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1.
Twitter has emerged as a global social network of active users who share conversations with one another in an online setting. Academics are one community that has increasingly taken to Twitter as a means of connecting with other scholars, sharing research, and obtaining meaningful feedback. Tweeting has become especially popular during academic conferences where conference attendees use Twitter hashtags to filter conference conversations into a separate dialogue. For geographers, the Annual Meeting of the American Association of Geographers (AAG) represents one such occasion to use Twitter to discuss contemporary developments in geographic research. In this article, we provide an overview of Twitter as well as the ways in which the academic community uses the platform. Following this, we discuss the tweets sent using the hashtag for the 2018 AAG Annual Meeting, #AAG2018. To analyze these tweets, we collected all tweets with this hashtag for a period of four weeks and examined the content using word clouds and sentiment analysis to explore general feelings and trends associated with geography and the AAG Annual Meeting. We conclude with suggestions for future research avenues that could use Twitter data to gauge the pulse of the geographic discipline. Key Words: academic conferences, American Association of Geographers, geography, sentiment analysis, Twitter.  相似文献   

2.
《Urban geography》2013,34(7):582-610
This paper examines the geography of violent crime across the neighborhoods of Tucson and South Tucson, Arizona. The research is informed by the tenets of modern social disorganization theory, which has a strong ecological or environmental basis. Three different crime indices are computed; each represents an annual average during the five-year period 1995-1999. The most comprehensive index captures aggravated assaults, homicides, robberies, and sexual assaults. After providing a factor-ecological study of the study area, using 27 variables taken from the 1990 census, various regression models are developed to explain violent crime patterns. These models use a smaller array of ten demographic, economic, and social attributes to predict patterns at the block group level. A number of variables are found to be significant across all models, thereby providing further support for social disorganization theory. Stability in the signs and values of the estimates suggest that a general model of violent crime can be established for the study region. The paper closes with a short discussion of some public policy implications.  相似文献   

3.
ObjectivesUsing publicly available, geotagged Twitter data, we created neighborhood indicators for happiness, food and physical activity for three large counties: Salt Lake, San Francisco and New York.MethodsWe utilize 2.8 million tweets collected between February–August 2015 in our analysis. Geo-coordinates of where tweets were sent allow us to spatially join them to 2010 census tract locations. We implemented quality control checks and tested associations between Twitter-derived variables and sociodemographic characteristics.ResultsFor a random subset of tweets, manually labeled tweets and algorithm labeled tweets had excellent levels of agreement: 73% for happiness; 83% for food, and 85% for physical activity. Happy tweets, healthy food references, and physical activity references were less frequent in census tracts with greater economic disadvantage and higher proportions of racial/ethnic minorities and youths.ConclusionsSocial media can be leveraged to provide greater understanding of the well-being and health behaviors of communities—information that has been previously difficult and expensive to obtain consistently across geographies. More open access neighborhood data can enable better design of programs and policies addressing social determinants of health.  相似文献   

4.
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.  相似文献   

5.
The movements of ideas and content between locations and languages are unquestionably crucial concerns to researchers of the information age, and Twitter has emerged as a central, global platform on which hundreds of millions of people share knowledge and information. A variety of research has attempted to harvest locational and linguistic metadata from tweets to understand important questions related to the 300 million tweets that flow through the platform each day. Much of this work is carried out with only limited understandings of how best to work with the spatial and linguistic contexts in which the information was produced, however. Furthermore, standard, well-accepted practices have yet to emerge. As such, this article studies the reliability of key methods used to determine language and location of content in Twitter. It compares three automated language identification packages to Twitter's user interface language setting and to a human coding of languages to identify common sources of disagreement. The article also demonstrates that in many cases user-entered profile locations differ from the physical locations from which users are actually tweeting. As such, these open-ended, user-generated profile locations cannot be used as useful proxies for the physical locations from which information is published to Twitter.  相似文献   

