Regional difference and dynamic mechanism of locality of the Chinese farming-pastoral ecotone based on geotagged photos from Panoramio

Cross-regional locality research reflects the influences of natural environment and the human activities due to the abundant land types and the multiple landscape combinations in related regions. The Chinese farming-pastoral ecotone is a typical large-scale region but few studies were conducted. This research contributed to the understanding of cross-regional locality of the Chinese farming-pastoral ecotone from different scales, including national, sectional, and provincial administrative units by utilizing geotagged photos（GTPs） obtained from the Panoramio website. The major results were as follows：（1） the locality elements of the Chinese farming-pastoral ecotone included 52 free nodes classified into 8 types of scene attributes;（2） there were huge differences between locality elements of different regions, and there was a negative correlation between the similarity degree of elements of different provinces and their spatial distances;（3） the Chinese farming-pastoral ecotone could be divided into the northern, central and southern sections, whose localities had differences in element constitution, association structure and the strength of elements, system stability and the anti-interference capability; and（4） the evolution of the localities of the northern and central sections was mainly influenced by human activities, while the locality of southern section retained more natural features. On a theoretical level, this research aimed to establish the research methodology of locality from the perspective of open data on the web with strong operability and replicability. On a practical level, this research could enrich the structuring recognition of the locality of the Chinese farming-pastoral ecotone and the comprehension of its dynamic mechanism. The results provide a reference for locality differentiation protection and the development of a cross-regional scale.

Building a model-based personalised recommendation approach for tourist attractions from geotagged social media data

When travelling,people are accustomed to taking and uploading photos on social media websites,which has led to the accumulation of huge numbers of geotagged photos.Combined with multisource information(e.g.weather,transportation,or textual information),these geotagged photos could help us in constructing user preference profiles at a high level of detail.Therefore,using these geotagged photos,we built a personalised recommendation system to provide attraction recommendations that match a user’s preferences.Specifically,we retrieved a geotagged photo collection from the public API for Flickr(Flickr.com)and fetched a large amount of other contextual information to rebuild a user’s travel history.We then created a model-based recommendation method with a two-stage architecture that consists of candidate generation(the matching process)and candidate ranking.In the matching process,we used a support vector machine model that was modified for multiclass classification to generate the candidate list.In addition,we used a gradient boosting regression tree to score each candidate and rerank the list.Finally,we evaluated our recommendation results with respect to accuracy and ranking ability.Compared with widely used memory-based methods,our proposed method performs significantly better in the cold-start situation and when mining‘long-tail’data.

Optimizing Vaccine Access: A Web-Based Scheduling System with Geo-Tagging Integration and Decision Support for Local Health Centers

The system created aims to produce an online vaccination appointment scheduling system with geo-tagging integration and a decision-support mechanism for neighborhood health clinics. With a decision support mechanism that suggests the essential vaccines based on their account details, it is made to meet the unique vaccination needs of each patient. The system includes immunizations that are accessible locally, and patients and midwives can manage their own corresponding information through personal accounts. Viewers of websites can visualize the distribution of vaccines by purok thanks to geotagging. The Agile Scrum Methodology was modified by the researchers for early delivery, change flexibility, and continual system improvement in order to accomplish the study’s main goal. In order to assess the system’s acceptability in terms of functional adequacy, performance efficiency, compatibility, usability, reliability, security, maintainability, and portability, it was designed in accordance with the ISO 25010 Product Software Quality Standards. Following the assessment, the system was given an average total weighted mean score of 4.62, which represents a verbal interpretation of “strongly agree”. This score demonstrates that the evaluators were in agreement that the system met the requirements of ISO 25010 for Product Software Quality Standards.

