Professor Ana Basiri

2025

1. Where England’s cities are growing: Evidence from big building footprint data and explainable AI

   Xinyi Yuan, Ziqi Li, Anahid Basiri, and 1 more author

   Habitat International, Sep 2025

   Abs

   Urban development is shaped by demographic and socio-economic factors, while simultaneously influencing these dynamics. Understanding these complex relationships is essential for informed urban planning, yet previous research has struggled with fine-scale analysis over large areas due to data and methodological limitations. This study overcomes these challenges by leveraging high-resolution building footprint data and eXplainable AI (XAI). Focusing on England between 2017 and 2023, we first quantify and map the extent of new urban development and further model it as a function of population density, ethnic composition, and the Index of Multiple Deprivation (IMD) using machine-learning algorithms. We then interpret the best-performing model with SHAP values and reveal a substantial nonlinear correlation between these demographic and socio-economic factors and new urban development. This analytical framework offers a novel, scalable, interpretable approach to fine-grained urban analysis, and, for the first time, provides a nationwide quantitative assessment of how population density, ethnic composition, and deprivation jointly shape urban development in England, thereby supporting evidence-based and equitable planning and policymaking.

2. Exploring a diagnostic test for missingness at random

   Dominick Sutton, Anahid Basiri, and Ziqi Li

   Mathematics, May 2025

   Abs

   Missing data remain a challenge for researchers and decision-makers due to their impact on analytical accuracy and uncertainty estimation. Many studies on missing data are based on randomness, but randomness itself is problematic. This makes it difficult to identify missing data mechanisms and affects how effectively the missing data impacts can be minimized. The purpose of this paper is to examine a potentially simple test to diagnose whether the missing data are missing at random. Such a test is developed using an extended taxonomy of missing data mechanisms. A key aspect of the approach is the use of single mean imputation for handling missing data in the test development dataset. Changing this to random imputation from the same underlying distribution, however, has a negative impact on the diagnosis. This is aggravated by the possibility of high inter-variable correlation, confounding, and mixed missing data mechanisms. The verification step uses data from a high-quality real-world dataset and finds some evidence-in one case-that the data may be missing at random, but this is less persuasive in the second case. Confidence in these results, however, is limited by the potential influence of correlation, confounding, and mixed missingness. This paper concludes with a discussion of the test’s merits and finds that sufficient uncertainties remain to render it unreliable, even if the initial results appear promising.

3. Diagnosing spatial and temporal biases of OSM contributors: identifying differences between gender and age from an online survey

   Hyesop Shin, Zoe Garden, Guy Solomon, and 1 more author

   Annals of the American Association of Geographers, Apr 2025

   Abs

   Citizen science projects are open and available to anyone to contribute data. However, the literature concerning volunteered geographic information (VGI) has demonstrated significant demographic participation biases across time and space. Understanding the significance and impacts of these biases is challenging due to privacy concerns, which lead to the (pseudo-)anonymity of contributors. Using a sample of 265 users, this paper statistically analyses edits to the crowdsourced mapping platform OpenStreetMap (OSM) to examine the impact of gender and age on spatial and temporal contribution patterns. We find that men aged in the Others group (i.e. below 25 or over 54) made more contributions during the week and at weekends than those in the Economically Active age group (i.e. age 25-54). Using the Kruskal-Wallis test to compare temporal contributions between gender groups, the Economically Active group showed a significant gender difference on both weekdays and weekends, as well as the hours of the day, with men making more contributions than women regardless of age category. Men in the Others group made the most contributions overall. Calculating the Simpson Index of Diversity for user edits reveals that women have more limited spatial interests (i.e. they contribute to fewer countries) than their male counterparts, suggesting particular spatial preferences by gender.

4. Geospatial Data Quality in the Era of Generative AI: Can we Trust the Geographic Information Produced by Large Language Models?

   Joseph Shingleton, and Ana Basiri

   Feb 2025
5. Construction enthusiasts versus demolition giants: insights from building footprint data in England

   Xinyi Yuan, Ziqi Li, Ana Basiri, and 1 more author

   Environment and Planning B: Urban Analytics and City Science, Jan 2025

   Abs

   This study uses building footprint data from the Ordnance Survey MasterMap to analyze construction and demolition activities across England from 2017 to 2023. By comparing the Topographic Object Identifiers (TOIDs) of each building between years, we identified newly constructed and demolished buildings, quantified changes, and used the bivariate color maps to visualize spatial patterns across England and within its five major cities. The study highlights the effectiveness of building footprint data in providing insights into urban changes and development trajectories, which are vital for urban planners and policymakers to understand dynamic urban processes and inform decision-making toward sustainable urban development.

