Identifying spatial concentrations of surnames

Surnames (family names) have been overlooked as a valuable source of spatially referenced population data. Presented here is a methodology, based on kernel density estimation, which is used to identify the areas of Great Britain where any surname is most concentrated. This not only indicates a surname's geographic origin in the country but also its current spatial extent and spatial relationship with other surnames and place names. We argue that such analysis can provide baseline and change measures, and an empirical basis to change forecasting. Such analysis offers valuable insights into national, regional and local changes in population structure, and testimony to the relevance of GIScience to population genetics, historical geography and genealogy.     

长三角地区地名通名文化景观空间格局及影响因素北大核心CSCDCSSCI

地名通名是不同地方地名具有共性特点的通用部分,表征人类社会对自然环境和行政区划变迁的认知。梳理多学科视域研究进展诠释地名通名文化景观解析逻辑,基于EOF模型、地理探测器等方法构建地名通名文化景观解析模型,探究长三角地区地名通名文化景观格局及成因。研究发现:①长三角地名通名文化景观用字比例受河网水系影响最大,地名通名文化景观用字类型丰富度在41个单元中总体呈现内陆到沿海且沿东北-西南方向递减趋势。②长三角地名通名文化景观分布异质性显著,呈现典型非均衡集聚,空间上形成皖中、苏南、浙北和上海等东西条带状的一级中心位于长三角中部,两个团块状次级中心分列南北;长三角地名通名文化景观分布呈显著空间正相关,空间集聚特征较为明显,热点区为双中心格局且分布于安徽西部和浙江南部等地,冷点区为单中心格局且分布在东部沿海呈纵向连续带状分布。③长三角地名通名文化景观分布受自然环境和社会经济综合影响明显,主要分布在海拔0~200 m、地形起伏度小于20 m、坡度小于5°的平原地带,人口集聚、经济发展水平和交通便捷性对地名通名传承影响深远。地名通名文化景观解析模型研究有助于提升地名命名科学性和规划管理协调度,为推动地名文化遗产保护提供有益参考。     

重建地方：人文地理与GIS结合研究的路径

地方是很重要的地理学概念。中外学界对地方的研究有丰富积累。围绕地方的理论与现实问题,探索人文地理与GIS结合研究的路径和方法对中国地理学尤为重要。“重建地方”旨在打破学科界限,重新发现学术研究的“地方”,并将学术与生活、思想与技术紧密结合起来,反思和重建生活中的“地方”。人文地理与GIS结合研究主要聚焦于流动性的大尺度城市空间与稳定性的小尺度社区空间。通过运用开放的街景地图、3D-VQGIS等新GIS工具和方法,整合大数据与小数据,将定量与定性结合,丰富了地方性研究的方法与理论,使人文地理与GIS结合有新的方法论与可操作的手段。以地方为主题,人文地理与GIS的结合还将重塑这2个子学科,进而促进地理学发展。未来应该大力探索人文地理与GIS结合的思想、理论与方法,实现学科内的交融互动,并将其应用于解释尺度交错的现实问题,进而在理论和实践上重建地方。     

Enriching user profiles using geo-social place semantics in geo-folksonomies

Geo-folksonomies link social web users to geographic places through the tags users choose to label the places with. These tags can be a valuable source of information about the user’s perception of place and can reflect their experiences and activities in the places they label. By analysing the associations between users, places and tags, an understanding of a place and its relationships with other places can be drawn. This place characterisation is unique, dynamic and reflects the perception of a particular user community that generated the geo-folksonomy. In this work, an approach is proposed to analysing geo-folksonomies that builds on and extends existing statistical methods by considering specific concepts of relevance to geographic place resources, namely, place types and place-related activities, and by building a place ontology to encode those concepts and relationships. The folksonomy analysis and evaluation are demonstrated using a realistic geo-folksonomy data set. The resulting ontology is used to build user profiles from the folksonomy. The derived profiles reflect the association between users and the specific places they tag as well as other places with relevant associated place type and activities. The methods proposed here provide the potential for many interesting and useful applications, including the harvesting of useful insight on geographic space and employing the derived user profiles to enhance the search experience and to identify similarities between users based on their association to geographic places.     

