geomatics
|
- | - | 2021 | 21.8 Days | CHF 1000 | Submit |
ISPRS International Journal of Geo-Information
|
2.8 | 6.9 | 2012 | 36.2 Days | CHF 1700 | Submit |
Land
|
3.2 | 4.9 | 2012 | 17.8 Days | CHF 2600 | Submit |
Urban Science
|
2.1 | 4.3 | 2017 | 24.7 Days | CHF 1600 | Submit |
Sustainability
|
3.3 | 6.8 | 2009 | 20 Days | CHF 2400 | Submit |
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Published Papers (8 papers)
Planned Papers
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: DPSTCN: Dynamic Pattern-aware Spatio-Temporal Convolutional Networks for Traffic Flow Forecasting
Authors: Zeping Dou; Danhuai Guo
Affiliation: Beijing University of Chemical Technology
Abstract: Accurate forecasting of multivariate traffic flow poses formidable challenges, primarily due to the ever-evolving spatio-temporal dynamics and intricate spatial heterogeneity, where the heterogeneity signifies that the correlations among locations are not just related to distance. However, few of the existing models are designed to fully and effectively integrate the above-mentioned features. To address these complexities head-on, this paper introduces a novel solution in the form of Dynamic Pattern-aware Spatio-Temporal Convolutional Networks(DPSTCN). Temporally, the model introduces a novel temporal module , containing temporal convolutional network(TCN) enriched with an enhanced pattern-aware self-attention mechanism, adept at capturing temporal patterns, including local/global dependencies, dynamics and periodicity. Spatially, the model constructs static and dynamic pattern-aware convolutions, leveraging geographical and area-functional information to effectively capture intricate spatial patterns, including dynamics and heterogeneity. Evaluations across four distinct traffic benchmark datasets, consistently demonstrate the state-of-the-art capacity of our model compared to existing eleven approaches, especially great improvements in RMSE value.
