Zoonotic cutaneous leishmaniasis (ZCL), a vector-borne disease, poses serious psychological as well as social and economic burden to many rural areas of Iran. The main objectives of this study were to analyse yearly spatial distribution and the possible spatial and spatio-temporal clusters of the disease to better understand spatio-temporal epidemiological aspects of ZCL in rural areas of an endemic province, located in north-east of Iran. Cross-sectional survey was performed on 2983 recorded cases during the period of 2010-2012 at village level throughout the study area. Global clustering methods including the average nearest-neighbour distance, Moran's I, general G indices and Ripley's K-function were applied to investigate the annual spatial distribution of the existing point patterns. Presence of spatial and spatio-temporal clusters was investigated using the spatial and space-time scan statistics. For each year, semivariogram analysis and all global clustering methods indicated meaningful persistent spatial autocorrelation and highly clustered distribution of ZCL, respectively. Eight significant spatial clusters, mainly located in north and northeast of the province, and one space-time cluster, observed in northern part of the province and during the period of September 2010-November 2010, were detected. Comparison of the location of ZCL clusters with environmental conditions of the study area showed that 97.8% of cases in clusters were located at low altitudes below 725 m above sea level with predominantly arid and semi-arid climates and poor socio-economic conditions. The identified clusters highlight high-risk areas requiring special plans and resources for more close monitoring and control of the disease.

Volunteered geographic information (VGI) is spatial data that has been contributed by numerous volunteers in the form of user‐generated content. VGI provides the ability for non‐experts to collect and share geographic information. Comparison of a VGI dataset with a reference dataset in order to find data correspondence is a common process in some analyses of VGI data, such as quantitative analysis of its quality. This study addresses this case and presents an automated feature matching method for VGI linear data. The proposed method consists of two main steps. In the first step, a feature point matching method based on a novel descriptor, named Location‐Orientation Rotary Descriptor (LORD), is performed. The proposed LORD descriptor uses the location and orientation information of the linear data in a log polar structure and in a rotary manner. In the second step, a new segment matching strategy based on the buffered linear features is performed to reject the outliers of the previous step. The usefulness of the proposed method was demonstrated by applying it to the OpenStreetMap (OSM) dataset, as a VGI dataset, using an official reference dataset.

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The main purpose of any facility location is to select the optimal places that satisfy project's goals. In location problems, the object is usually to optimize a function -objective function -that defines the problem conditions and efficient decision parameters. Numerous methods are proposed to challenge the location facility issues. In this paper, we consider a class of location/allocation problem that can assume more realistic conditions in real-life applications. This problem is an extension to the well-known capacitated multi source Weber problem. A new method that uses two genetic algorithms is used to solve the problem efficiently. The first, External GA, solves the location problem while the second, Internal GA, solves the allocation problem. A case study was designed to assess the feasibility of the proposed solution. The results indicate that the new approach is optimal, efficient and successful.

Trajectory similarity measurement is a vital and widely used step in many applications, including recommendation systems. In the discipline of similarity measurement, research has mostly been focused on raw trajectories, consisting of location and time‐stamp information. Due to the explosion in the use of the internet and location‐based social networks, raw trajectories can be easily enriched with semantic information. Nevertheless, few attempts have been made to apply semantic and location information during similarity measurement. In light of this, we present a new similarity measure called the enhanced maximal travel match (EMTM) in this article. The proposed EMTM improves on conventional maximal travel match (MTM) methods by simultaneously considering the location and place category as the most basic semantic information. EMTM first generates a new place category‐location hierarchical framework (CLHF) for each trajectory. Subsequently, it identifies MTMs at each layer of hierarchy to explore the similarity between each pair of CLHFs. Finally, the proposed method calculates similarity scores based on the identified MTMs. In experiments on a Foursquare data set, EMTM outperformed the conventional MTM methods by more than 50% in terms of mean average precision. Additionally, EMTM was the most accurate technique among four state‐of‐the‐art methods.