Liu, Y., Yuan, Y., & Gao, S. (2019). Modeling the Vagueness of Areal Geographic Objects: A Categorization System. ISPRS International Journal of Geo-Information, 8(7), 306. DOI: https://doi.org/10.3390/ijgi8070306
Abstract: Modeling vague objects with indeterminate boundaries has drawn much attention in geographic information science (GIScience). Because fields and objects are two perspectives in modeling geographic phenomena, this paper investigates the characteristics of vague regions from the perspective of the field/object dichotomy. Based on the assumption that a vague object can be viewed as the conceptualization of a field, we defined five categories of vague objects: (1) direct field-cutting objects, (2) focal operation-based field-cutting objects, (3) element-clustering objects, (4) object-referenced objects, and (5) dynamic boundary objects. We then established a categorization system to formalize the semantic differences between vague objects using the fuzzy set theory. The proposed framework provides valuable input for the conceptualization, interpretation, and modeling of vague geographical objects.
Understanding human mobility is important in many fields, such as geography, urban planning, transportation, and sociology. Due to the wide spatiotemporal coverage and low operational cost, mobile phone data have been recognized as a major resource for human mobility research. However, due to conflicts between the data sparsity problem of mobile phone data and the requirement of fine-scale solutions, trajectory reconstruction is of considerable importance. Although there have been initial studies on this problem, existing methods rarely consider the effect of similarities among individuals and the spatiotemporal patterns of missing data. To address this issue, we propose a multi-criteria data partitioning trajectory reconstruction (MDP-TR) method for large-scale mobile phone data. In the proposed method, a multi-criteria data partitioning (MDP) technique is used to measure the similarity among individuals in near real-time and investigate the spatiotemporal patterns of missing data. With this technique, the trajectory reconstruction from mobile phone data is then conducted using classic machine learning models. We verified the method using a real mobile phone dataset including 1 million individuals with over 15 million trajectories in a large city. Results indicate that the MDP-TR method outperforms competing methods in both accuracy and robustness. We argue that the MDP-TR method can be effectively utilized for grasping highly dynamic human movement status and improving the spatiotemporal resolution of human mobility research.
This paper proposes a methodology framework to transfer the cartographic style in different kinds of maps. By inputting the raw GIS vector data, the system can automatically render styles to the input data with target map styles but without CartoCSS or MapboxGL style specification sheets. The Generative Adversarial Networks (GANs) are used in this research. The study explores the potential of implementing artificial intelligence in cartography in the era of GeoAI.
We outline several important directions for the use of AI in cartography moving forward. First, our use of GANs can be extended to other mapping contexts to help cartographers deconstruct the most salient stylistic elements that constitute the unique look and feel of existing designs, using this information to improve design in future iterations. This research also can help nonexperts who lack professional cartographic knowledge and experience to generate reasonable cartographic style sheet templates based on inspiration maps or visual art. Finally, integration of AI with cartographic design may automate part of the generalization process, a particularly promising avenue given the difficult of updating high resolution datasets and rendering new tilesets to support the ’map of everywhere’.
Here is the abstract:
The advancement of the Artificial Intelligence (AI) technologies makes it possible to learn stylistic design criteria from existing maps or other visual arts and transfer these styles to make new digital maps. In this paper, we propose a novel framework using AI for map style transfer applicable across multiple map scales. Specifically, we identify and transfer the stylistic elements from a target group of visual examples, including Google Maps, OpenStreetMap, and artistic paintings, to unstylized GIS vector data through two generative adversarial network (GAN) models. We then train a binary classifier based on a deep convolutional neural network to evaluate whether the transfer styled map images preserve the original map design characteristics. Our experiment results show that GANs have a great potential for multiscale map style transferring, but many challenges remain requiring future research.