基于MGIS的GeoTIFF应用研究

剖析了GeoTIFF的坐标空间、坐标转换、存储地理信息的相关途径和6个地理标签之间的关系及含义。同时分析了MGIS转换GeoTIFF的前提条件,在此基础上首次提出了GeoTIFF在MGIS上的应用方案。最后介绍了LibGeoTIFF的结构并结合实例对应用方案进行了验证并总结了该方案的优点。

GeoTIFF格式分析及其在测量中的应用

曹凤海,易昌华,殷学涛,罗桂圆,柳安青.GeoTIFF格式分析及其在测量中的应用.物探装备,2015,25(1):52-57GeoTIFF地理信息的标签图像文件格式在物探测量等领域得到了越来越广泛地应用。本文详细地论述GeoTIFF用于存储地理信息的具体技术途径和六个地理标签的结构、含义。以DEM数据的读取和应用为例,介绍了GeoTIFF在测量中的应用,列出了以GeoTIFF格式存储的DEM数据分析以及某一点对应高程值(灰度值)提取的实现代码。

Mining coterie patterns from Instagram photo trajectories for recommending popular travel routes

Abstract Instagram is a popular photo-sharing social ap- plication. It is widely used by tourists to record their journey information such as location, time and interest. Consequently, a huge volume of get-tagged photos with spatio-temporal in- formation are generated along tourist＇s travel trajectories. Such Instagram photo trajectories consist of travel paths, travel density distributions, and traveller behaviors, prefer- ences, and mobility patterns. Mining Instagram photo trajec- tories is thus very useful for many mobile and location-based social applications, including tour guide and recommender systems. However, we have not found any work that extracts interesting group-like travel trajectories from Instagram pho- tos asynchronously taken by different tourists. Motivated by this, we propose a novel concept： coterie, which reveals representative travel trajectory patterns hidden in Instagram photos taken by users at shared locations and paths. Our work includes the discovery of （1） coteries, （2） closed co- teries, and （3） the recommendation of popular travel routes based on closed coteries. For this, we first build a statistically reliable trajectory database from Instagram get-tagged pho- tos. These trajectories are then clustered by the DBSCAN method to find tourist density. Next, we transform each raw spatio-temporal trajectory into a sequence of clusters. All dis- criminative closed coteries are further identified by a Cluster- Growth algorithm. Finally, distance-aware and conformity- aware recommendation strategies are applied on closed co- teries to recommend popular tour routes. Visualized demosand extensive experimental results demonstrate the effective- ness and efficiency of our methods.

Personalized tourist route recommendation model with a trajectory understanding via neural networks

Travel recommendations form a major part of tourism service. Traditional collaborative filtering and Markov model are not appropriate for expressing the trajectory features,for travel preferences of tourists are dynamic and affected by previous behaviors. Inspired by the success of deep learning in sequence learning,a personalized recurrent neural network (P-RecN) is proposed for tourist route recommendation. It is data-driven and adaptively learns the unknown mapping of historical trajectory input to recommended route output. Specifically,a trajectory encoding module is designed to mine the semantic information of trajectory data,and LSTM neural networks are used to capture the sequence travel patterns of tourists. In particular,a temporal attention mechanism is integrated to emphasize the main behavioral intention of tourists. We retrieve a geotagged photo dataset in Shanghai,and evaluate our model in terms of accuracy and ranking ability. Experimental results illustrated that P-RecN outperforms other baseline approaches and can effectively understand the travel patterns of tourists.

A visual-textual fused approach to automated tagging of flood-related tweets during a flood event

In recent years,social media such as Twitter have received much attention as a new data source for rapid flood awareness.The timely response and large coverage provided by citizen sensors significantly compensate the limitations of non-timely remote sensing data and spatially isolated river gauges.However,automatic extraction of flood tweets from a massive tweets pool remains a challenge.Taking the Houston Flood in 2017 as a study case,this paper presents an automated flood tweets extraction approach by mining both visual and textual information a tweet contains.A CNN architecture was designed to classify the visual content of flood pictures during the Houston Flood.A sensitivity test was then applied to extract flood-sensitive keywords that were further used to refine the CNN classified results.A duplication test was finally performed to trim the database by removing the duplicated pictures to create the flood tweets pool for the flood event.The results indicated that coupling CNN classification results with flood-sensitive words in tweets allows a significant increase in precision while keeps the recall rate in a high level.The elimination of tweets containing duplicated pictures greatly contributes to higher spatio-temporal relevance to the flood.