2024

1. Is There a Spatial Element to ’Don’t know’ and ’Prefer not to say’ Survey Item Nonresponses?

   Dominick Sutton, and Ana Basiri

   Sep 2024

   Abs

   This talk will provide evidence for a difference between ’Don’t know’ and ’Prefer not to say’ nonresponses in a survey. The study was conducted on the FCA ’Financial Lives Survey, 2020’, sourced from the Consumer Data Research Centre. The variable examined was ’Annual Household Income’ for England. Research has found that these two nonresponses can have different meanings and encode different information. In financial surveys ’Don’t know’ respondents were found to be different to those refusing to answer and those providing informative answers. Accordingly, these nonresponses should be treated as different categories, not just missing data. Indeed, some argue that ’Don’t know’ answers should be treated as a valid response. The analysis found evidence of clear differences between the two nonresponses. ’Prefer not to say’ respondents were more likely to withhold responses to other questions (answering ’Prefer not to say’ if possible) than were ’Don’t know’ respondents (e.g. for Gender, 48% versus 14%; Marital Status 52% versus 14%; Ethnicity, 61% versus 13%). In line with prior research there was a higher ’Prefer not to say’ nonresponse rate for those with more education, this time three times higher. Moran’s I was used to test for spatial characteristics. While both were found to have a spatial component, it was stronger for ’Prefer not to say’. Geographically weighted regression (GWR) also found evidence of a spatial component for both, there being a stronger component for ’Prefer not to say’. As a final test a crude imputation of these nonresponses was used to calculated annual household income and compared to ONS data. Using mean squared error against ONS data as a fit criterion, GWR-based coefficients improved the fit compared to complete case analysis. ’Prefer not to say’ data generated a closer fit than ’Don’t know’, indicating both a higher spatial and informational content.

2. Enhancing Toponym Identification: Leveraging Topo-BERT and Open-source Data to Differentiate Between Toponyms and Extract Spatial Relationships

   Joseph Shingleton, and Ana Basiri

   May 2024

   Abs

   Geoparsing, the process of linking locations within text to sets of geographic coordinates, plays an important role in the extraction and analysis of information from unstructured textual data. With the rapid growth in availability of user-generated data from online sources, there is increasing demand for reliable geoparsing methods. Central to many of these methods is the accurate identification of toponyms within text. For some applications, however, simple identification of toponyms is insufficient. Problems which require the association of a piece of text containing multiple toponyms to a singular location require a more nuanced approach. In this paper, we show that a transformer based deep learning model, is able to identify the subject toponym within a given text, and classify other toponyms in terms of their spatial relationship with the subject. We curate a dataset of text taken from Wikipedia pages representing 5252 locations, and use OpenStreetMap data to classify toponyms within the text in terms of their spatial relationship with the subject of each article. This dataset is then used to train a transformer based deep-learning model. On a human labelled test set, our model achieves an F1 score of 0.916 when identifying the subject toponym, and 0.884 and 0.793 when identifying toponyms representing parent and child locations of the subject, respectively. We also consider the more complex adjacent and crossing relationships - with the model achieving F1 scores of 0.548 and 0.704 in these categories, respectively.

3. Where is the News? Improving Toponym Identification and Differentiation in Online News

   Joseph Shingleton, and Ana Basiri

   May 2024

   Abs

   Understanding the geographical context of unstructured textual data is a key challenge in information extraction. In many applications, however, simple identification of toponyms is insufficient and can often lead to ambiguities in the extracted information. One such application is in the geolocation of online news - where a single article may mention multiple locations, with only one location referring to the article’s subject. In this paper, we present a transformer based model, trained to identify the subject toponyms of news articles. Further, our model identifies likely parents of the subject toponym, potentially helping to improve later geolocation tasks. Our model is able to identify the subject of anarticle with an F1-score of 0.760 when tested on a human-tagged test dataset.

