Identifying stops from mobile phone location data by introducing uncertain segments

Identifying stops is a primary step in acquiring activity‐related information from mobile phone location data to understand the activity patterns of individuals. However, signal jumps in mobile phone location data may create “fake moves,” which will generate fake activity patterns of “stops‐and‐moves.” These “fake moves” share similar spatiotemporal features with real short‐distance moves, and the stops and moves of trajectories (SMoT), which is the most extensively used stop identification model, often fails to distinguish them when the dataset has coarse temporal resolution. This study proposes the stops, moves, and uncertainties of trajectories (SMUoT) model to address this issue by introducing uncertain segment analysis to distinguish “fake moves” and real short‐distance moves. A real mobile phone location dataset collected in Shenzhen, China is used to evaluate the performance of SMUoT. We find that SMUoT improves the performance (i.e., 15 and 19% increase in accuracy and recall rate for a one‐hour temporal resolution dataset, respectively) of stop identification and exhibits high robustness to parameter settings. With a better reliability of “stops‐and‐moves” pattern identification, the proposed SMUoT can benefit various individual activity‐related research based on mobile phone location data for many fields, such as urban planning, traffic analysis, and emergency management.     

A name‐led approach to profile urban places based on geotagged Twitter data

Place is a concept that is fundamental to how we orientate and communicate space in our everyday lives. Crowdsourced social media data present a valuable opportunity to develop bottom‐up inferences of places that are integral to social activities and settings. Conventional location‐led approaches use a predefined spatial unit to associate data and space with places, which cannot capture the richness of urban places (i.e., spatial extents and their dynamic functions). This article develops a name‐led framework to overcome these limitations in using social media data to study urban places. The framework first derives place names from georeferenced Twitter data combining text mining and spatial point pattern analysis, then estimates the spatial extents by spatial clustering, and further extracts their dynamic functions with time, which makes up a complete place profile. The framework is tested on a case study in Camden Borough, London and the results are evaluated through comparisons to the Foursquare point of interest data. This name‐led approach enables the shift from space‐based analysis to place‐based analysis of urban space.     

SinoGrids: a practice for open urban data in China

ABSTRACT

In the past decade, an explosion of data has taken place in Chinese cities due to widespread use of mobile Internet devices, Web 2.0 applications, and the development of the “Wired City.” With advances in data storage and high-performance computing, big/open urban data have opened up important avenues for urban studies, planning practice, and commercial consultancy. Urban researchers and planners are eager to make use of these abundant, sophisticated, and dynamic data to deepen their understanding on urban form and functions. However, in practice, access to such urban data is limited in China due to institutional constraints on data distribution and data holders’ hesitation to share data. And this hampers urban analytics. To draw reliable conclusions about the workings of complex urban systems, efficient and effective interoperation of multisource urban datasets is needed. Also, dealing with the heterogeneity between datasets is an equally critical challenge, especially for urban planners and government officers. They would derive value from data analytics, but have little data processing experience. To address these issues, we initiated SinoGrids (Plan Xu Xiake), a crowdsourcing platform that standardizes (or “downscales”) microscale urban data in China to facilitate its sharing and interoperation. To assess the performance evaluation of SinoGrids, we propose field-testing with actual urban data and their potential users. Digital desert, a son project of SinoGrids is also included.     

