The document discusses applying bio-inspired computational techniques to analyze and visualize changing spatial patterns in spatio-temporal datasets. The research plan involves developing methods to detect and track changes in spatio-temporal clusters using agro-ecological data. Challenges include heterogeneity, scales, boundaries, and visualizing cluster dynamics over time. Self-organizing maps will be used to gain insights into spatial autocorrelation, geographic vs feature space, and visualizing overall structure and temporal patterns.
Introduction to Geographic Information system and Remote Sensing (RS)chala hailu
A geographic information system (GIS) is a system designed to capture, store, manipulate, analyze, manage, and present all types of geographical data
Remote Sensing is Art, science and technology of observing an object, scene or phenomenon by instrument-based techniques without physical contact
Land Use and Land Cover change monitoring of Surajpur Wetland, Uttar Pradesh:...Arnab Saha
Abstract:
Wetlands are extremely important areas throughout the world for wildlife protection, recreation, sediment control and flood prevention. Wetlands are important bird’s habitats and birds use them for feeding, roosting, nesting and rearing their young. In Surajpur Wetland are mainly used for agriculture, fisheries, reclamation for harboring and irrigation purposes. In this paper an attempt is made to study the changes in land use and land cover in Surajpur wetland area over 11 years’ period (2003-2014). LULC is an important component in understanding the interactions of the human activities with the environment and thus it is necessary to be able to simulate changes. The land cover mapping of study area was attempted using remotely sensed images of Landsat and Google Earth imagery. The study area was classified into five categories on the basis of field study, geographical conditions, and remote sensing data. LULC changes have been detected by image processing method in EDRAS imagine 2014 and ArcGIS 10.3. The eleven years’ time period of 2003-2014 shows the major type of land use change. Vegetation area that occupied about around 60 per cent of the Surajpur wetland area in 2003 has decreased to 34.25 percent in 2014. Wetland is increased 8.17 percent and Urban area, Fallow land and Water body also have experienced change. Finally, through the work it is recommended that the wetlands need detail mapping through the use of advance remote sensing techniques like microwave and LIDAR for restoration and management of wetland.
Keywords: LULC, ArcGIS, Surajpur, ERDAS, Remote Sensing
Application of Remote Sensing Techniques for Change Detection in Land Use/ La...iosrjce
IOSR Journal of Applied Geology and Geophysics (IOSR-JAGG) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of Applied Geology and Geophysics. The journal welcomes publications of high quality papers on theoretical developments and practical applications in Applied Geology and Geophysics. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
As part of the GSP’s capacity development and improvement programme, FAO/GSP have organised a one week training in Izmir, Turkey. The main goal of the training was to increase the capacity of Turkey on digital soil mapping, new approaches on data collection, data processing and modelling of soil organic carbon. This 5 day training is titled ‘’Training on Digital Soil Organic Carbon Mapping’’ was held in IARTC - International Agricultural Research and Education Center in Menemen, Izmir on 20-25 August, 2017.
LAND USE /LAND COVER CLASSIFICATION AND CHANGE DETECTION USING GEOGRAPHICAL I...IAEME Publication
Land use and land cover change has become a central component in current strategies for managing natural resources and monitoring environmental changes. Geographical information system and image processing techniques used for the analysis of land use/land cover and change detection of Sukhana Basin of Aurangabad District, Maharashtra state. The tools used ArcGIS10.1 and ERDAS IMAGINE9.1, landsat images of 1996, 2003and 2014. From land use / land cover change detection it is found that during 1996-2014, water bodies cover have loss of 4 Sq. Km. Barren land have 146 Sq.Km. loss and forest area with 96 Sq.Km. loss. It is found that urbanization area has gain of 51 Sq.Km. and agricultural land cover also have gain of 195 Sq.Km.
Change detection analysis in land use / land cover of Pune city using remotel...Nitin Mundhe
Lecture delivered in the National Conference entitled “Monitoring Degraded Lands” jointly organized by Agasti Arts, Commerce and Dadasaheb Rupwate Science
College, Akole and Maharashtra Bhugolshastra Parishad Pune to be held on 4 to 6 February 2014.
