Spatial Decision Support Systems with Automated Machine Learning: A Review
Abstract
:1. Introduction
2. Methods
2.1. Step One: Literature Search
2.2. Step Two: Review and Discussion
3. Search Results
4. Review Results
4.1. Spatial Decision Support Systems (SDSS)
4.2. Automated Machine Learning (AutoML)
4.3. Spatial Problems in SDSS and ML
4.3.1. Spatial Estimation
4.3.2. Spatial Optimization
4.3.3. Spatial Clustering
4.3.4. Spatial Simulation
4.3.5. Spatial Insight
4.4. SDSS with AutoML
5. Discussion
5.1. SDSS with AutoML Framework
- Spatial problems: what are the spatial problem(s) to solve given the context and actors in decision making?
- Metrics: what metrics are appropriate for evaluating and measuring the defined spatial problem(s)?
- Potential solutions: with the given spatial problem(s) and metric(s), what are the potential solutions to solve the spatial problem(s)?
5.1.1. Key Consideration 1: Spatial Problems
- Independent: decisions made by a decision maker with full responsibility and authority;
- Sequential interdependent: decisions made partially by a decision maker and partially by another party;
- Pooled interdependent: decisions made from negotiation and interaction among decision makers.
- Intelligence: examination of spatial data to identify spatial problems that require decisions and have the opportunity for change;
- Design: determining possible and alternative decisions and developing approaches to evaluate and understand the decisions;
- Choice: selecting from the range of possible and alternative decisions after evaluating and understanding each decision.
- Spatial estimation: calculation of unknown values in space (e.g., prediction, overlay);
- Spatial optimization: optimization of entities in space (e.g., placement, routing);
- Spatial clustering: organization of entities in space (e.g., grouping, categorization, zoning);
- Spatial simulation: simulation of phenomena in space (e.g., physics, theoretical simulations);
- Spatial insight: interpretation and exploration of phenomena and entities in space (e.g., interactive maps, visualizations, plots).
5.1.2. Key Consideration 2: Metrics
- Regression: estimation or prediction of continuous target values given other factors (e.g., calculating landslide risk, predicting number of traffic collisions);
- Classification: identification of discrete target values (groups or categories) given other factors (e.g., predicting landuse types, identifying building types);
- Clustering: organization of entities into groups or categories based on characteristics (e.g., identifying crime zones, disease areas).
5.1.3. Key Consideration 3: Potential Solutions
- Data size: how large or small the data are;
- Interpretability: whether the potential solutions need to be interpreted or simply produce outputs to be used (e.g., identifying important variables vs. prediction performance);
- Resource constraints: time, computation, and expertise constraints (e.g., runtime of models, training to interpret results);
- Update frequency: how often potential solutions need to be re-evaluated (e.g., new input data, new model/algorithm adjustments).
5.2. Implementation Challenges
5.2.1. Data Quality
5.2.2. Model Interpretability
5.2.3. Evidence of Usefulness
5.3. Research Opportunities
5.3.1. Spatial AutoML
5.3.2. Resource-Aware Approaches
5.3.3. Collaborative and Connected Systems
5.3.4. Human-Centered System Design
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
ABM | Agent-based modelling |
AI | Artificial intelligence |
AUC | Area under the ROC curve |
AutoML | Automated machine learning |
CA | Cellular automata |
DBSCAN | Density-based spatial clustering of applications with noise |
DT | Decision trees |
GA | Genetic algorithms |
GIS | Geographic information systems |
GW-ML | Geographically weighted machine learning |
GWR | Geographically weighted regression |
LISA | Local indicators of spatial association |
LSTM | Long short-term memory |
MAE | Mean absolute error |
MCDA | Multiple criteria decision analysis |
ML | Machine learning |
MSE | Mean squared error |
NAS | Neural architecture search |
NB | Naïve Bayes |
NN | Neural networks |
PSO | Particle swarm optimization |
PSS | Planning support systems |
RL | Reinforcement learning |
RMSE | Root mean square error |
SDSS | Spatial decision support systems |
SSE | Sum of square error |
SVM | Support vector machines |
TPOT | Tree-based pipeline optimization tool |
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Spatial Problem | Applications | Spatial Methods | ML Methods |
---|---|---|---|
Estimation | Land use classification Disease risk calculation Disaster risk prediction | MCDA Spatial Regression GW-ML | SVM RF NN |
Optimization | Facility selection Delivery routing Infrastructure placement Traffic control | MCDA PSO | GA |
Clustering | Crime hotspots Agricult./disease zoning Social-media analysis Travel analysis | LISA Hotspot analysis SaTScan | K-means DBSCAN |
Simulation | Traffic control simulation Wildfire simulation Land use simulation | Cellular Automata ABM Custom models | RL |
Insight | Risk factor identification Interactive exploration Data/model interpretation | Spat. Regression Webmapping Interactive models | Feat. selection Feat. importance Pipeline explore |
AutoML Approach | AutoML Method/Software | Data | SDSS Applications |
---|---|---|---|
Ensembling (n = 6) | HO Extra trees class. | Satellite imagery UAV imagery Sensors Surveys Sociodemographic Simulations | Crop prediction Violence rate prediction Total water storage est. Landslide risk est. Soil estimation |
Bayesian (n = 4) | Bayesian Opt. Auto-Sklearn Bayesian Nets MATLAB fitrauto | Satellite imagery UAV imagery Sensors Surveys | Crop prediction/classify Soil estimation Env. impact assessment |
Neural Nets (n = 3) | NAS Deep Learning LSTM | Satellite imagery Vehicle imagery | Meteorological forecasting Road health inspection |
Evolutionary (n = 2) | Fedot TPOT | Sensors Surveys | Oil-well placement Waterlogging risk est. |
Other (n = 2) | Autogluon AutonML AlphaD3M | Sociodemographic Satellite imagerySurveys | Violence rate prediction Water potential mapping |
Task | Metrics |
---|---|
Regression | Error, correlation coefficient, MSE, MAE, RMSE |
Classfication | Accuracy, precision, recall, sensitivity, specificity, F1 score, AUC, ROC |
Clustering | Euclidean distance, Rand index, entropy, purity, silhouette soefficient, Dunn’s index, Calinski–Harabasz index, homogeneity |
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Wen, R.; Li, S. Spatial Decision Support Systems with Automated Machine Learning: A Review. ISPRS Int. J. Geo-Inf. 2023, 12, 12. https://doi.org/10.3390/ijgi12010012
Wen R, Li S. Spatial Decision Support Systems with Automated Machine Learning: A Review. ISPRS International Journal of Geo-Information. 2023; 12(1):12. https://doi.org/10.3390/ijgi12010012
Chicago/Turabian StyleWen, Richard, and Songnian Li. 2023. "Spatial Decision Support Systems with Automated Machine Learning: A Review" ISPRS International Journal of Geo-Information 12, no. 1: 12. https://doi.org/10.3390/ijgi12010012