Kenji Shimizu’s Post

Random Projection Random projection is a technique used to reduce the dimensionality of a set of points which lie in Euclidean space. According to theoretical results, random projection preserves distances well. Random projection is one of the fastest dimensionality reduction method. It can be done with integer computations and sparse matrix projection, which means further computational savings in database applications. The core idea behind random projection is given in the Johnson-Lindenstrauss lemma, which states that if points in a vector space are of sufficiently high dimension, then they may be projected into a suitable lower-dimensional space in a way which approximately preserves pairwise distances between the points with high probability. Random projection is computationally simple: form the random matrix "R" and project data matrix X onto k dimensions. If the data matrix X is sparse with about c nonzero entries per column, then the complexity of this operation is of order O(ckN). For a detailed survey, see my article: https://lnkd.in/d7di8t9m

Last 10 days to submit your abstract in our session in the EMS 2024 conference!! ⛈️🖥️🌪️ This session covers a wide range of topics related to Weather and Climate modelling. You can have a look at it here: https://lnkd.in/eTtVJ2up

🤔 Spatial analysis is a type of geographical analysis that seeks to understand patterns, relationships, and processes within a given spatial context. Type of spatial Analysis 👇 1. Descriptive Spatial Analysis: This involves summarizing the main features of spatial data, such as mean center, standard distance, and spatial distribution patterns. 2. Exploratory Spatial Data Analysis (ESDA): This method helps in identifying patterns, trends, and relationships in spatial data without prior hypotheses. Techniques include spatial autocorrelation and hot spot analysis. 3. Spatial Autocorrelation Analysis: This assesses the degree to which objects in a spatial dataset are similar to their neighbors. Measures like Moran's I and Geary's C are commonly used. 4. Point Pattern Analysis: Used to study the spatial arrangement of points. Methods include nearest neighbor analysis, K-function, and Quadrat analysis. 5. Spatial Interpolation: This estimates values at unsampled locations within the area covered by existing observations. Techniques include Kriging, Inverse Distance Weighting (IDW), and spline interpolation. 6. Spatial Regression: This incorporates spatial relationships into regression models to account for spatial dependence. Examples are spatial lag models and spatial error models. 7. Geostatistics: A set of statistical techniques for analyzing spatially correlated data. This includes variography and Kriging. 8. Network Analysis: Used to study and analyze spatial networks such as transportation or utility networks. Techniques include shortest path analysis and network flow analysis. 9. Spatial Simulation: This involves creating models that simulate spatial processes and patterns over time. Cellular automata and agent-based modeling are examples. 10. Geographically Weighted Regression (GWR): This method accounts for spatial heterogeneity by allowing local variations in regression relationships. 11. Spatial Overlay Analysis: Combines multiple layers of spatial data to identify relationships between them. Techniques include Boolean overlay, weighted overlay, and fuzzy overlay. 12. Cluster Analysis: Identifies groups of similar objects within a spatial dataset. Methods include K-means clustering and hierarchical clustering. #data #map #analysis #geography #clustering #gis #geogis #mapping #overlay #cluster #Kriging #network #simulation #fuzzy #mcda #Geostatistics #techniques #regression #relationship #layer #idw #modeling

Interested in quantifying the evolution of a characteristic in time 📈 by modelling multiple records of the same metric taken over time ⏳ on the same subject 🙎♂️ ? That would be a repeated measures study context… Join us to learn how to use mixed models methodology for repeated measures 🤹♀️. In particular, we will fit marginal models with different covariance structures 🎛. We will use R. More details here https://lnkd.in/dN5w5ExN #CovariancePattern #CovarianceStructure #MarginalModel #RepeatedMeasures #RandomCoefficients