6.
In recent years, social media emerged as a potential resource to improve the management of crisis situations such as disasters triggered by natural hazards. Although there is a growing research body concerned with the analysis of the usage of social media during disasters, most previous work has concentrated on using social media as a stand-alone information source, whereas its combination with other information sources holds a still underexplored potential. This article presents an approach to enhance the identification of relevant messages from social media that relies upon the relations between georeferenced social media messages as Volunteered Geographic Information and geographic features of flood phenomena as derived from authoritative data (sensor data, hydrological data and digital elevation models). We apply this approach to examine the micro-blogging text messages of the Twitter platform (tweets) produced during the River Elbe Flood of June 2013 in Germany. This is performed by means of a statistical analysis aimed at identifying general spatial patterns in the occurrence of flood-related tweets that may be associated with proximity to and severity of flood events. The results show that messages near (up to 10 km) to severely flooded areas have a much higher probability of being related to floods. In this manner, we conclude that the geographic approach proposed here provides a reliable quantitative indicator of the usefulness of messages from social media by leveraging the existing knowledge about natural hazards such as floods, thus being valuable for disaster management in both crisis response and preventive monitoring.  相似文献   

7.
In this article, we present the GeoCorpora corpus building framework and software tools as well as a geo-annotated Twitter corpus built with these tools to foster research and development in the areas of microblog/Twitter geoparsing and geographic information retrieval. The developed framework employs crowdsourcing and geovisual analytics to support the construction of large corpora of text in which the mentioned location entities are identified and geolocated to toponyms in existing geographical gazetteers. We describe how the approach has been applied to build a corpus of geo-annotated tweets that will be made freely available to the research community alongside this article to support the evaluation, comparison and training of geoparsers. Additionally, we report lessons learned related to corpus construction for geoparsing as well as insights about the notions of place and natural spatial language that we derive from application of the framework to building this corpus.  相似文献   

8.
The 2015 Middle East respiratory syndrome (MERS) outbreak in South Korea gave rise to chaos caused by psychological anxiety, and it has been assumed that people shared rumors about hospital lists through social media. Sharing rumors is a common form of public perception and risk communication among individuals during an outbreak. Social media analysis offers an important window into the spatiotemporal patterns of public perception and risk communication about disease outbreaks. Such processes of socially mediated risk communication are a process of meme diffusion. This article aims to investigate the role of social media meme diffusion and its spatiotemporal patterns in public perception and risk communication. To do so, we applied analytical methods including the daily number of tweets for metropolitan cities and geovisualization with the weighted mean centers. The spatiotemporal patterns shown by Twitter users' interests in specific places, triggered by real space events, demonstrate the spatial interactions among places in public perception and risk communication. Public perception and risk communication about places are relevant to both social networks and spatial proximity to where Twitter users live and are interpreted in reference to both Zipf's law and Tobler's law.  相似文献   

9.
ABSTRACT

Accurate crime prediction can help allocate police resources for crime reduction and prevention. There are two popular approaches to predict criminal activities: one is based on historical crime, and the other is based on environmental variables correlated with criminal patterns. Previous research on geo-statistical modeling mainly considered one type of data in space-time domain, and few sought to blend multi-source data. In this research, we proposed a spatio-temporal Cokriging algorithm to integrate historical crime data and urban transitional zones for more accurate crime prediction. Time-series historical crime data were used as the primary variable, while urban transitional zones identified from the VIIRS nightlight imagery were used as the secondary co-variable. The algorithm has been applied to predict weekly-based street crime and hotspots in Cincinnati, Ohio. Statistical tests and Predictive Accuracy Index (PAI) and Predictive Efficiency Index (PEI) tests were used to validate predictions in comparison with those of the control group without using the co-variable. The validation results demonstrate that the proposed algorithm with historical crime data and urban transitional zones increased the correlation coefficient by 5.4% for weekdays and by 12.3% for weekends in statistical tests, and gained higher hit rates measured by PAI/PEI in the hotspots test.  相似文献   

10.
Research within the geography of crime and spatial criminology literature most often show that crime is highly concentrated in particular places. Moreover, a subset of this literature has shown that the spatial patterns of these concentrations are different across crime types. This raises questions regarding the appropriateness of aggregating crime types (property and violent crime, for example) when the underlying spatial pattern is of interest. In this paper, using crime data from Campinas, Brazil, we investigate the crime concentrations and the similarities among different crime types across space. Similar to some recent research in another context, we find that crime is highly concentrated in Campinas but the ability to aggregate similar crime types at the street segment level is not generalizability when compared to a North American context.  相似文献   