Hierarchical Self-Organizing Networks are used to reveal the topology and structure of datasets. Those structures create crisp partitions of the dataset producing branches or prototype vectors that represent groups of data with similar characteristics. However, when observations can be represented by several prototypes with similar accuracy, crisp partitions are forced to classify it in just one group, so crisp divisions usually lose information about the real dataset structure. To deal with this challenge we propose the Fuzzy Growing Hierarchical Self-Organizing Networks (FGHSON). FGHSON are adaptive networks which are able to reflect the underlying structure of the dataset, in a hierarchical fuzzy way. These networks grow by using three variables which govern the membership degree of data observations to its prototype vectors and the quality of the network representation. The resulting structure allows to represent heterogeneous groups and those that present similar membership degree to several clusters
Introduction to Geographic Information system and Remote Sensing (RS)chala hailu
A geographic information system (GIS) is a system designed to capture, store, manipulate, analyze, manage, and present all types of geographical data
Remote Sensing is Art, science and technology of observing an object, scene or phenomenon by instrument-based techniques without physical contact
Land Use and Land Cover change monitoring of Surajpur Wetland, Uttar Pradesh:...Arnab Saha
Abstract:
Wetlands are extremely important areas throughout the world for wildlife protection, recreation, sediment control and flood prevention. Wetlands are important bird’s habitats and birds use them for feeding, roosting, nesting and rearing their young. In Surajpur Wetland are mainly used for agriculture, fisheries, reclamation for harboring and irrigation purposes. In this paper an attempt is made to study the changes in land use and land cover in Surajpur wetland area over 11 years’ period (2003-2014). LULC is an important component in understanding the interactions of the human activities with the environment and thus it is necessary to be able to simulate changes. The land cover mapping of study area was attempted using remotely sensed images of Landsat and Google Earth imagery. The study area was classified into five categories on the basis of field study, geographical conditions, and remote sensing data. LULC changes have been detected by image processing method in EDRAS imagine 2014 and ArcGIS 10.3. The eleven years’ time period of 2003-2014 shows the major type of land use change. Vegetation area that occupied about around 60 per cent of the Surajpur wetland area in 2003 has decreased to 34.25 percent in 2014. Wetland is increased 8.17 percent and Urban area, Fallow land and Water body also have experienced change. Finally, through the work it is recommended that the wetlands need detail mapping through the use of advance remote sensing techniques like microwave and LIDAR for restoration and management of wetland.
Keywords: LULC, ArcGIS, Surajpur, ERDAS, Remote Sensing
Application of Remote Sensing Techniques for Change Detection in Land Use/ La...iosrjce
IOSR Journal of Applied Geology and Geophysics (IOSR-JAGG) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of Applied Geology and Geophysics. The journal welcomes publications of high quality papers on theoretical developments and practical applications in Applied Geology and Geophysics. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
As part of the GSP’s capacity development and improvement programme, FAO/GSP have organised a one week training in Izmir, Turkey. The main goal of the training was to increase the capacity of Turkey on digital soil mapping, new approaches on data collection, data processing and modelling of soil organic carbon. This 5 day training is titled ‘’Training on Digital Soil Organic Carbon Mapping’’ was held in IARTC - International Agricultural Research and Education Center in Menemen, Izmir on 20-25 August, 2017.
LAND USE /LAND COVER CLASSIFICATION AND CHANGE DETECTION USING GEOGRAPHICAL I...IAEME Publication
Land use and land cover change has become a central component in current strategies for managing natural resources and monitoring environmental changes. Geographical information system and image processing techniques used for the analysis of land use/land cover and change detection of Sukhana Basin of Aurangabad District, Maharashtra state. The tools used ArcGIS10.1 and ERDAS IMAGINE9.1, landsat images of 1996, 2003and 2014. From land use / land cover change detection it is found that during 1996-2014, water bodies cover have loss of 4 Sq. Km. Barren land have 146 Sq.Km. loss and forest area with 96 Sq.Km. loss. It is found that urbanization area has gain of 51 Sq.Km. and agricultural land cover also have gain of 195 Sq.Km.