4. Advancing Human Activity Recognition Using Ultra-Wideband Channel Impulse Response Snapshots

   Yu WANG, and Ana Basiri

   Apr 2024

   Abs

   Human Activity Recognition (HAR) in fields like security surveillance and healthcare benefits from the non-intrusive, cost-effective nature of wireless signal technologies. Ultra-Wideband (UWB) signals, with their strong interference resistance and excellent time-domain resolution, are still under-researched in this area. In this work, we explore the potential of UWB signals in conducting HAR tasks. We collect a diverse UWB Channel Impulse Response (CIR) dataset in real-world indoor settings, incorporating recordings of six activities performed by volunteers and background CIR data. Our novel approach treats each CIR as a unique snapshot, capturing specific activities and creating a dynamic activity representation through concatenated feature vectors. A thorough grid search identified optimal parameters for constructing these vectors. In applying eleven different learning models for classification analysis on the dataset, it is generally observed that deep learning methods yielded enhanced classification accuracy compared to traditional machine learning techniques. Our research not only demonstrates the potential of UWB in HAR but also provides insights into effective feature vector creation and model performance.

5. Evaluating Geotemporal Behaviours in OpenStreetMap Contributors

   Guy Solomon, Dominick Sutton, Merve Polat Kayali, and 3 more authors

   Mar 2024

   Abs

   Volunteered Geographic Information (’VGI’) and crowdsourcing are integral for projects such as OpenStreetMap (’OSM’). However, despite the wide use of OSM as one of the most successful crowdsourcing platforms, the under-representation of certain demographic groups amongst those who contribute information may ultimately mean this information favours the interests of some groups over others. This can result in misleading conclusions for analyses conducted on the basis of these data. This paper connects OSM user contributions to demographic data collected via a survey. It shows that, in relation to geographic diversity of contributions, men and women demonstrate distinct trends over time. It then considers the extent to which this observed pattern can be seen as influenced by the COVID-19 pandemic. In this regard, it concludes that there does not appear to be a distinct ’pandemic effect’ divergent from longer-term trends.

6. The impact of postures and moving directions in fire evacuation in a low-visibility environment

   Jingjing Yan, Gengen He, Anahid Basiri, and 2 more authors

   Sensors, Mar 2024

   Abs

   Walking speed is a significant aspect of evacuation efficiency, and this speed varies during fire emergencies due to individual physical abilities. However, in evacuations, it is not always possible to keep an upright posture, hence atypical postures, such as stoop walking or crawling, may be required for survival. In this study, a novel 3D passive vision-aided inertial system (3D PVINS) for indoor positioning was used to track the movement of 20 volunteers during an evacuation in a low visibility environment. Participants’ walking speeds using trunk flexion, trunk-knee flexion, and upright postures were measured. The investigations were carried out under emergency and non-emergency scenarios in vertical and horizontal directions, respectively. Results show that different moving directions led to a roughly 43.90% speed reduction, while posture accounted for over 17%. Gender, one of the key categories in evacuation models, accounted for less than 10% of the differences in speed. The speeds of participants under emergency scenarios when compared to non-emergency scenarios was also found to increase by 53.92-60% when moving in the horizontal direction, and by about 48.28-50% when moving in the vertical direction and descending downstairs. Our results also support the social force theory of the warming-up period, as well as the effect of panic on the facilitating occupants’ moving speed.

2023

1. The Importance of Communication with Missing Data with Colours of Map for Decision-Making

   Merve Polat Kayali, and Ana Basiri

   Dec 2023

   Abs

   Missing data is a growing issue in data-driven studies. This study focuses on measuring the importance of communication with missing data and the perception of missing data with colours to find better solutions to potential problems in research with datasets with missing data. Understanding the significance of the interaction with missingness may help us to improve the visualisation of missing geospatial data. We have created a survey that includes some sentences, terms, graphs, maps, and colours we can encounter in daily life. However, all these expressions have some missingness in measuring participants’ perceptions and decision-making mechanisms. One hundred-one respondents from different demographics, all from the UK, participated in the survey. It aims to improve the visualisation of missinggeospatial data using the results of this research for future studies. But, in this paper, we specifically focus on the perception of representation of missingness with different colours on maps. We observed some differences depending on the gender, age, and education categories of the participants. In the gender-aged analysis of this study’s results, a significant difference was observed, especially in the perception of a specific colour.

2. Assessing the Relationship Between Socio-Demographic Characteristics and OpenStreetMap Contributor Behaviours

   Dominick Sutton, Guy Solomon, Xinyi Yuan, and 3 more authors

   Nov 2023

   Abs

   ’Volunteered Geographic Information’ (VGI) has particular importance - in part - for its democratisation of geographic information. However, some recent research has suggested that despite being publicly open, several successful VGI platforms have under-representation of particular socio-demographic groups, which may lead to biases in the types of information contributed. This paper examines the relationship between demographic characteristics and user contributions to OpenStreetMap (OSM), one of the most successful examples of a project reliant on VGI. It demonstrates statistically significant differences in the information provided by users of different genders, ages, and education-levels. Differences between the demographic characteristics of OSM contributors and the underlying population are therefore likely to be reflected in the VGI contained in OSM.