新疆多语种地名空间分布特征研究

将新疆维吾尔自治区16037个地名作为研究对象,利用ArcGIS10.5核密度的空间平滑分析法和数理统计方法,以多语种地名的视角,分析新疆各语种地名空间分布特征及所蕴含的文化内涵。研究发现：汉族与维吾尔族为农耕民族,因生产方式对地形、坡度的挑剔和水源的依赖等特征,新疆汉语和维吾尔语地名多分布于山前冲积平原,以天山山脉为界,天山北麓汉语地名集中分布,南麓维吾尔语地名分布密集;蒙古族与哈萨克族为游牧民族,因其换季游牧需要广阔草场等特性,蒙古语地名多分布于天山北麓以西的缓坡、低山、高平原地区,总体呈现大分散、小集中的特点。哈萨克语地名集中分布于阿尔泰山、塔尔巴哈台山及伊犁河谷的中山区。总体上,新疆多民族多语种地名的分布特征与地形海拔、坡度等地理环境条件和以此为基础的生产方式关系密切。     

A natural language processing and geospatial clustering framework for harvesting local place names from geotagged housing advertisements

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.     

Are prominent mountains frequently mentioned in text? Exploring the spatial expressiveness of text frequency

Data-driven GIScience shows a growing interest in making spatial information from large text data. In this paper, we quantify and thus evaluate the relation between text frequency and properties of the outer-text, geographic setting by comparing text frequencies of mountain names to the respective geomorphometric characteristics. We focus on some 2000 unique mountain names that appear some 50,000 times in a large compilation of texts on Swiss alpine history. The results on the full data set suggest only a weak relation: only 5–10% of the variation in the text frequency being explained by the respective geomorphometric characteristics. However, an analysis of multiple scales allows us to identify a Simpson’s Paradox. What appears to be ‘noise’ in the analysis of all mountains in the whole of Switzerland shows significant local signals. Small spatial extents, found all over Switzerland, can show considerably strong correlations between text frequency and spatial prominence, with up to 90% of the total variation explained. We argue that our findings have practical implications for data-driven GIScience. Retrieving meaningful spatial information from text might only be possible if the spatial scale of analysis reflects the spatial scale described in the input text documents.     

Criminal dons and extralegal security privatization in downtown Kingston,Jamaica

In cities across the world, neoliberal retrenchment, shrinking public expenditure and an emphasis on private‐public partnerships have resulted in the privatization of many services formerly provided by the state. The privatization of security has been one of the most significant shifts in this regard, with citizens becoming responsibilized for safeguarding their own physical integrity and material belongings. This transfer of state responsibility has had an important effect on the spatial organization of cities, as formerly public spaces become both privatized and militarized. Drawing on research in Kingston, Jamaica, this paper describes how new actors from criminal organizations to security companies move to fill these physical and social spaces. Arguing for an increased focus on spatial retreat from below, it highlights the extent to which an extralegal transformation of urban security and services takes place in marginalized areas, parallel to the better studied privatization of security services in affluent areas.     

茂名市地名与地形关系

地形是自然环境中影响地名的一个重要地理要素,通过对茂名市88个镇和其下辖1 513个村地名的统计,结合地名志等文献资料中的地名来源,发现茂名市地名与地形的关系密切,且山地地区地名与地形关联性最强,丘陵地区次之,平原台地地区最小。地形影响地名的产生,地名也反映着居住地选择的趋向,显示了茂名的地名文化。通过对茂名市地名与地形各个要素之间关系的分析,得出了山区的聚落多选择建立在山谷中比较低平的区域;丘陵地区的居住地常选择在山坡和山谷、山间的低地;平原台地地区有较多地名与单座山有关。     

Mapping and measuring place attachment

The concept of place attachment has been studied extensively across multiple disciplines but only recently with empirical measurement using public participation GIS (PPGIS) and related crowd-sourcing mapping methods. This research trialed a spatially explicit method for identifying place attachment in a regional study in South Australia. Our research objectives were to (1) analyze and present the spatial results of the mapping method as a benchmark for future research, (2) compare mapped place attachment to the more common practice of mapping landscape values in PPGIS that comprise a values home range, (3) identify how participant socio-demographic and home location attributes influence place attachment, (4) provide some guidance for mapping place attachment in future research. We found large spatial variability in individual place attachment and mapped landscape values using both area and distance-based measures. The area of place attachment is influenced by occupational roles such as farming or conservation, as well as home location, especially in coastal versus non-coastal contexts. The spatial distribution of mapped landscape values or values home range is related to, but not identical to mapped place attachment with just over half of landscape values located outside the area of mapped place attachment. Economic livelihood values, as an indicator of place dependence, and social values, as an indicator of place identity, are more likely to be mapped within the place attachment area. Aggregated place attachment across participants in the region showed similar spatial intensity to aggregated values home range, but area-based assessment of place attachment and values home range are distorted by edge effects such as a coastline. To further develop the mapping of place attachment in PPGIS, we identify knowledge gaps from our study and offer suggestions for future research design.     