Crowdsensing smart ambient environments and services

Whether it be Smart Cities, Ambient Intelligence, or the Internet of Things, current visions for future urban spaces share a common core, namely the increasing role of distributed sensor networks and the on‐demand integration of their data to power real‐time services and analytics. Some of the greatest hurdles to implementing these visions include security risks, user privacy, scalability, the integration of heterogeneous data, and financial cost. In this work, we propose a crowdsensing mobile‐device platform that empowers citizens to collect and share information about their surrounding environment via embedded sensor technologies. This approach allows a variety of urban areas (e.g., university campuses, shopping malls, city centers, suburbs) to become equipped with a free ad‐hoc sensor network without depending on proprietary instrumentation. We present a framework, namely the GeoTracer application, as a proof‐of‐concept to conduct multiple experiments simulating use‐case scenarios on a university campus. First, we demonstrate that ambient sensors (e.g. temperature, pressure, humidity, magnetism, illuminance, and audio) can help determine a change in environment (e.g. moving from indoors to outdoors, or floor changes inside buildings) more accurately than typical positioning technologies (e.g. global navigation satellite system, Wi‐Fi, etc.). Furthermore, each of these sensors contributes a different amount of data to detecting events. for example, illuminance has the highest information gain when trying to detect changes between indoors and outdoors. Second, we show that through this platform it is possible to detect and differentiate place types on a university campus based on inferences made through ambient sensors. Lastly, we train classifiers to determine the activities that a place can afford at different times (e.g. good for studying or not, basketball courts in use or empty) based on sensor‐driven semantic signatures.     

高分辨率遥感影像城中村提取的景观语义指数方法

城中村是中国一类特殊的非正式居民区。本文从城中村的物理特点出发,采用景观语义指数描述复杂的城中村场景,提出基于景观语义指数的高分辨率遥感影像城中村提取方法,并采用“分类置信度-反馈”机制进行实际可操作的大范围城中村制图。以广州市核心城区为例,城中村检测的总体精度达到了90%以上。试验结果表明相对于传统的光谱、纹理特征,景观语义指数能够更好地描述城中村的根本形态特点,更准确的城中村提取。此外,“分类置信度-反馈”机制能够充分参考机器学习的分类概率,以有限的人工干预生产更加准确的城中村制图产品。因此,本文方法能够有效应用于大范围的城中村提取与制图。     

Calibrating the dynamic Huff model for business analysis using location big data

The Huff model has been widely used in location‐based business analysis to delineate a trade area containing a store’s potential customers. Calibrating the Huff model and its extensions requires empirical location visit data. Many studies rely on labor‐intensive surveys. With the increasing availability of mobile devices, users in location‐based platforms share rich multimedia information about their locations at a fine spatio‐temporal resolution, which offers opportunities for business intelligence. In this research, we present a time‐aware dynamic Huff model (T‐Huff) for location‐based market share analysis and calibrate this model using large‐scale store visit patterns based on mobile phone location data across the 10 most populated US cities. By comparing the hourly visit patterns of two types of stores, we demonstrate that the calibrated T‐Huff model is more accurate than the original Huff model in predicting the market share of different types of business (e.g., supermarkets versus department stores) over time. We also identify the regional variability where people in large metropolitan areas with a well‐developed transit system show less sensitivity to long‐distance visits. In addition, several socioeconomic and demographic factors (e.g., median household income) that potentially affect people’s visit decisions are examined and summarized.     

Establishing Classification and Hierarchy in Populated Place Labeling for Multiscale Mapping for The National Map

Current standards for federal mapping call for use of the Geographic Names Information System (GNIS) point layer for placement of United States populated place labels. However, this point layer contains limited classification information and hierarchy information, resulting in problems of map quality for database-driven, multi-scale, reference mapping, such as maps served by The National Map Viewer from USGS. Database-driven mapping often relies simply on what labels fit best in the map frame. Our research investigates alternative sources for labeling populated places, including polygons defined by the U.S. Census Bureau, such as incorporated place, census designated place (CDP), and economic place. Within each of these polygon layers we investigate relevant attributes from the decennial and economic censuses, such as population for incorporated places and CDPs, and the number of employees for economic places. The data selected are available for the entire country to serve national mapping requirements. This combination of data allows a more refined classification of populated places on maps that better represents relative importance. Visual importance on maps through scale should derive from more than simply residential population, but also economic importance, though comparison is made to this simpler case. We differentiate a fourth category of GNIS populated place points, essentially “neighborhoods” and related features—which are not incorporated places, CDPs, nor economic places. Populated places in this fourth class do not have federally defined boundaries, necessitating an alternative method for determining hierarchy in label presentation through scale.     