3. Scalable 3D mapping of cities using computer vision and signals of opportunity

   Anahid Basiri, Terence Lines, and Miguel Fidel Pereira

   International Journal of Geographical Information Science, Jul 2023

   Abs

   Three-dimensional (3D) maps are used extensively in a variety of applications, from air and noise pollution modelling to location-based services such as 3D mapping-aided Global Navigation Satellite Systems (GNSS), and positioning and navigation for emergency service personnel, unmanned aerial vehicles and autonomous vehicles. However, the financial cost associated with creating and updating 3D maps using the current state-of-the-art methods such as laser scanning and aerial photogrammetry are prohibitively expensive. To overcome this, researchers have proposed using GNSS signals to create 3D maps. This paper advances that family of methods by proposing and implementing a novel technique that avoids the difficult step of directly classifying GNSS signals into line-of-sight and non-line-of-sight classes by utilising edge detection techniques adapted from computer vision. This prevents classification biases and increases the range of environments in which GNSS-based 3D mapping methods can be accurately deployed. Being based on the patterns of blockage and attenuation of GNSS signals that are freely and globally available to receive by many mobile phones, makes the proposed technique a free, scalable and accessible solution. This paper also identifies some key indicators affecting data collection scalability and efficiency of the 3D mapping solution.

4. Learning from data with structured missingness

   Robin Mitra, Sarah F. McGough, Tapabrata Chakraborti, and 17 more authors

   Nature Machine Intelligence, Jan 2023

   Abs

   Missing data are an unavoidable complication in many machine learning tasks. When data are ’missing at random’ there exist a range of tools and techniques to deal with the issue. However, as machine learning studies become more ambitious, and seek to learn from ever-larger volumes of heterogeneous data, an increasingly encountered problem arises in which missing values exhibit an association or structure, either explicitly or implicitly. Such ’structured missingness’ raises a range of challenges that have not yet been systematically addressed, and presents a fundamental hindrance to machine learning at scale. Here we outline the current literature and propose a set of grand challenges in learning from data with structured missingness.

2022

1. A Participatory Approach to Develop Missing Geospatial Data Visualisation

   Merve Polat Kayali, and Ana Basiri

   In European Cartographic Conference - EuroCarto 2022, Sep 2022

   Abs

   Abstract available from publisher’s website.

2. Classification of Missing Geospatial Data from Structure and Mechanism Perspective

   Merve Polat Kayali, and Ana Basiri

   Apr 2022

   Abs

   Data-centric science, data-empowered society, and policymaking based on data can suffer from flawed conclusions if data are representative, biased, or unavailable. This paper focuses on missingness for which the common mitigation and handling strategies is a deletion or single imputation. However, understanding the reasons causing the missingness can help to understand phenomena better. Distinguishing the different types of missingness help us to develop and implement new imputation approaches, sampling strategies and output uncertainty quantification. In this paper, using missing data mechanism and structure a new taxonomy has been created to classify the causalities of missing geospatial data.

3. Geographic Biases in OSM Contributions: How do the Geographic Extent of Contributions Differ Among Demographic Groups?

   Hyesop Shin, and Ana Basiri

   Jan 2022

   Abs

   OpenStreetMap (OSM) is one of the most successful participatory mapping platforms for creating and editing geographic data. Despite being technically open and available to anyone to contribute, there is a significant demographic participation bias in the contributors of OSM, particularly from their spatial patterns on OSM. This study presents how geo-demographic biases of OSM contributions can be measured using the users’ ‘number of contributed countries’ and their ‘changesets’. We found that working-age male participants have a larger geographic extent of entries compared to their female counterparts. However, this once again varied significantly by the age groups. Both variables were employed as proxies to estimate the individual has a propensity to contribute locally or internationally. Future studies could add temporal aspects to compare the temporal patterns between demographic groups to give a multi-dimensional insight for VGI studies.

4. Missing data as data

   Anahid Basiri, and Chris Brunsdon

   Patterns, Jan 2022

   Abs

   Our "digified" lives have provided researchers with an unprecedented opportunity to study society at a much higher frequency and granularity. Such data can have a large sample size but can be sparse, biased, and exclusively contributed by the users of the technologies. We look at the increasing importance of missing data and under-representation and propose a new perspective that considers missing data as useful data to understand the underlying reasons for missingness and that provides a realistic view of the sample size of large but under-represented data.