You can also visit the following links to see some of the trained results:
This paper proposes a methodology framework to calculate the solar energy that can be collected by solar panels paved on the road. Estimation of how much energy can be collected help making decision of where these photovoltaic road system should be built. Exemplified by the city of Boston, using street view images and taking light obstacles, traffic conditions, weather conditions and seasonal changes of solar radiation into consideration, the potential of solar energy generated by Boston’s road network is estimated precisely. Our results show that the energy obtained from urban road network can support all private cars in Boston.
Here is the abstract: A sustainable city relies on renewable energy, which promotes the development of electric vehicles. To support electric vehicles, the concept of charging vehicles while driving has been put forward. Under such circumstances, constructing solar panels on urban roads is an innovative option with great benefits, and the accurate calculation of road photovoltaic power generation is a prerequisite. In this paper, we propose a novel framework for predicting and calculating the solar radiation and electric energy that can be collected from the roads. Google Street View images are collected to measure the sky obstruction of roads which is integrated with the solar radiation model to estimate the irradiation receiving capability. In addition to sky obstruction, we also take the impact of traffic conditions and weather situations into consideration in the calculation. Radiation maps at different times in a year are produced from our work to analyze the roads photovoltaic distribution. In order to test the feasibility of our framework, we take Boston as a case study. Results show that roads in Boston can generate abundant electricity for all future electric vehicles in the city. What’s more, main roads through Boston exhibit better power generation potential, and the effect of the traffic condition is limited. Our calculation framework confirms that utilizing solar panels as road surfaces is a great supplement of city power with the unique ability to charge moving cars.
Recently, Dr. Laurel Legenza (PI) from the UW School of Pharmacy, Dr. Thomas R. Fritsche (Co-PI) from the Marshfield Medical Center and Professor Song Gao participating as a geospatial analysis scientist along with the State Cartographer’s Office (SCO) and other multidisciplinary collaborators, have been awarded a pilot grant from theUW Institute for Clinical and Translational Research (ICTR) and the Marshfield Clinic Research Institute for a research proposal titled “Geo-mapping antimicrobial resistance in E. coli from humans & animals” in Wisconsin.
When a patient arrives at a hospital with an infection, his/her doctor must decide which antibiotic might have the best chance of curing him/her — no easy feat when disease-causing pathogens are increasingly resistant to multiple antibiotics. To make this data more accessible, a team of researchers at the University of Wisconsin–Madison School of Pharmacyand the State Cartographer’s Office have developed a prototype system that maps out trends in antibiotic resistance across the State of Wisconsin, which provides guidance at a glance of the likelihood a pathogen will respond to a particular drug.
In our research, state-of-the-art computer vision and AI technologies are utilized to collect, store, handle, manipulate and analyze the human emotions and sentiment at different geographic scales. The research explored what and how people express their emotions at different places, and why and how their emotions would be influenced by environmental factors. Several maps are utilized to visualize where people may be happier than at other locations. In traditional research, we may only use questionnaires to investigate the human emotions and socioeconomic factors. But nowadays, it is possible to collect human emotions using large-scale user generated data online, including tweets, emoji, photos, articles, etc.. As we know, human emotions are innate characteristics of human beings, and with computer technology, it is possible to use objective methods to quantify the subjective human emotion. And it is quite important to build a computational workflow to handle large volumes of user generated data and extract emotion from those data efficiently. Here are several examples which we are working on.
In this study, a novel framework for extracting human emotions from large-scale georeferenced photos at different places is proposed. After the construction of places based on spatial clustering of user generated footprints collected in social media websites, online cognitive services are utilized to extract human emotions from facial expressions using state-of-the-art computer vision techniques. And two happiness metrics are defined for measuring the human emotions at different places. To validate the feasibility of the framework, we take 80 tourist attractions around the world as an example and a happiness ranking list of places is generated based on human emotions calculated over 2 million faces detected out from over 6 million photos. Different kinds of geographical contexts are taken into consideration to find out the relationship between human emotions and environmental factors. Results show that much of the emotional variation at different places can be explained by a few factors such as openness. The research may offer insights on integrating human emotions to enrich the understanding of sense of place in geography and in place-based GIS.