藏族村落地名的空间格局、生成机制与保护策略——以甘南藏族自治州夏河县为例

地名是特定空间上自然或人文地理实体的专有名称。村落地名作为文化景观的重要组成部分,记录着多元文化信息,也是集体记忆的重要载体。研究村落地名的空间格局、生成机制与保护策略,对于传承乡土文脉具有深远的价值。以甘南州夏河县藏族村落为例,应用GIS核密度估计法刻画了地名的空间格局,从文化景观视角出发解读了藏族村落地名的类型和生成机制。研究表明：以文化型为主、自然型为辅、复合型补充的地名体系是藏族村落命名的基本范式;映射出深厚的自然性基因、恋地性基因、记述性基因和关系性基因等文化景观特质;环境指向、族源沿袭、信仰认同和区际交融是藏族村落地名生成的源发力量。制定地名保护名录、建立地名标识系统是藏族村落地名保护的当务之急。     

地理信息系统学科中几个基本问题探讨

地理信息科学(GISci)尚不成熟,但中国地理信息系统(GIS)高等教育发展快速,因此GIS学科发展面临许多问题和挑战。尝试从哲学本体论、认识论和方法论角度探讨GIS学科中的几个基本问题:现实世界、概念世界、数字世界、空间认知、空间表达、空间分析与研究方法,同时从社会实践角度总结了GIS应用中的组织、管理与社会问题。1)系统总结地理空间、空间认知与信息表达的关系以及地理信息的基本特征;2)回顾GIS的概念与内涵演变,论证GIS概念的多样性与同一性,分析了几个代表性的GIS研究方向;3)在回顾地理学研究范式转变的基础上,探讨空间分析、知识产生过程以及基于GIS的地理学研究范式;4)辨析GIS与组织管理间的关系、GIS与社会间的互动关系,特别强调发展中国家GIS应用面临的问题。建议围绕“空间认知与表达”、“GIS方法论”、“GIS、组织与社会”进行跨学科研究,完善GIS学科体系。     

北京城市认知的空间关联模式——城市地名认知率的空间分析

论文通过空间聚类分析方法,在基于认知率和位置关系划分认知地点类型、街道类型的基础上,讨论了北京城市地名认知率的空间差异性、基于地点-街道的空间认知关联模式,并得出三方面的结论.第一,北京城市空间认知差异性明显.从地点类型来看,城区南部中心城区和近郊区的认知率低且分布集中,东北部近郊区认知率低且离散性强,西部中心城区和近郊区认知率高且集中,东南中心城区认知率高且离散.从街道类型看,中心城区街道认知率高,外围街道认知率低,表现为中心-边缘结构.第二,各类型街道对23地点的认知率与距离之间表现为不同的结合型式,有近距离一低认知率型、近距离-高认知率型、中距离-中认知率型、远距离-低认知率型、远距离-高认知率型.不同结合型式中距离对认知率的影响因结合型式而异.第三,北京城市空间的认知曲线存在双峰,一峰表现为近距离、高认知率特征,具体位置随认知地点而异;另一峰表现为远距离、高认知率特征,主要是城市中心区认知地点.     

基于Tripadvisor 的中国旅游地国际关注度及空间格局

用户贡献内容(UGC)已逐渐成为旅游行为与感知研究的重要数据源.区别于通常利用搜索引擎关键词数量描述网络关注度的方法,本文引入电子社区层次结构为权重因子,建立了基于社区UGC的旅游关注度模型,能够灵活调节模型表达的重点,优化计算结果.针对著名旅游电子社区Tripadvisor 的研究发现,国外社区用户对中国旅游的关注呈现3 个典型特征:①旅游关注集中在“长城、泰山、黄山、九寨沟、张家界”等少数旅游吸引物,和“北京、香港、上海、桂林”少量目的地城市;大量吸引物和目的地关注度较低,呈现“长尾现象”与极化特征.②吸引物与目的地城市的关注空间具有明显耦合性,关注度较高的吸引物多邻近或隶属于关注度较高的城市,如桂林阳朔、北京长城、成都都江堰和九寨沟、杭州西湖等.③旅游关注空间整体呈现出由高到低的“东—中—西”格局,与中国区域经济的“东—中—西”梯度格局基本耦合;北京、香港、广州、深圳、上海、成都等关注中心也与区域经济中心一致.旅游资源禀赋、电子口碑传播模式、地理区位、经济水平和关注者国家的文化背景、经济发展状况、地理区位等是影响旅游者关注度及其空间格局变化的主要因素.旅游关注度模型旨在解决互联网用户对区域旅游关注的定量计算问题,为基于互联网UGC的旅游地理学研究提供新思路.     

