Detecting events from the social media through exemplar-enhanced supervised learning

ABSTRACT

Understanding and detecting the intended meaning in social media is challenging because social media messages contain varieties of noise and chaos that are irrelevant to the themes of interests. For example, conventional supervised classification approaches would produce inconsistent solutions to detecting and clarifying whether any given Twitter message is really about a wildfire event. Consequently, a renovated workflow was designed and implemented. The workflow consists of four sequential procedures: (1) Apply the latent semantic analysis and cosine similarity calculation to examine the similarity between Twitter messages; (2) Apply Affinity Propagation to identify exemplars of Twitter messages; (3) Apply the cosine similarity calculation again to automatically match the exemplars to known training results, and (4) Apply accumulative exemplars to classify Twitter messages using a support vector machine approach. The overall correction ratio was over 90% when a series of ongoing and historical wildfire events were examined.     

Cartographic Industry Profiles

Abstract

Analysis of eye movements has been used for decades as a method for assessing the performance of visual stimuli. Until recently, this has mainly been applied to static and non-cartographic stimuli, but due to technological developments and reduced cost of equipment, interactive and cartographic applications are now feasible. suggest a new analysis method which applies Hägerstrand’s Space-Time-Cube (STC; ) to eye movement data. However, in an interactive three-dimensional STC, identifying and exploring key behaviours can be difficult. In order to ameliorate these difficulties, we propose a variation of the STC method, which uses two-dimensional projections of the STC onto the XT and YT planes. These two-dimensional projections are found to facilitate rapid identification of significant patterns in the data set. A prototype implementing this and other dynamical methods has been developed, and is presented with examples illustrating the benefits of working with two-dimensional projections of the STC.     

基于多核学习的高分辨率遥感图像目标检测方法

为更有效地实现复杂场景中的多类目标同时检测,本文提出了一种基于多核学习算法进行目标检测的框架。该方法由特征提取和模型训练2个阶段组成。特征提取阶段,引入了多尺度下的点特征、表观特征同时对多类目标进行综合描述;模型训练阶段,分别采用加权相加和相乘2种方法将提取的各个基础特征组合起来,在支持向量机的框架下对各特征所代表的基础核权重进行学习。将训练所得的分类器结合滑动窗搜索技术对遥感图像进行目标检测实验,结果表明,与传统单核支持向量机相比,准确率更高。     

Dislocation Pedagogy

This article describes an experiment in learning with a course on “research and philosophies of place and space” in which students were repeatedly dislocated from an environment familiar to them and replaced in key locations about the city with local place experts as their guide. The article begins with a review of the meaning of place and the opportunities offered by the City of Kingston for reflective engagement. It briefly hints at some of the social trends devaluing place learning and the consequently political nature of this pedagogy. Finally, it summarizes the lessons learned in five of the ten classes.     

Learning Wisdom Through Geographical Dislocations

Wisdom is at once one of the most elusive and most valued kinds of knowledge. Empirical research shows that, indeed, across cultures, people hope that life experience will eventually make them wiser. The problem is that, to date, the academic study of wisdom and of the processes by which it can be learned has been dominated by psychologists. The first part of the article reviews the state-of-the-art psychological scholarship on wisdom to show how that conceptualization lacks geographical sensitivity and therefore misses some of the crucial geographical mechanisms by which people become wiser. The second part of the article singles out and focuses on one such mechanism, namely, the learning of wisdom through geographical dislocations. By drawing on insights from the study of international migration, exile, and transculturation in postcolonial contexts, the final part of the article suggests specific learning processes that might strengthen the hypothesis that geographical dislocations and the attendant cross-cultural experiences they generate are often conducive to wisdom.     

Promoting Local Growth: Process,Practice, and Policy

Electromap World Atlas, Version 1.1.

Interactive Population Statistical System, Version 1.0. Jerry W. Wicks and Jose Luiz Pereira de Almeida

MATHCAD, Version 2.5.

Memory Mate.