(2) Urban scale: relationship between human emotion and stock market fluctuation at Manhattan
In this research, we examined whether emotion expressed by users in social media can be influenced by stock market index or can predict the fluctuation of the stock market index. We collected the emotion data in Manhattan, New York City using face detection technology and emotion cognition services for photos uploaded to Flickr. Each face’s emotion was described in 8 dimensions the location was also recorded. An emotion score index was defined based on the combination of all 8 dimensions of emotion calculated by principal component analysis. The correlation coefficients between the stock market values and emotion scores are significant (R>0.59 with p < 0.01). Using Granger Causality analysis for cause and effect detection, we found that users’ emotion is influenced by stock market value change. A multiple linear regression model was established (R-square=0.76) to explore the potential factors that influence the emotion score. Finally, a sensitivity map was created to show sensitive areas where human emotion is easily affected by the stock market changes. We concluded that in Manhattan region: (1) there is a statistically significant relationship between human emotion and stock market fluctuation; (2) emotion change follows the movements of the stock market; (3) the Times Square and Broadway Theatre are the most sensitive regions in terms of public emotional reaction to the economy represented by stock value.
(3) Global scale: global human emotions in different groups of people
In this research, we used a huge global scale image dataset: YFCC100, to extract emotions from photos and to describe the worldwide geographic patterns of human happiness. Two indices of Average Smiling Index (ASI) and Happiness Index (HI) are defined from different perspectives to describe the degree of human happiness in a specific region. We computed the spatio-temporal characteristics of facial expression-based happiness on a global scale and linked them to some demographic variables (ethnicity, gender, age, and nationality). After that, the robust analysis was made to ensure our results are reliable. Results are in accordance with some previous studies in Social Science. For example, White and Black are often better at expressing happiness than Asian, women are more expressive than men, and happiness expressed varies across space and time. Our research provides a novel methodology for emotion measurement and it could be utilized for assessing a region‘s emotion conditions based on geo-crowdsourcing data. Robust analysis results indicate that our approaches are reliable and could be implemented for other research projects on place-based human sentiment analysis.
In the Mobile Age, with the widespread use of location-awareness devices, it is possible to collect large-scale location-awareness datasets, such as mobile phone data, GPS-enabled taxi trajectories, and location-based social media data, to sense complex human movements and human-environment interactions in urban space. Here are some examples of research that we have been working on.
(1) Discovering Spatial Interaction Communities from Mobile Phone Data
This research attempts to explore and interpret patterns embedded in the network of phone‐call interaction and the network of phone‐users’ movements, by considering the geographical context of mobile phone cells. We adopt an agglomerative clustering algorithm based on a Newman‐Girvan modularity metric and propose an alternative modularity function incorporating a gravity model to discover the clustering structures of spatial‐interaction communities using a mobile phone dataset from one week in a city in China. The results verify the distance decay effect and spatial continuity that control the process of partitioning phone‐call interaction, which indicates that people tend to communicate within a spatial‐proximity community. Furthermore, we discover that a high correlation exists between phone‐users’ movements in physical space and phone‐call interaction in cyberspace. Our approach presents a combined qualitative‐quantitative framework to identify clusters and interaction patterns, and explains how geographical context influences communities of callers and receivers. The findings of this empirical study are valuable for urban structure studies as well as for the detection of communities in spatial networks.
(2) Spatio-Temporal Analytics for Exploring Human Mobility Patterns and Urban Dynamics
In this research, we present a spatio-temporal analytical framework including spatiotemporal visualization (STV), space-time kernel density estimation (STKDE), and spatio-temporal-autocorrelation-analysis (STAA), to explore human mobility patterns and intra-urban communication dynamics. Experiments were conducted using large-scale detailed records of mobile phone calls in a city. The space-time path, time series graphs, vertical Bezier curves, STKDE, STAA, and related techniques in 3D GIS as well as statistical tests have been suggested for different spatio-temporal analysis tasks. We also investigated several statistical measures that extend the classic spatial association indices for spatio-temporal autocorrelation analysis. The spatial order of weighted matrix was found to have more significant effects than the temporal neighbors on influencing the autocorrelation strength of hourly phone calls.