Processing Tree Canopy Height with Sentinel Series and GEDI 1. **Multi-sensor Data Loading**: The script begins by loading data from various sources, including SAR imagery from Sentinel-1, optical imagery from Sentinel-2, elevation data from SRTM, and land cover data from ESA World Cover. Integrating multi-sensor data provides rich environmental information, enabling more comprehensive analysis. 2. **Data Preparation**: Data from various sources are prepared with various operations, including filtering to remove disturbances such as clouds and shadows, image merging, and reprojection to ensure consistency and projection suitability. 3. **Training Dataset Preparation**: The training dataset is intelligently prepared by selecting point samples from GEDI data, which provides information on tree canopy height. Relevant predictor attributes, such as radar intensity, optical reflectance, elevation, and slope, are extracted from other images and included in the dataset. 4. **Regression Modeling with Random Forest Algorithm**: To model tree canopy height, the Random Forest Classifier (RFC) algorithm, which has proven effective in regression problems, is used. This model is trained using the previously prepared training dataset, and parameters such as the number of trees and maximum depth are set to improve model performance. 5. **Model Evaluation**: To evaluate model performance, validation is performed using a dataset that has never been seen before. Root Mean Squared Error (RMSE) is used as the evaluation metric, measuring how well the model fits observation data. Evaluation results, including RMSE for both the training and validation datasets, are presented to provide a clear understanding of model accuracy. 6. **Exporting Results**: After the model is assessed satisfactorily, the resulting regression images are exported for further analysis or integration with other platforms, enabling stakeholders to make informed decisions about environmental management. "Using multi-sensor data from Sentinel-1, Sentinel-2, SRTM, and GEDI, we attempted to model tree canopy height. After undergoing training and validation, the model yielded a Training RMSE of 5.22 and Validation RMSE of 6.38." #RemoteSensing #MachineLearning #EnvironmentalScience #GEE

Are you an engineer working on functional materials? Are you looking to discover new metal organic frameworks (MOFs)? Check out the #CSD. It’s not just for Pharma and it’s not just for Chemists! #MOFs #metalorganicframeworks #materialscience

We have just posted a preprint on a system identification method to arXiv. The proposed method Violina is a mathematical optimization algorithm to identify a non-Markovian dynamical system using a set of multiple multidimensional time-series data. We numerically demonstrated that the model obtained using Violina has a greater generalization performance compared to a model obtained using dynamic mode decomposition. Also, we can use Violina for high-dimensional time-series data whose spatial dimension is around or more than 100.

I am proud to share that our latest (and my first!) contribution entitled "A nonparametric penalized likelihood approach to density estimation of space-time point patterns" has been published in "Spatial Statistics". 💡 We propose a novel nonparametric method to estimate the unknown spatio-temporal probability density function associated with point patterns spatially observed on complex domains of various kinds. ✏ We establish some important theoretical properties of the considered estimator and develop a flexible and efficient estimation procedure. 📊 We thoroughly validate the proposed method, by means of several simulation studies and applications to real-world data. Authors: Blerta Begu, Simone Panzeri, Eleonora Arnone, Michelle Carey, Laura M. Sangalli Code available at: https://lnkd.in/eX9KsgqQ Paper available at: https://lnkd.in/eqKQ_mzz #nonparametric #densityestimation #spatiotemporal #pointpatterns

🌐 Excited to delve into the world of Stochastic Spatial Interpolation and Processes! 🌟 In today's rapidly evolving landscape of spatial data analysis, understanding stochastic spatial interpolation and processes is paramount. With its innovative techniques, this field offers dynamic insights into environmental, urban, and geospatial phenomena. 🔍 What is Stochastic Spatial Interpolation and Process? Stochastic spatial interpolation involves predicting values at unsampled locations within a geographical area based on available data points. By incorporating randomness and probability distributions, it accounts for uncertainties inherent in spatial data. 🌱 Key Applications: 1️⃣ Environmental Monitoring: Assessing air quality, soil contamination, and species distribution. 2️⃣ Urban Planning: Predicting population density, traffic patterns, and land use changes. 3️⃣ Natural Resource Management: Estimating water availability, forest cover, and crop yield. 🔬 Advanced Techniques: 1️⃣ Kriging: Utilizes spatial correlation to predict values and quantify uncertainty. 2️⃣ Gaussian Processes: Models spatial dependencies using kernel functions, offering flexible predictions. 3️⃣ Monte Carlo Simulation: Generates multiple realizations of spatial patterns, capturing variability. 📈 Benefits: 1️⃣ Enhanced Accuracy: Incorporates spatial autocorrelation and uncertainty estimation. 2️⃣ Improved Decision-Making: Facilitates risk assessment and resource allocation. 3️⃣ Robustness: Adaptable to various data types and spatial scales. 💡 Real-World Impact: From climate modeling to infrastructure planning, stochastic spatial interpolation and processes empower researchers, policymakers, and businesses to make informed decisions in a complex, interconnected world. Let's harness the power of spatial data analytics to unlock new insights and drive positive change! 💪 #SpatialAnalysis #DataScience #GIS #StochasticModeling #SpatialInterpolation #SpatialProcesses #DataAnalytics