Assessing the Veracity of Methods for Extracting Place Semantics from Flickr Tags

The volume and potential value of user generated content (UGC) is ever growing. Multiply sourced, its value is greatly increased by the inclusion of metadata that adequately and accurately describes that content – particularly if such data are to be integrated with more formal data sets. Typically, digital photography is tagged with location and attribute information that variously describe the location, events or objects in the image. Often inconsistent and incomplete, these attributes reflect concepts at a range of geographic scales. From a spatial data integration perspective, the information relating to “place” is of primary interest. The challenge therefore is in selecting the most appropriate tags that best describe the geography of the image. This article presents a methodology based on an information retrieval technique that separates out “place related tags” from the remainder of the tags. Different scales of geography are identified by varying the size of the sampling area within which the imagery falls. This is applied in the context of urban environments, using Flickr imagery. Empirical analysis is then used to assess the correctness of the chosen tags (i.e. whether the tag correctly describes the geographic region in which the image was taken). Logistic regression and Bayesian inference are used to attach a probability value to each place tag. The high correlation values achieved indicate that this methodology can be used to automatically select place tags for any urban region and thus hierarchically structure UGC in order that it can be semantically integrated with other data sources.     

Reference data enhancement for geographic information retrieval using linked data

Gazetteers are instrumental in recognizing place names in documents such as Web pages, news, and social media messages. However, creating and maintaining gazetteers is still a complex task. Even though some online gazetteers provide rich sets of geographic names in planetary scale (e.g. GeoNames), other sources must be used to recognize references to urban locations, such as street names, neighborhood names or landmarks. We propose integrating Linked Data sources to create a gazetteer that combines a broad coverage of places with urban detail, including content on geographic and semantic relationships involving places, their multiple names and related non‐geographic entities. Our final goal is to expand the possibilities for recognizing, disambiguating and filtering references to places in texts for geographic information retrieval (GIR) and related applications. The resulting ontological gazetteer, named LoG (Linked OntoGazetteer), is accessible through Web services by applications and research initiatives on GIR, text processing, named entity recognition and others. The gazetteer currently contains over 13 million places, 140 million attributes and relationships, and 4.5 million non‐geographic entities. Data sources include GeoNames, Freebase, DBPedia and LinkedGeoData, which is based on OpenStreetMap data. An analysis on how these datasets overlap and complement one another is also presented.     

Interplay between urban communities and human‐crowd mobility: A study using contributed geospatial data sources

An intense process of urbanization, witnessed particularly in the last decade, has stressed the need to comprehend human mobility behavior in urban settings. Although the emergence of contributed geospatial data (i.e., pervasive activity‐based data) has contributed to substantial progress toward understanding human activity, the relationship between human‐crowd mobility and the functional structure of a city is not yet well understood. In this context, the present research focuses on the intra‐urban origin–destination matrix modeling founded on a combination of two major crowdsourced datasets as well as the inclusion of urban communities’ structure. Specifically, the well‐known “radiation” and “PWO” models were modified through first, identifying the communities embedded in the cyberspace network then employing the identified hierarchical structure of the spatial‐interaction network for the formulation of the users’ movement network and second, imposing proper input variables including the telecommunication activity volume and check‐in frequency. The results obtained by various empirical analyses suggest that the modified community‐constrained origin–destination flow estimation models exhibit better performance levels than those of alternative conventional mobility models.     