(3) Spatio-Temporal-Network Visualization for Exploring Human Movements and Interactions in Physical and Virtual Spaces
Spatiotemporal visualization techniques are effective in detecting human activity patterns over space and time, while social network analysis is good for exploring graph structures and interactions among individual nodes and groups. Integrating these two types of methods can facilitate the exploration of complex social networks in space and time, and can help discover hidden spatiotemporal and social connections. In this research, we propose a novel conceptual framework for spatiotemporal and social network visualization in a three-dimensional context. Based on this framework, new spatio-temporal-network (STN) quantitative metrics (including STN-impact-extent, STN-impact-center, STN-distance, STN-efficiency, and STN-centrality) are introduced to measure the underlying dynamic interactions among entities. The proposed framework aims to help better understand spatiotemporal patterns of human dynamics and social interactions over both physical and virtual spaces simultaneously, as well as explore how emerging events trigger spatial-temporal-social interactions and information diffusion from a process perspective. As a proof of concept, we demonstrate the proposed framework with a case study using geotagged tweets and associated visualization in the ArcScene software. We hope that this research can stimulate new insights on integrating multidisciplinary knowledge to explore human dynamics in a broader way.
(4) Uncovering the digital divide and the physical divide using mobile phone data
In this research, we first aim at developing data analytics that can derive insights about how people from different regions communicate and connect via mobile phone calls and physical movements. We uncover the digital divide (geographical segregation of phone communication patterns) and the physical divide (geographical limits of human mobility) in a developing country. The research also demonstrates that the chosen spatial unit and temporal resolution can affect the community detection results of spatial interaction graphs when analyzing human mobility patterns and exploring urban dynamics in the mobile age. We find that the daily detection has generated a more stable partition structure than an hourly one, while monthly changes also exist over time. The presented framework can help identify patterns of spatial interaction in both cyberspace and physical space with phone call detailed records in some regions where census data acquisition is difficult, especially in some developing countries.
(5) Identifying Local Spatiotemporal Autocorrelation Patterns of Large-scale Taxi Pick-ups and Drop-offs
Analyzing spatiotemporal autocorrelation would be helpful to understand the underlying dynamic patterns in space and time simultaneously. In this work, we aim to extend the conventional spatial autocorrelation statistics to a more general framework considering both spatial and temporal dimensions. Specifically, we focus on the spatiotemporal version of Getis-Ord’s G*. The new indicator STG* can quantify the local association of adjacent features in space and time. As a proof of concept, the proposed method is applied in a large-scale GPS-enabled taxi dataset to identify local spatiotemporal autocorrelation patterns of taxi pick-ups and drop-offs in New York City.
(6) Extracting urban functional regions from points of interest and human activities on location‐based social networks
Data about points of interest (POI) have been widely used in studying urban land use types and for sensing human behavior. However, it is difficult to quantify the correct mix or the spatial relations among different POI types indicative of specific urban functions. In this research, we develop a statistical framework to help discover semantically meaningful topics and functional regions based on the co‐occurrence patterns of POI types. The framework applies the latent Dirichlet allocation (LDA) topic modeling technique and incorporates user check‐in activities on location‐based social networks. Using a large corpus of about 100,000 Foursquare venues and user check‐in behavior in the 10 most populated urban areas of the US, we demonstrate the effectiveness of our proposed methodology by identifying distinctive types of latent topics and, further, by extracting urban functional regions using K‐means clustering and Delaunay triangulation spatial constraints clustering. We show that a region can support multiple functions but with different probabilities, while the same type of functional region can span multiple geographically non‐adjacent locations. Since each region can be modeled as a vector consisting of multinomial topic distributions, similar regions with regard to their thematic topic signatures can be identified. Compared with remote sensing images which mainly uncover the physical landscape of urban environments, our popularity‐based POI topic modeling approach can be seen as a complementary social sensing view on urban space based on human activities.