Change detection analysis in land use / land cover of Pune city using remotel...Nitin Mundhe
Lecture delivered in the National Conference entitled “Monitoring Degraded Lands” jointly organized by Agasti Arts, Commerce and Dadasaheb Rupwate Science
College, Akole and Maharashtra Bhugolshastra Parishad Pune to be held on 4 to 6 February 2014.
Hierarchical Self-Organizing Networks are used to reveal the topology and structure of datasets. Those structures create crisp partitions of the dataset producing branches or prototype vectors that represent groups of data with similar characteristics. However, when observations can be represented by several prototypes with similar accuracy, crisp partitions are forced to classify it in just one group, so crisp divisions usually lose information about the real dataset structure. To deal with this challenge we propose the Fuzzy Growing Hierarchical Self-Organizing Networks (FGHSON). FGHSON are adaptive networks which are able to reflect the underlying structure of the dataset, in a hierarchical fuzzy way. These networks grow by using three variables which govern the membership degree of data observations to its prototype vectors and the quality of the network representation. The resulting structure allows to represent heterogeneous groups and those that present similar membership degree to several clusters
The climate and earth sciences have recently undergone a rapid transformation from a data-poor
to a data-rich environment. In particular, massive amount of data about Earth and its
environment is now continuously being generated by a large number of Earth observing satellites
as well as physics-based earth system models running on large-scale computational platforms.
These massive and information-rich datasets offer huge potential for understanding how the
Earth's climate and ecosystem have been changing and how they are being impacted by humans’
actions. This talk will discuss various challenges involved in analyzing these massive data sets
as well as opportunities they present for both advancing machine learning as well as the science
of climate change in the context of monitoring the state of the tropical forests and surface water
on a global scale.
The presentation was given by Mr. Bas Kempen & Ms. V.L. Mulder, ISRIC, during the GSOC Mapping Global Training hosted by ISRIC - World Soil Information, 6 - 23 June 2017, Wageningen (The Netherlands).
This interdisciplinary tutorial was presented at the 2017 IEEE International Conference on Data Engineering in San Diego.
Reference:
Andreas Züfle, Goce Trajcevski, Dieter Pfoser, Matthew T. Rice, Matthias Renz, Timothy Leslie, Paul Delamater and Tobias Emrich. Handling Uncertainty in Geo-Spatial Data. 33rd International Conference on Data Engineering (ICDE). 2017.
In this study various techniques for exploratory spatial data analysis are reviewed : spatial autocorrelation, Moran's I statistic, hot spots analysis, spatial lag and spatial error models.
Self-organzing maps in Earth Observation Data Cube AnalysisLorena Santos
Earth Observation (EO) Data Cubes infrastructures model
analysis-ready data generated from remote sensing images as multidimensional cubes (space, time and properties), especially for satellite image time series analysis. These infrastructures take advantage of big data technologies and methods to store, process and analyze the big amount of Earth observation satellite images freely available nowadays. Recently, EO Data Cubes infrastructures and satellite image time series analysis
have brought new opportunities and challenges for the Land Use and Cover Change (LUCC) monitoring over large areas. LUCC have caused a great impact on tropical ecosystems, increasing global greenhouse gases emissions and reducing the planet’s biodiversity. This paper presents the
utility of Self-Organizing Maps (SOM) neural network method in the
process to extract LUCC information from EO Data Cubes infrastructures, using image time series analysis. Most classification techniques to create LUCC maps from satellite image time series are based on supervised learning methods. In this context, SOM is used as a method to assess land use and cover samples and to evaluate which spectral bands and vegetation indexes are best suitable for the separability of land use and cover classes. A case study is described in this work and shows the potential of SOM in this application
John McGaughey, CEO/President of Mira Geoscience offers his thoughts and the practices of integrated geophysical interpretation at the 3D Interest Group
Presentation introduces the concept of Climate Scenarios and Analogues. This was during a training held in Nairobi in late 2013. Presenters were David Arango and Edward Jones who work for CCAFS - CIAT. Find out more about the work of CCAFS in East Africa: http://ccafs.cgiar.org/regions/east-africa
CLUSTER DETECTION SCHEMES IN SPATIO TEMPORAL NETWORKSIJDKP
A spatiotemporal challenge can be portrayed as an inquiry that has no short of what one spatial and one
momentary property. The spatial properties are region and geometry of the inquiry. The transient property
is timestamp or time interval for which the challenge is real. The spatio fleeting inquiry as a general rule
contains spatial, common and topical or non-spatial properties. Instances of such inquiries are moving
auto, forest fire, and earth shake. Spatiotemporal educational accumulations essentially find changing
estimations of spatial and topical attributes over a time allotment. Spatio transient bunching is a procedure
of collection articles in view of their spatial and worldly similitude. It is generally new sub-field of
information mining which increased high notoriety particularly in geographic data sciences because of the
inescapability of a wide range of area based or ecological gadgets that record position, time or/and
natural properties of a protest or set of articles progressively. As a result, distinctive sorts and a lot of
spatio-transient information got to be distinctly accessible that acquaint new difficulties with information
examination and require novel ways to deal with learning revelation.