11.
This research assesses the impact that one natural disaster—Hurricane Katrina—and subsequent population movements have had on crime in the state of Louisiana. Using Index Crimes from the Louisiana Commission on Law Enforcement and population estimates from the U.S. Census Bureau, time series of violent and nonviolent crime rates were first analyzed using autoregressive, integrated, and moving average (ARIMA) models. Cumulative percentile maps were created next to analyze spatial trends of crime hot and cold spots in the study area. Overall, results from this research support theories that suggest that crime rates remain stable or actually decline in regions receiving evacuees from areas hardest hit by the hurricane. In the case of Orleans Parish, results are inconclusive due to unreliable crime rates for the period following Hurricane Katrina until the beginning of 2006. It is suggested that crime rates in Orleans Parish fell drastically after the storm. However, some crime types, including robbery, burglary, and larceny, returned to pre-Katrina levels and murder and aggravated assault even exceeded prestorm averages by the end of December 2007.  相似文献   

12.
张延吉  朱春武 《地理研究》2021,40(2):528-540
基于面域汇总数据的犯罪地理分析不仅存在MAUP局限,还会制约理论发展。本文将基于距离测度方法的DO指数用于犯罪地理研究,在连续空间上揭示2013—2018年北京盗窃、抢夺抢劫、暴力犯罪与32类城市功能的分布关系。研究表明：① 98%的“犯罪-功能”组合呈共聚分布,单一尺度分析极易低估犯罪发生地的种类数;② 由于罪犯在中等尺度上选择收益、风险、成本适中的概率最高,“犯罪-功能”组合的共聚尺度与程度多为倒U型关系,该规律有助补足日常活动理论和理性选择理论的空间视角;③ 随着监管加强,三种犯罪与所有功能的总体共聚程度渐趋下降,暴力犯罪的共聚尺度大于“两抢一盗”;④ 较之犯罪模式理论中的单一共聚类型,共聚组合可细分成大、中、小尺度强共聚型以及弱共聚型等小类。本研究将犯罪空间形成机制简化为犯罪点与功能点的几何关系,未来需克服混淆因素干扰、功能点均质化假设等。  相似文献   

13.
The combination of crime mapping and geospatial analysis methods has enabled law enforcement agencies to develop more proactive methods of targeting crime-prone neighborhoods based on spatial patterns, such as hot spots and spatial proximity to specific points of interest. In this article, we investigate the spatial and temporal patterns of the neighborhood crimes of aggravated assault and larceny in 297 census tracts in Miami–Dade County from 2007 to 2015. We use emerging hot spot analysis (EHSA) to identify the spatial patterns of emerging, persistent, continuous, and sporadic hot spots. In addition, we use geographically weighted regression to analyze the spatial clustering effects of sociodemographic variables, poverty rate, median age, and ethnic diversity. The hot spots for larceny are much more diffused than those for aggravated assaults, which exhibit clustering in the north over Liberty City and Miami Gardens and in the south near Homestead, and the ethnic heterogeneity index has a moderate and positive effect on the incidence of both larceny and aggravated assaults. The findings suggest that law enforcement can better target prevention programs for violent versus property crime using geospatial analyses. Additionally, the ethnic concentration of neighborhoods influences crime differently in neighborhoods of different socioeconomic status, and future studies should account for spatial patterns when estimating conventional regression models.  相似文献   

14.
Many physical and sociological processes are represented as discrete events in time and space. These spatio-temporal point processes are often sparse, meaning that they cannot be aggregated and treated with conventional regression models. Models based on the point process framework may be employed instead for prediction purposes. Evaluating the predictive performance of these models poses a unique challenge, as the same sparseness prevents the use of popular measures such as the root mean squared error. Statistical likelihood is a valid alternative, but this does not measure absolute performance and is therefore difficult for practitioners and researchers to interpret. Motivated by this limitation, we develop a practical toolkit of evaluation metrics for spatio-temporal point process predictions. The metrics are based around the concept of hotspots, which represent areas of high point density. In addition to measuring predictive accuracy, our evaluation toolkit considers broader aspects of predictive performance, including a characterisation of the spatial and temporal distributions of predicted hotspots and a comparison of the complementarity of different prediction methods. We demonstrate the application of our evaluation metrics using a case study of crime prediction, comparing four varied prediction methods using crime data from two different locations and multiple crime types. The results highlight a previously unseen interplay between predictive accuracy and spatio-temporal dispersion of predicted hotspots. The new evaluation framework may be applied to compare multiple prediction methods in a variety of scenarios, yielding valuable new insight into the predictive performance of point process-based prediction.  相似文献   