(7) Detecting Origin-Destination Mobility Flows From Geotagged Tweets
Human origin-to-destination (OD) trip information is of major importance in urban transportation modelling and infrastructure planning in order to optimize the use of street networks. The increasing use of social media like Twitter offers unprecedented opportunities to study individual activities and movements, to know where users are at which time, and what they are talking about. In this work we study the reliability of detecting regional OD trips from individual geotagged tweets in comparison with survey data in a quantitative manner, and explore the spatiotemporal flow patterns extracted from social media. We investigate the research question of whether OD trips mined from social media yield comparable results to expensive and labour intensive large-scale studies. To do so, we derive large-scale OD trips from geotagged tweets, aggregate them, and compare the results by correlating them to the American Community Survey data.
(8) Exploring the uncertainty of activity zone detection from Geotagged Tweets with multi-scaled DBSCAN (M-DBSCAN)
While exploring human mobility patterns based on digital footprints captured from social media, the density-based spatial clustering of applications with noise (DBSCAN) method is often used to identify activity zones which an individual regularly visits. However, DBSCAN is sensitive to the two parameters, including the search radius of a cluster (eps), and the minimum number of points (minpts). This research first discusses the uncertainty while detecting an individual’s activity zones through digital footprints. An improved density-based clustering algorithm for mobility analysis known asMulti-Scaled DBSCAN (M-DBSCAN), is then presented to mitigate the detection uncertainty of clusters produced by DBSCAN at different scales of density and cluster size. Next, we demonstrate that M-DBSCAN iteratively calibrates suitable local eps and minpts values instead of using one global parameter setting as DBSCAN for detecting clusters of varying densities, and proves to be very effective for detecting potential activity zones (clusters) with the historic geo-tagged tweets of selected users. Besides, M-DBSCAN can significantly reduce the noise ratio (the proportion of trajectory points not included in any cluster) by identifying all points capturing the activities performed in each zone. Using the historic geo-tagged tweets of a large number of users in Madison, Wisconsin and Washington, D.C., the results of M-DBSCAN and DBSCAN with a minpts value of 4 and varying eps values reveal that: 1) M-DBSCAN can capture dispersed clusters with low density of points, and therefore detecting more activity zones for each user and resulting in a lower noise ratio; 2) A value of 40m or higher should be used for eps in order to reduce the possibility of collapsing distinctive activity zones, and ensure a relatively low noise ratio during the clustering process; and 3) A value between 200m to 300m is recommended for eps while using DBSCAN for detecting activity zones from geotagged tweets.
(9) Reconstruction of human movement trajectories from large-scale low-frequency mobile phone data
Understanding human mobility is significant in many fields, such as urban planning, transportation, and sociology. Due to the wide spatiotemporal coverage and low operational cost, mobile phone data have been recognized as a major resource for human mobility research. However, due to conflicts between the data sparsity problem of mobile phone data and the requirement of fine-scale solutions, trajectory reconstruction is of considerable importance. Although there have been initial studies on this problem, existing methods rarely consider the effect of similarities among individuals and the temporal patterns of missing data. To address this issue, we propose a novel multi-criteria data partitioning trajectory reconstruction (MDP-TR) method for large-scale mobile phone data. In this research, a multi-criteria data partitioning (MDP) technique is used to measure the similarity among individuals in near real-time and investigate the spatiotemporal patterns of missing data. With this technique, the trajectory reconstruction from mobile phone data is then conducted with classic machine learning models. We verified the method using a real mobile phone dataset in a large city. Results indicate that the MDP-TR method outperforms competing methods in both accuracy and robustness. We argue that the MDP-TR method can be effectively utilized for grasping highly dynamic human movement status and improving the spatiotemporal resolution of human mobility research.