Similar to Bio inspired computational techniques applied to the analysis and visualization of spatio-temporal cluster dynamics (20)
Bio-inspired techniques and their application to precision agriculture (Andre...askroll
"Bio-inspired techniques and their application to precision agriculture"
Bio-inspired computational techniques
capable of producing complex models
to predict/describe the site-specific
behavior of given crops
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Bio inspired computational techniques applied to the analysis and visualization of spatio-temporal cluster dynamics
1. Bio-inspired computational techniques applied to the analysis and visualization of spatio-temporal cluster dynamics Miguel Arturo Barreto Sánz [email_address] Faculté des Hautes Etudes Commerciales (HEC) Institut des Systèmes d'information (ISI)
2. Outline ● Introduction Data mining in spatio-temporal datasets ● Research plan Specific Goals Challenges in mining spatio-temporal datasets State of the art Approaches ● Preliminary results and discussion 1
3. Introduction 2 ● Increasing number of complex data sets associated to geographical areas ● Routinely capture huge volumes of data describing several human or nature behaviors For instance :
4. Information sources 3 Information received from remote sensing systems, and environmental monitoring devices used in: ● Agriculture ● Weather prediction ● Cartography Introduction
5. 4 These data sets are critical for decision support , but their value depends on the ability to extract useful information for studying and understanding the phenomena governing the data source. Introduction Data mining in spatio-temporal datasets
6. 5 Currently ● Data mining in geospatial data take just the static view of geospatial phenomena . However ● Geographic phenomena evolve over time ● Mining spatio-temporal data is related to the temporal dynamics of geospatial data = crucial to our understanding of geographic-based process and events. Goal ● Describe the manner in which spatial patterns change through time Introduction Data mining in spatio-temporal datasets
7. Data mining in spatio-temporal datasets 6 Introduction Some fields and applications include: ● Agro-ecology ● Environmental change ● Species distribution ● Disease propagation ● Urban dynamics ● Migration patterns
8. 1 Introduction Data mining in spatio-temporal datasets Manage and understand changing spatial patterns of yields ● What are the variables that make that some regions produce more that the others ? ● Why are regions that maintain its production over time ? 7
9. 8 The Normalized Difference Vegetation Index ( NDVI ) gives a measure of the vegetative cover on the land surface over wide areas. ● What variables are related with the changes in the vegetative cover ? Introduction Data mining in spatio-temporal datasets Environmental Change (Satellite images) Summer 1989 Summer 1990 Summer 1991 Summer 1992 Sumer 1993 Summer 1994 Summer 1996 Summer 1997 Summer 1998 Summer 1999 Summer 2000 Summer 2001
10. 9 It is very important to conduct research on data mining of spatio-temporal datasets . ● Develop methodologies ● Assist the knowledge extraction from spatio-temporal datasets ● Improving making decision processes. Introduction Data mining in spatio-temporal datasets New methodologies
11. 10 To deal with the inherent characteristics of the spatio-temporal datasets ● Multivariate and Temporal Mapping ● Visualization of Very Large Datasets ● Changing spatial patterns Introduction Data mining in spatio-temporal datasets New methodologies For instance … New methodologies to mining spatio-temporal datasets Visualization of spatio-temporal cluster dynamics To provide insights about the nature of cluster change
12. Introduction Data mining in spatio-temporal datasets New methodologies Similarity of sugarcane growing environmental conditions (1999-2001) using Self-organizing maps 11
13. 12 Introduction Data mining in spatio-temporal datasets New methodologies ● Which is the variable or variables that make that two clusters merge in one. ● There are sites that change from one cluster to another year after year? ● Why that happens?. ● It is possible to find recurrent patterns in the dynamics of the clusters?