Micro DEM+, Version 5.21. Peter Guth

NCSS – Number Cruncher Statistical System, Version 5.02. Dr. Jerry L. Hintze     

Predicting potential distributions of geographic events using one-class data: concepts and methods

One common problem with geographic data is that, for a specific geographic event, only occurrence information is available; information about the absence of the event is not available. We refer to these specific types of geospatial data as geographic one-class data (GOCD). Predicting the potential spatial distributions that a particular geographic event may occur from GOCD is difficult because traditional binary classification methods that require availability of both positive and negative training samples cannot be used. The objective of this research is to define GOCD and propose novel approaches for modelling potential spatial distributions of geographic events using GOCD. We investigate the effectiveness of one-class support vector machine (OCSVM), maximum entropy (MAXENT) and the newly proposed positive and unlabelled learning (PUL) algorithm for solving GOCD problems using a case study: species distribution modelling from synthetic data. Our experimental results indicate that generally OCSVM, MAXENT and PUL are effective in modelling the GOCD. Each method has advantages and disadvantages, but PUL seems to be the most promising method.     

Mining urban land-use patterns from volunteered geographic information by means of genetic algorithms and artificial neural networks

In the context of OpenStreetMap (OSM), spatial data quality, in particular completeness, is an essential aspect of its fitness for use in specific applications, such as planning tasks. To mitigate the effect of completeness errors in OSM, this study proposes a methodological framework for predicting by means of OSM urban areas in Europe that are currently not mapped or only partially mapped. For this purpose, a machine learning approach consisting of artificial neural networks and genetic algorithms is applied. Under the premise of existing OSM data, the model estimates missing urban areas with an overall squared correlation coefficient (R ²) of 0.589. Interregional comparisons of European regions confirm spatial heterogeneity in the model performance, whereas the R ² ranges from 0.129 up to 0.789. These results show that the delineation of urban areas by means of the presented methodology depends strongly on location.     

LSTM神经网络在地震资料初至拾取中的应用

本文引入RNN的升级算法LSTM神经网络技术,建立了一套海量数据、高精度的自动拾取地震资料初至流程。相比于其他神经网络方法,LSTM神经网络能够提取数据的时序特征,加强学习初至前噪音时序特征,从而提高初至拾取的精度,为地震资料的初至拾取提供一套新的思路。首先设计样本制作过程并建立、训练模型,通过模拟资料验证方法的有效性,应用于胜利油田浅海与西部山地地震勘探资料的初至拾取,取得理想效果,证明LSTM神经网络初至拾取具有较高的精度与适用性。     

Multiple model combination methods for annual maximum water level prediction during river ice breakup

The Athabasca River is the largest unregulated river in Alberta, Canada, with ice jams frequently occurring in the vicinity of Fort McMurray. Modelling tools are desired to forecast ice‐related flood events. Multiple model combination methods can often obtain better predictive performances than any member models due to possible variance reduction of forecast errors or correction of biases. However, few applications of this method to river ice forecasting are reported. Thus, a framework of multiple model combination methods for maximum breakup water level (MBWL) Prediction during river ice breakup is proposed. Within the framework, the member models describe the relations between the MBWL (predicted variable) and their corresponding indicators (predictor variables); the combining models link the relations between the predicted MBWL by each member model and the observed MBWL. Especially, adaptive neuro‐fuzzy inference systems, artificial neural networks, and multiple linear regression are not only employed as member models but also as combining models. Simple average methods (SAM) are selected as the basic combining model due to simple calculations. In the SAM, an equal weight (1/n) is assigned to n member models. The historical breakup data of the Athabasca River at Fort McMurray for the past 36 years (1980 to 2015) are collected to facilitate the comparison of models. These models are examined using the leave‐one‐out cross validation and the holdout validation methods. A SAM, which is the average output from three optimal member models, is selected as the best model as it has the optimal validation performance (lowest average squared errors). In terms of lowest average squared errors, the SAM improves upon the optimal artificial neural networks, adaptive neuro‐fuzzy inference systems, and multiple linear regression member models by 21.95%, 30.97%, and 24.03%, respectively. This result sheds light on the effectiveness of combining different forecasting models when a scarce river ice data set is investigated. The indicators included in the SAM may indicate that the MBWL is affected by water flow conditions just after freeze‐up, overall freezing conditions during winter, and snowpack conditions before breakup.