Spatial hotspot detection using polygon propagation

Katragadda, Satya; Chen, Jian; Abbady, Shaaban

doi:10.1080/17538947.2018.1485754

Cited by 2 publications

(1 citation statement)

References 33 publications

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“…[9] developed an efficient method for POI/ROI discovery using Flickr Geotagged Photos; Ref. [31] proposed an approach to spatial hotspot detection for a point data set with no geographical boundary information; Ref. [8] proposed a computational framework for harvesting local place names from geotagged housing advertisements.…”

Section: Related Workmentioning

confidence: 99%

Spatial Context-Based Local Toponym Extraction and Chinese Textual Address Segmentation from Urban POI Data

Kuai

Guo

Zhang

et al. 2020

IJGI

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Georeferencing by place names (known as toponyms) is the most common way of associating textual information with geographic locations. While computers use numeric coordinates (such as longitude-latitude pairs) to represent places, people generally refer to places via their toponyms. Query by toponym is an effective way to find information about a geographic area. However, segmenting and parsing textual addresses to extract local toponyms is a difficult task in the geocoding field, especially in China. In this paper, a local spatial context-based framework is proposed to extract local toponyms and segment Chinese textual addresses. We collect urban points of interest (POIs) as an input data source; in this dataset, the textual address and geospatial position coordinates correspond at a one-to-one basis and can be easily used to explore the spatial distribution of local toponyms. The proposed framework involves two steps: address element identification and local toponym extraction. The first step identifies as many address element candidates as possible from a continuous string of textual addresses for each urban POI. The second step focuses on merging neighboring candidate pairs into local toponyms. A series of experiments are conducted to determine the thresholds for local toponym extraction based on precision-recall curves. Finally, we evaluate our framework by comparing its performance with three well-known Chinese word segmentation models. The comparative experimental results demonstrate that our framework achieves a better performance than do other models.

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Section: Related Workmentioning

confidence: 99%