14. Specific Goals 13 Development of bio-inspired methodologies for the detection and tracking of changes in spatio-temporal clusters. ● Agro-ecological datasets will be used as a case study. ● This approach implies to find clusters of sites with similar characteristics in time and space. Development of bio-inspired methodologies for the visualization of spatio-temporal cluster dynamics. Research plan
15. Clusters of sites with similar characteristics in time and space 14 Research plan Specific Goals What crops or varieties are likely to perform well where and when . Soil Homologues places for Colombian coffee production. Brazil, Equator, East Africa, and New Guinea. Climate Genotype
16. Clusters of sites with similar characteristics in time and space 15 Research plan Specific Goals Harvest at different time of the same crop
17. Clusters of sites with similar characteristics in time and space 16 Research plan Specific Goals For commercial (mass production) crops (rice, corn) it is known the “when” and “where” For native crops (guanabana, lulo) or special types of crops (coffee varieties) it is not the case. DAPA (Diversification Agriculture Project Alliance) When and what I must cultivate ? Market demand The COCH project
18. Challenges in mining spatio-temporal datasets 17 Research plan The special nature of spatio-temporal data poses several challenges to the knowledge extraction process. For instance: ● Heterogeneity in sources of information and in scales of time and space ● Spatial autocorrelation ● Boundaries in geospatial data ● Temporal relationships between spatial objects ● Visualization of spatio-temporal cluster dynamics ● Geographic space and feature space
19. 18 Research plan Challenges in mining spatio-temporal datasets Conventional methods are not effective for handling mixture of data types and sources. Heterogeneity in sources of information
20. 19 Research plan Challenges in mining spatio-temporal datasets Heterogeneity in scales of time and space Necessary to have methodologies to evaluate clusters at different scales in order to find “interesting” patterns between levels. Improve the analysis of cluster structure at different scales, creating representations of the cluster facilitating the selection of clusters at different scales.
21. 20 Research plan Challenges in mining spatio-temporal datasets Spatial autocorrelation The spatial autocorrelation can be defined as the degree of relationship that exists between two or more spatial-data variables
22. 21 Research plan Challenges in mining spatio-temporal datasets Boundaries in geospatial data Algorithms for knowledge discovery in spatio-temporal databases have to consider the neighbors of the geo-referenced data. For instance, part of the complexity of the problem lies in the fact that the boundaries of these neighbors are not hard, but rather soft boundaries .
23. Research plan Challenges in mining spatio-temporal datasets The relationship between spatial objects can change over time. This dynamic relationships can be observed for instance in the cluster changes over the time. Temporal relationships between spatial objects 22 Similarity of sugarcane growing environmental conditions (1999-2001) using Self-organizing maps
24. Research plan Challenges in mining spatio-temporal datasets Geographic space and feature space Geographic space Feature space Geographic space is concerned with surface features as the terrain we walk on. Feature space visualization is concerned with the representation of similarities associated with geo-referenced sites in the geographic space 23
25. Research plan Challenges in mining spatio-temporal datasets Visualization of spatio-temporal cluster dynamics ● Visualization of the overall structure of the dataset, ● Exploration of correlations and relationships. ● Visualization of temporal patterns. 24 1 336,025 points just for Colombia 1 Km 1 Km 1 point
26. State of the art Research plan Myra Spiliopoulou, et al. Monic: modeling and monitoring cluster transitions . In KDD ’06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining. Daniel B. Neill et al. Detection of emerging space-time clusters . In KDD ’05: Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining. Geoffrey M. Jacquez. Spatial Cluster Analysis (The Handbook of Geographic Information Science). John Wilson (University of Southern California), 2008 ● Small databases ● No agro-ecologic or environmental databases ● Recorded in controlled conditions ● Based on statistical models 25
27. Approaches Research plan Used to analyze data when there is only a low level of knowledge about the dataset ● Unsupervised learning Heterogeneous data ● Hierarchical methods Heterogeneity in scales of time and space 26