(10) Understanding neighborhood isolation through spatial interaction network analysis using location big data
Hidden biases of racial and socioeconomic preferences shape residential neighborhoods throughout the USA. Thereby, these preferences shape neighborhoods composed predominantly of a particular race or income class. However, the assessment of spatial extent and the degree of isolation outside the residential neighborhoods at large scale is challenging, which requires further investigation to understand and identify the magnitude and underlying geospatial processes. With the ubiquitous availability of location-based services, large-scale individual-level location data have been widely collected using numerous mobile phone applications and enable the study of neighborhood isolation at large scale. In this research, we analyze large-scale anonymized smartphone users’ mobility data in Milwaukee, Wisconsin, to understand neighborhood-to-neighborhood spatial interaction patterns of different racial classes. Several isolated neighborhoods are successfully identified through the mobility-based spatial interaction network analysis.
A street network is a system of interconnecting polylines and points (called edges and nodes in network science) that represent street segments/roads and intersections for a given area. It plays a very important role in transportation operation and management and many other fields. Below are some examples of research that we have been working on in this field.
(1) Street Centrality and Traffic Flow
The structural and morphological properties of a street network, represented in topological or geographic metric measurements, are considered to be the key factors that shape dynamic urban traffic flow. Urban traffic flow can be seen as individual trips aggregately distributed in street networks. Each trip is generated from an origin and destination (OD) pair or multiple destinations with a network path connecting them. Generally, urban planners and transportation engineers rely on household questionnaires or transportation surveys on job–housing places for traffic-demand forecasting. With the rapid development of information and communication technology (ICT), the availability of large amounts of GPS (Global Positioning System) data and mobile phone data over time and space has increased the capability for monitoring, visualizing, analyzing, and modeling urban dynamics. In this research we investigate the spatial distribution of urban traffic flow based on taxi trajectories in a city in China and compute the correlation between urban traffic fl ow and street betweenness centrality. The results confirm that purely betweenness centrality is not an ideal measure for predicting urban traffic flow. We analyze the characteristics of betweenness and point out the ‘gap’ between betweenness centrality and actual flow. The gap is filled by a framework taking into account the distance decay and the spatial heterogeneity of human activities.
(2) Ride-sharing and Street Networks
Given different types of constraints on human life, people must make decisions that satisfy social activity needs. Minimizing costs (i.e. distance, time, or money) associated with travel plays an important role in perceived and realized social quality of life. Identifying optimal interaction locations (e.g., for ride-sharing) along road networks when there are multiple moving objects (MMO) with space–time constraints remains a challenge. In this research, we formalize the problem of finding dynamic ideal interaction locations for MMO as a spatial optimization model and introduce a context-based geoprocessing heuristic framework to address this problem.
(3) Spatial Interactions along Street Networks
Researchers have proposed many methods to investigate the spatial interactions derived from human movements, such as the gravity model and the radiation model. However, most studies have mainly focused on the interactions among areal units at an aggregated level, neglecting that in most cases, human movements are carried by vehicles and constrained by the underlying road networks. To fill this gap, we propose a novel approach to identify spatial interaction patterns of vehicle movements along urban road networks.
Good characterization of traffic interactions among urban roads can facilitate traffic-related applications, such as traffic control and short-term forecasting. Most studies measure the traffic interaction between two roads by their topological distance or the correlation between their traffic variables. However, the distance-based methods neglect the spatial heterogeneity of roads’ traffic interactions, while the correlation-based methods cannot capture the non-linear dependency between two roads’ traffic variables. In this paper, we propose a novel approach called Road2Vec to quantify the implicit traffic interactions among roads based on large-scale taxi operating route data using a Word2Vec model from the natural language processing (NLP) domain. A case study on short-term traffic forecasting is conducted with artificial neural network (ANN) and support vector machine (SVM) algorithms to validate the advantages of the presented method. The results show that the forecasting achieves a higher accuracy with the support of the Road2Vec method than with the topological distance and traffic correlation based methods.
(4) Public Transportation Optimization
Public transportation systems, in particular, bus systems, play an essential role in the process of urbanization. Typically more bus stops enable more people to access the bus whereas lower the efficiency of bus system. We utilize spatial optimization techniques to address this issue.