Cyber Literacy for GIScience: Toward Formalizing Geospatial Computing Education

The unprecedented availability of geospatial data and technologies is driving innovation and discovery but not without the risk of losing focus on the geographic foundations of space and place in this vast “cyber sea” of data and technology. There is a pressing need to educate a new generation of scientists and citizens who understand how space and place matter in the real world and who understand and can keep pace with technological advancements in the computational world. We define cyberliteracy for GIScience (cyberGIScience literacy) and outline eight core areas that serve as a framework for establishing the essential abilities and foundational knowledge necessary to navigate and thrive in this new technologically rich world. The core areas are arranged to provide multiple dimensions of learning ranging from a technological focus to a problem solving focus or a focus on GIScience or computational science. We establish a competency matrix as a means of assessing and evaluating levels of cyberGIScience literacy across the eight core areas. We outline plans to catalyze the collaborative development and sharing of instructional materials to embed cyberGIScience literacy in the classroom and begin to realize a cyberliterate citizenry and academe. Key Words: big data, computational thinking, geographic education, GIS, spatial thinking.     

Diversity patterns of plant place names reveal connections with environmental and social factors

There is a strong connection between cultural and biological diversity at a global scale, especially in the linguistic domain, but less is known at regional scales. Indicators of such reciprocity are found in the linguistic expression of natural elements, and their representation in the landscape through toponymy. Here we evaluate the geographic distribution of phyto-toponyms, places named after a native local flora, in Galicia, NW Spain. We created the concept of toponymic species (topo-species) for groups of places named after a plant taxon. By using different regression models assuming global effects of the variables (Ordinary Least Squares, OLS) or non-stationarity (Geographically Weighted Regression, GWR), we explored the connection of topo-species richness and diversity with environmental (river density, altitude and natural habitats density) and social (total density of toponyms, population density) factors.Topo-species richness and diversity were significantly correlated with the studied factors. Total density of toponyms, river density, altitude and natural habitats density showed significant positive values in the models, while population density had little or no effects. GWR performed better for all variables, especially for Shannon diversity index. We conclude that place names of natural elements depict human’s interaction with the environment. They are stable, spatially-explicit elements that may be used as indicators of bio-cultural diversity. In addition, they represent an intangible cultural heritage that should also be preserved.     

Utilising volunteered geographic information to assess resident's flood evacuation shelters. Case study: Jakarta

Research on disaster response frequently uses volunteered geographic information (VGI), due to its capability to provide near real-time information during and after a disaster. It is much less commonly used in spatial planning related to disaster management. However, VGI appears to have considerable potential for use in spatial planning and offers some advantages over traditional methods. For example, VGI can capture residents' preferences in a much faster, more timely, and more comprehensive fashion than is possible with, for example, questionnaires and surveys. This research investigates the usefulness of VGI for planning flood evacuation shelters. Using Jakarta, Indonesia, as a case study, we use VGI to capture the locations of flood evacuation shelters based on residents' preferences during flood periods in 2013–2014 and 2014–2015 and compare these with the locations of official shelters. Floods frequently affect Jakarta and the city administration uses VGI in flood emergency responses. Moreover, Jakarta has been identified as having the largest number of active Twitter users among cities worldwide. Thus, Jakarta is an appropriate place to study the use of VGI for planning evacuation shelters. VGI generated by Twitter users was used to identify the shelter locations preferred by Jakarta residents, and more precisely the flood evacuees. Of 171,046 tweets using keywords relating to flood evacuation, the content of 306 tweets indicated that they had been sent from inside or near evacuation shelters. The spatial pattern showed that those tweets were sent from 215 locations, mostly located near flooded areas. The analysis further showed that 35.6% of these shelter locations preferred by residents intersected with the locations of official evacuation shelters. As a general conclusion, our study demonstrates the advantages of using VGI for spatial planning, which mainly relates to the ease of capturing community preferences over a large area.     

An OLS regression model for context-aware tile prefetching in a web map cache

The increasing popularity of web map services has motivated the development of more scalable services in the spatial data infrastructures. Tiled map services have emerged as a scalable alternative to traditional map services. Instead of rendering map images on the fly, a collection of pre-generated image tiles can be served very fast from a server-side cache. However, during the start-up of the service, the cache is initially empty and users experience a poor quality of service. Tile prefetching attempts to improve hit rates by proactively fetching map images without waiting for client requests.

While most popular prefetching policies in traditional web caching consider only the previous access history to make predictions, significant improvements could be achieved in web mapping by taking into account the background geographic information.

This work proposes a regressive model to predict which areas are likely to be requested in the future based on spatial cross-correlation between an unconstrained catalog of geographic features and a record of past cache requests. Tiles that are anticipated to be most frequently requested can be pre-generated and cached for faster retrieval. Trace-driven simulations with several million cache requests from two different nation-wide public web map services in Spain demonstrate that accurate predictions and performance gains can be obtained with the proposed model.