基于百度迁徙数据的中国三大城市群网络结构中心性和对称性分析北大核心CSCD

本文首先提出了探测城市网络中心性和对称性特征的方法框架,具体包括3个“S”的维度,即强度、对称性和结构。然后分别对京津冀、长三角和珠三角城市群的人流迁徙网络进行了实证分析。结果表明:(1)三大城市群内的城市节点具有明显的层级特征,且人流迁徙呈现较大的对称性结构;(2)京津冀、长三角和珠三角分别呈现包围式、“>”形及“一核两翼”的核心-边缘结构;(3)京津冀呈典型的单中心结构,而长三角和珠三角则呈明显的多中心结构。最后结合上述分析结果,对于三大城市群的城市定位、区域规划给出了建议,旨在促进区域的协调发展和一体化集成。     

USE OF TRANSFORMED CARTOGRAPHIC IMAGES IN URBAN CONSTRUCTION STUDIES

The authors describe how maps compiled according to non-spatial “metrics” or systems of scaling can be used in the support of urban planning and construction efforts. In particular, maps scaled according to travel time instead of ordinary distance reveal inadequacies not only in urban mass transit, but permit assessment of the rationality of urban land use as a whole, by demonstrating the degree of effort that urban residents must expend to perform such basic activities as shopping, the journey to work, and access to essential services. A methodology for the compilation of such maps is described. Translated from: Izvestiya AN SSSR, seriya geograficheskaya, 1987, No. 1, pp. 100-105.     

DBSCAN空间聚类算法及其在城市规划中的应用

空间聚类是空间数据挖掘和知识发现的主要方法之一。DBSCAN算法可以从带有“噪声”的空间数据库中发现任意形状的聚类,是一种较好的聚类算法。本文介绍了DBSCAN算法的基本概念和原理,并应用GIS二次开发组件MapObjects予以了实现。然后,本文将该算法应用于城市规划中,对某城市中小学和商业网点等公共设施的分布进行了聚类分析,并根据聚类结果对城市规划设计规范中的某些条款进行了讨论。     

MapSafe: A complete tool for achieving geospatial data sovereignty

Sensitive geographic data are invaluable assets for the people to whom they belong and their disclosure should be decided by the sovereign data owner (SDO). Due to several high-profile data breaches and business models that commercialize user data, the need for new approaches to geoprivacy and data sovereignty has grown. We propose MapSafe, a client web application that first obfuscates datasets using donut masking or hexagonal binning, separately, and thereafter implements a multi-level encryption scheme that permits SDOs to share the final encrypted volume containing the geospatial information when they choose and at a level of detail which they are comfortable. The authenticity verification of the volume is facilitated by storing the hash value corresponding to the encrypted volume immutably on the Blockchain as a public record. Our approach places geoprivacy under data guardians'’ control, and its integration capabilities promote its adoption in existing and future geospatial web systems.     

Ecosystem functional assessment based on the “optical type” concept and self-similarity patterns: An application using MODIS-NDVI time series autocorrelation

Remote sensing (RS) time series are an excellent operative source for information about the land surface across several scales and different levels of landscape heterogeneity. Ustin and Gamon (2010) proposed the new concept of “optical types” (OT), meaning “optically distinguishable functional types”, as a way to better understand remote sensing signals related to the actual functional behavior of species that share common physiognomic forms but differ in functionality. Whereas the OT approach seems to be promising and consistent with ecological theory as a way to monitor vegetation derived from RS, it received little implementation.This work presents a method for implementing the OT concept for efficient monitoring of ecosystems based on RS time series. We propose relying on an ecosystem's repetitive pattern in the temporal domain (self-similarity) to assess its dynamics. Based on this approach, our main hypothesis is that distinct dynamics are intrinsic to a specific OT. Self-similarity level in the temporal domain within a broadleaf forest class was quantitatively assessed using the auto-correlation function (ACF), from statistical time series analysis. A vector comparison classification method, spectral angle mapper, and principal component analysis were used to identify general patterns related to forest dynamics. Phenological metrics derived from MODIS NDVI time series using the TIMESAT software, together with information from the National Forest Map were used to explain the different dynamics found.Results showed significant and highly stable self-similarity patterns in OTs that corresponded to forests under non-moisture-limited environments with an adaptation strategy based on a strong phenological synchrony with climate seasonality. These forests are characterized by dense closed canopy deciduous forests associated with high productivity and low biodiversity in terms of dominant species. Forests in transitional areas were associated with patterns of less temporal stability probably due to mixtures of different adaptation strategies (i.e., deciduous, marcescent and evergreen species) and higher functional diversity related to climate variability at long and short terms. A less distinct seasonality and even a double season appear in the OT of the broadleaf Mediterranean forest characterized by an open canopy dominated by evergreen-sclerophyllous formations. Within this forest, understory and overstory dynamics maximize functional diversity resulting in contrasting traits adapted to summer drought, winter frosts, and high precipitation variability.     