In one of our studies, we use the Spatial Interaction Coverage (SIC) model to identify and remove redundant bus stops while maintain the overall success of the whole bus system. The SIC model aims to model the relationship between demand points and bus stops. It takes factors such as the distance and the attractiveness of each bus stop into consideration. By applying the SIC model to the iXpress 202 route in Kitchener-Waterloo region in Canada, we can effectively identify the number of stops to maintain and remove redundant stops. The bus operation efficiency can be increased by 7.28% after optimization. The relationships between bus ridership and the socioeconomic variables (population, income, and age) in the study area are also analyzed. We are expanding our research to multiple cities with the consideration of multi-route buses and multi-mode transportation.
(5) Regional Economy and Transportation Network Analytics
With the booming economy in China, many researches have pointed out that the improvement of regional transportation infrastructure among other factors had an important effect on economic growth. Utilizing a large-scale dataset which includes 3.5 billion entry and exit records of vehicles along highways generated from toll collection systems, we attempt to establish the relevance of mid-distance land transport patterns to regional economic status through transportation network analyses. We apply standard measurements of complex networks to analyze the highway transportation networks. A set of traffic flow features are computed and correlated to the regional economic development indicator. The multi-linear regression models explain about 89% to 96% of the variation of cities’ GDP across three provinces in China. We then fit gravity models using annual traffic volumes of cars, buses, and freight trucks between pairs of cities for each province separately as well as for the whole dataset. We find the temporal changes of distance-decay effects on spatial interactions between cities in transportation networks, which link to the economic development patterns of each province. We conclude that transportation big data reveal the status of regional economic development and contain valuable information of human mobility, production linkages, and logistics for regional management and planning. Our research offers insights into the investigation of regional economic development status using highway transportation big data.
Funded by UW-Madison Office of the Vice Chancellor for Research and Graduate Education (VCRGE) and the Wisconsin Alumni Research Foundation (WARF).
Place names and the semantics of places described in natural languages rather than coordinates (i.e., longitude and latitude) are pervasive in human discourse, documents, and social media while location needs to be specified for mapping or interlinking other information. However, there is still a gap between the informal or vague cognition expressions of place (e.g., downtown, neighborhood, up north Wisconsin) and the formal computational representations of place in computerized information systems. Cognitive regions and places are notoriously difficult to represent in geographic information science and systems. They arise from the complex interactions of individuals, society, and the environment.
The emergence of big data brings new opportunities to better understand our geographic and socioeconomic environments. In this research, we will synthesize multi-source datasets from census, location-based social networks, news media, Wikipedia, travel blogs, and other open data websites, and utilizing the theory of place, natural language processing, machine learning, and visualization techniques for extracting and representing vague cognitive places. The research will also help better understand individuals’ observations, experiences, and exposures to different types of places and ambient social environments.
A specialissue on “Place-Based Research in GIScience and Geoinformatics” recently published on the ISPRS International Journal of Geo-Information (ISSN 2220-9964). It consists of 12 peer-reviewed articles relevant to the topic, co-edited by Professor Thomas Blaschke and Professor Song Gao.
Congratulations to Yuhao Kang and Yunlei Liang for their paper acceptance! Check “Publication” for more details.
“Utilizing Reverse Viewshed Analysis in Image Geo-Localization”by Yuhao Kang, Song Gao, and Yunlei Liang, has been accepted at the 2nd ACM SIGSPATIAL International Workshop on Recommendations for Location-based Services and Social Networks (LocalRec 2018).
“Optimizing Bus Stop Spacing Using the Simulated Annealing Algorithm with Spatial Interaction Coverage Model“, by Yunlei Liang, Song Gao, Tianyu Wu, Sujing Wang and Yuhao Wu, has been accepted for presentation at the 11th ACM SIGSPATIAL International Workshop on Computational Transportation Science (IWCTS 2018).