28. Approaches Research plan ● Data abstraction methods Heterogeneity in scales of time and space 27 Examples Prototype Examples Prototype
29. Approaches Research plan A Self-Organizing Map ( SOM) applies a learning strategy used in neural structures like the cortex, and presents several advantages that we will exploit in our research in order to gain insights about the spatial autocorrelation present in the geographic zones. The neighbourhood function hck ( t ) of a SOM, centred over the best matched neuron mc . ● Self-Organizing Map ( SOM) Spatial autocorrelation 28
30. Approaches Research plan 29 Similarity of sugarcane growing environmental conditions (1999-2005) using Self-organizing maps The clusters found in the feature space in many cases are not the same as those found in geographic space. Represent clusters of a multidimensional space: map multidimensional data onto a two-dimensional lattice of cells. ● Self-Organizing Map ( SOM) Geographic space and feature space
31. Approaches Research plan ● Self-Organizing Map ( SOM) Visualization of spatio temporal cluster dynamics Visualization of the overall structure of the dataset , it is clustering, patterns (similarities) and irregularities. Exploration of correlations and relationships . This is primarily based on component plane displays in multiple views. Visualization of temporal patterns . Examples are ordered component displays and trajectories. 30 Partial Correlation
32. Approaches Research plan In many applications crisp partitions are not the optimal representation of clusters. With the purpose of representing degrees of membership, is a feature that could be added to the model. ● Fuzzy logic Boundaries in geospatial data 31
33. Approaches Research plan To deal with non stationary-relationships implies to find relationships which varies through time and space . This challenge involves the creation of methodologies capable to adapt their models in order to reveal the dynamics of the clusters and represent their characteristics in the most accurate manner. Growing hierarchical Self-Organizing Structures could be used as a base for hybrid models in order to detect, reveal and analyze spatio-temporal cluster dynamics. ● Non-stationarity relationships between spatial objects Growing hierarchical Self-Organizing Structures 32
34. I propose ... Research plan Approaches An unsupervised model based on self-organization which allows data abstraction, hierarchical organization of the clusters , and automatic detection of interesting changes in the dynamics of spatio-temporal clusters. Some characteristics of the model must be: ● Adapt its structure . ● Changes presented in its structure will reveal cluster dynamics as merging, emergence, mutation, and parallel dynamics. 33
35. I propose ... Research plan Approaches ● The hierarchical structure will permit to tackle the problem related to the scale effect (navigation of the clustering structure in different levels). ● The model will work with fuzzy memberships to avoid the problem of boundaries in geospatial data. ● The unsupervised methodology will help to find relationships that can be hidden in very large and heterogeneous datasets ( Heterogeneity in sources of information ). 34
36. Preliminary results and discussion [1] Miguel Barreto-Sanz. and Andrés Pérez-Uribe. Classification of similar productivity zones in the sugar cane culture using clustering of som component planes based on the som distance matrix. In The 6th International Workshop on Self-Organizing Maps (WSOM), 2007. [2] Miguel Barreto-Sanz. and Andrés Pérez-Uribe. Improving the correlation hunting in a large quantity of som component planes. In ICANN 2007. Proceedings of the 1th international conference on Artificial Neural Networks. [3] Miguel Barreto-Sanz and Andrés Pérez-Uribe. Tree-structured self-organizing map component planes as a visualization tool for data exploration in agro-ecological modeling. In in Proc. of the 6th European Conf. on Ecological Modelling, Trieste, Italy, 2007 35
37. Preliminary results and discussion [4] Miguel Barreto-Sanz, Andrés Pérez-Uribe, Carlos-Andres Peña-Reyes, and Marco Tomassini. Fuzzy growing hierarchical self organizing networks . In ICANN 2008: Proceedings of the 18th international conference on Artificial Neural Networks. [5] Miguel Barreto-Sanz, Andrés Pérez-Uribe, Carlos-Andres Peña-Reyes, and Marco Tomassini. Tuning Parameters in the Fuzzy Growing Hierarchical Self-Organizing Networks . To appear in: Studies in Computational Intelligence, CONSTRUCTIVE NEURAL NETWORKS Springer, 2009. 36