15.
ABSTRACT

The investigation of human activity patterns from location-based social networks like Twitter is an established approach of how to infer relationships and latent information that characterize urban structures. Researchers from various disciplines have performed geospatial analysis on social media data despite the data’s high dimensionality, complexity and heterogeneity. However, user-generated datasets are of multi-scale nature, which results in limited applicability of commonly known geospatial analysis methods. Therefore in this paper, we propose a geographic, hierarchical self-organizing map (Geo-H-SOM) to analyze geospatial, temporal and semantic characteristics of georeferenced tweets. The results of our method, which we validate in a case study, demonstrate the ability to explore, abstract and cluster high-dimensional geospatial and semantic information from crowdsourced data.  相似文献   

16.
Control data are critical for improving areal interpolation results. Remotely sensed imagery, road network, and parcels are the three most commonly used ancillary data for areal interpolation of population. Meanwhile, the open access geographic data generated by social networks is emerging as an alternative control data that can be related to the distribution of population. This study evaluates the effectiveness of geo-located night-time tweets data as ancillary information and its combination with the three commonly used ancillary datasets in intelligent areal interpolation. Due to the skewed Twitter user age, the other purpose of this study is to test the effect of age bias control data on estimation of different age group populations. Results suggest that geo-located tweets as single control data does not perform as well as the three other control layers for total population and all age-specific population groups. However, the noticeable enhancement effect of Twitter data on other control data, especially for age groups with a high percentage of Twitter users, suggests that it helps to better reflect population distribution by increasing variation in densities within a residential area delineated by other control data.  相似文献   

17.
ABSTRACT

The analysis of geographically referenced data, specifically point data, is predicated on the accurate geocoding of those data. Geocoding refers to the process in which geographically referenced data (addresses, for example) are placed on a map. This process may lead to issues with positional accuracy or the inability to geocode an address. In this paper, we conduct an international investigation into the impact of the (in)ability to geocode an address on the resulting spatial pattern. We use a variety of point data sets of crime events (varying numbers of events and types of crime), a variety of areal units of analysis (varying the number and size of areal units), from a variety of countries (varying underlying administrative systems), and a locally-based spatial point pattern test to find the levels of geocoding match rates to maintain the spatial patterns of the original data when addresses are missing at random. We find that the level of geocoding success depends on the number of points and the number of areal units under analysis, but generally show that the necessary levels of geocoding success are lower than found in previous research. This finding is consistent across different national contexts.  相似文献   

18.
Geography of crime research dates back to the early 1800s, most of which in English and in the context of the United States and Europe, but with a growing and significant literature studying the developing world. We contribute to this literature through an application of social disorganization theory in a Latin American context: Campinas, Brazil. We consider a number of property and violent crime types using census tracts as the spatial unit of analysis. Implementing a spatial regression method, we find support for social disorganization theory, but not as strong as similar studies in Europe and North America. However, because of the context of Campinas, Brazil, a different understanding of the local conditions proves to be important for understanding the geography of crime in this context. The implications of these results are discussed in the context of theoretical developments as well as crime prevention initiatives.  相似文献   

19.
Georeferenced user-generated datasets like those extracted from Twitter are increasingly gaining the interest of spatial analysts. Such datasets oftentimes reflect a wide array of real-world phenomena. However, each of these phenomena takes place at a certain spatial scale. Therefore, user-generated datasets are of multiscale nature. Such datasets cannot be properly dealt with using the most common analysis methods, because these are typically designed for single-scale datasets where all observations are expected to reflect one single phenomenon (e.g., crime incidents). In this paper, we focus on the popular local G statistics. We propose a modified scale-sensitive version of a local G statistic. Furthermore, our approach comprises an alternative neighbourhood definition that enables to extract certain scales of interest. We compared our method with the original one on a real-world Twitter dataset. Our experiments show that our approach is able to better detect spatial autocorrelation at specific scales, as opposed to the original method. Based on the findings of our research, we identified a number of scale-related issues that our approach is able to overcome. Thus, we demonstrate the multiscale suitability of the proposed solution.  相似文献   

20.
This research analyzes changes in crime rates by city size and determines the extent to which these changes can be explained by socioeconomic variables. More particularly it addresses rates of change in mean crime rates for violent and property crime between 1976–1984 and 1985–1994 for all U. S. cities, then compares results to Ohio cities. It provides a detailed analysis of changing crime rates in 111 Ohio cities with populations between 10,000 and 99,999 inhabitants and attempts to account for crime differentials between these cities employing linear regression and factor analysis. Results indicate that crime is significantly related to poverty and its associated conditions and processes.  相似文献   

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