Local place names are frequently used by residents living in a geographic region. Such place names may not be recorded in existing gazetteers, due to their vernacular nature, relative insignificance to a gazetteer covering a large area (e.g. the entire world), recent establishment (e.g. the name of a newly-opened shopping center) or other reasons. While not always recorded, local place names play important roles in many applications, from supporting public participation in urban planning to locating victims in disaster response. In this paper, we propose a computational framework for harvesting local place names from geotagged housing advertisements. We make use of those advertisements posted on local-oriented websites, such as Craigslist, where local place names are often mentioned. The proposed framework consists of two stages: natural language processing (NLP) and geospatial clustering. The NLP stage examines the textual content of housing advertisements and extracts place name candidates. The geospatial stage focuses on the coordinates associated with the extracted place name candidates and performs multiscale geospatial clustering to filter out the non-place names. We evaluate our framework by comparing its performance with those of six baselines. We also compare our result with four existing gazetteers to demonstrate the not-yet-recorded local place names discovered by our framework. 相似文献
In many arid ecosystems, vegetation frequently occurs in high-cover patches interspersed in a matrix of low plant cover. However, theoretical explanations for shrub patch pattern dynamics along climate gradients remain unclear on a large scale. This context aimed to assess the variance of the Reaumuria soongorica patch structure along the precipitation gradient and the factors that affect patch structure formation in the middle and lower Heihe River Basin (HRB). Field investigations on vegetation patterns and heterogeneity in soil properties were conducted during 2014 and 2015. The results showed that patch height, size and plant-to-patch distance were smaller in high precipitation habitats than in low precipitation sites. Climate, soil and vegetation explained 82.5% of the variance in patch structure. Spatially, R. soongorica shifted from a clumped to a random pattern on the landscape towards the MAP gradient, and heterogeneity in the surface soil properties (the ratio of biological soil crust (BSC) to bare gravels (BG)) determined the R. soongorica population distribution pattern in the middle and lower HRB. A conceptual model, which integrated water availability and plant facilitation and competition effects, was revealed that R. soongorica changed from a flexible water use strategy in high precipitation regions to a consistent water use strategy in low precipitation areas. Our study provides a comprehensive quantification of the variance in shrub patch structure along a precipitation gradient and may improve our understanding of vegetation pattern dynamics in the Gobi Desert under future climate change.
Water quality is often highly variable both in space and time, which poses challenges for modelling the more extreme concentrations. This study developed an alternative approach to predicting water quality quantiles at individual locations. We focused on river water quality data that were collected over 25 years, at 102 catchments across the State of Victoria, Australia. We analysed and modelled spatial patterns of the 10th, 25th, 50th, 75th and 90th percentiles of the concentrations of sediments, nutrients and salt, with six common constituents: total suspended solids (TSS), total phosphorus (TP), filterable reactive phosphorus (FRP), total Kjeldahl nitrogen (TKN), nitrate-nitrite (NOx), and electrical conductivity (EC). To predict the spatial variation of each quantile for each constituent, we developed statistical regression models and exhaustively searched through 50 catchment characteristics to identify the best set of predictors for that quantile. The models predict the spatial variation in individual quantiles of TSS, TKN and EC well (66%–96% spatial variation explained), while those for TP, FRP and NOx have lower performance (37%–73% spatial variation explained). The most common factors that influence the spatial variations of the different constituents and quantiles are: annual temperature, percentage of cropping land area in catchment and channel slope. The statistical models developed can be used to predict how low- and high-concentration quantiles change with landscape characteristics, and thus provide a useful tool for catchment managers to inform planning and policy making with changing climate and land use conditions. 相似文献