Bayesian networks and agent-based modeling approach for urban land-use and population density change: a BNAS model

Land-use change models grounded in complexity theory such as agent-based models (ABMs) are increasingly being used to examine evolving urban systems. The objective of this study is to develop a spatial model that simulates land-use change under the influence of human land-use choice behavior. This is achieved by integrating the key physical and social drivers of land-use change using Bayesian networks (BNs) coupled with agent-based modeling. The BNAS model, integrated Bayesian network–based agent system, presented in this study uses geographic information systems, ABMs, BNs, and influence diagram principles to model population change on an irregular spatial structure. The model is parameterized with historical data and then used to simulate 20 years of future population and land-use change for the City of Surrey, British Columbia, Canada. The simulation results identify feasible new urban areas for development around the main transportation corridors. The obtained new development areas and the projected population trajectories with the“what-if” scenario capabilities can provide insights into urban planners for better and more informed land-use policy or decision-making processes.     

Do choropleth maps linked with parallel coordinates facilitate an understanding of multivariate spatial characteristics?

Our study has three objectives. We want to investigate (1) whether choropleth maps linked with parallel coordinates help people understand the locations of vulnerable places and the factors making these places vulnerable, (2) whether sparklines that imitate the polylines from a parallel coordinate plot support the understanding of the information provided in that plot, and (3) whether a multiple-view geovisualization approach might be intuitive and useful also for nonexperts. Although we base our work on the functionalities available in the tool called ‘ViewExposed,’ we intend to outline more general conclusions on whether multiple linked views facilitate the understanding of multivariate spatial characteristics. An empirical study with 53 individuals was conducted to obtain insights on these objectives. Our task-based assessment considered the ways in which participants understood the dynamic linking capabilities. Some of the key findings are as follows: (1) even nonexpert users are able to use parallel coordinates linked with choropleth maps; however, only those with extensive inference-making abilities will do so efficiently; (2) parallel coordinates sparklines work as an independent component facilitating the understanding of multivariate data; and (3) the intuitive nature of a multiple-view geovisualization tool is not enough to encourage people to use it.     

RNDSI: A ratio normalized difference soil index for remote sensing of urban/suburban environments

Understanding land use land cover change (LULCC) is a prerequisite for urban planning and environment management. For LULCC studies in urban/suburban environments, the abundance and spatial distributions of bare soil are essential due to its biophysically different properties when compared to anthropologic materials. Soil, however, is very difficult to be identified using remote sensing technologies majorly due to its complex physical and chemical compositions, as well as the lack of a direct relationship between soil abundance and its spectral signatures. This paper presents an empirical approach to enhance soil information through developing the ratio normalized difference soil index (RNDSI). The first step involves the generation of random samples of three major land cover types, namely soil, impervious surface areas (ISAs), and vegetation. With spectral signatures of these samples, a normalized difference soil index (NDSI) was proposed using the combination of bands 7 and 2 of Landsat Thematic Mapper Image. Finally, a ratio index was developed to further highlight soil covers through dividing the NDSI by the first component of tasseled cap transformation (TC1). Qualitative (e.g., frequency histogram and box charts) and quantitative analyses (e.g., spectral discrimination index and classification accuracy) were adopted to examine the performance of the developed RNDSI. Analyses of results and comparative analyses with two other relevant indices, biophysical composition index (BCI) and enhanced built-up and bareness Index (EBBI), indicate that RNDSI is promising in separating soil from ISAs and vegetation, and can serve as an input to LULCC models.