5. The Impact of Built Environment on Bike Commuting: Utilising Strava Bike Data and Geographically Weighted Models

   Hyesop Shin, Costanza Cagnina, and Ana Basiri

   Jan 2022

   Abs

   Active travel provides significant public health benefits including improving physical and mental health and air quality. Given the geography of congested roads, availability of required infrastructure and cost of transportation in cities, promoting active travel, including cycling, can be a good solution for commuting within built environments. Having a better understanding of the key drivers that may influence bike ridership can help with designing cities that accommodate cyclists’ needs for healthier citizens. This paper examines the built environment features that may affect commuting cyclists. We respectively employ Ordinary Linear Square (OLS) regression and Geographically Weighted Regression (GWR) for 136 Intermediate Zones of the city of Glasgow, UK. The results of GWR show that the significant local variation in green areas suggests that even though the global regression showed a negative association between the greenness and commute cycling, over half of the IZ areas had a strong positive association with the green areas. Building height and Public Transport Availability Index show geographic patterns where the residuals are fairly stationary across the study area with some clusters of high residuals. Performance wise, the results from GWR provided an R2 of 0.73 which was higher than OLS at 0.3. Our results can provide insights into how to use crowdsourced cycling data when there are spatially and temporally limited resources available.

2021

1. 3D map creation using crowdsourced GNSS data

   Terence Lines, and Anahid Basiri

   Computers, Environment and Urban Systems, Sep 2021

   Abs

   3D maps are increasingly useful for many applications such as drone navigation, emergency services, and urban planning. However, creating 3D maps and keeping them up-to-date using existing technologies, such as laser scanners, is expensive. This paper proposes and implements a novel approach to generate 2.5D (otherwise known as 3D level-of-detail (LOD) 1) maps for free using Global Navigation Satellite Systems (GNSS) signals, which are globally available and are blocked only by obstacles between the satellites and the receivers. This enables us to find the patterns of GNSS signal availability and create 3D maps. The paper applies algorithms to GNSS signal strength patterns based on a boot-strapped technique that iteratively trains the signal classifiers while generating the map. Results of the proposed technique demonstrate the ability to create 3D maps using automatically processed GNSS data. The results show that the third dimension, i.e. height of the buildings, can be estimated with below 5 metre accuracy, which is the benchmark recommended by the CityGML standard.

2. Predictably unpredictable

   Anahid Basiri

   Journal of Navigation, Jul 2021

   Abs

   No abstract available.

3. Effectiveness modelling of digital contact-tracing solutions for tackling the COVID-19 pandemic

   Viktoriia Shubina, Aleksandr Ometov, Anahid Basiri, and 1 more author

   Journal of Navigation, Jul 2021

   Abs

   Since the beginning of the coronavirus (COVID-19) global pandemic, digital contact-tracing applications (apps) have been at the centre of attention as a digital tool to enable citizens to monitor their social distancing, which appears to be one of the leading practices for mitigating the spread of airborne infectious diseases. Many countries have been working towards developing suitable digital contact-tracing apps to allow the measurement of the physical distance between citizens and to alert them when contact with an infected individual has occurred. However, the adoption of digital contact-tracing apps has faced several challenges so far, including interoperability between mobile devices and users’ privacy concerns. There is a need to reach a trade-off between the achievable technical performance of new technology, false-positive rates, and social and behavioural factors. This paper reviews a wide range of factors and classifies them into three categories of technical, epidemiological and social ones, and incorporates these into a compact mathematical model. The paper evaluates the effectiveness of digital contact-tracing apps based on received signal strength measurements. The results highlight the limitations, potential and challenges of the adoption of digital contact-tracing apps.

4. Identifying urban functional areas and their dynamic changes in Beijing: using multiyear transit smart card data

   Zijia Wang, Haixu Liu, Yadi Zhu, and 5 more authors

   Journal of Urban Planning and Development, Jun 2021

   Abs

   A growing number of megacities have been experiencing changes to their landscape due to rapid urbanization trajectories and travel behavior dynamics. Therefore, it is of great significance to investigate the distribution and evolution of a city’s urban functional areas over different periods of time. Although the smart card automated fare collection system is already widely used, few studies have used smart card data to infer information about changes in urban functional areas, particularly in developing countries. Thus, this research aims to delineate the dynamic changes that have occurred in urban functional areas based on passengers’ travel patterns, using Beijing as a case study. We established a Bayesian framework and applied a Gaussian mixture model derived from transit smart card data in order to gain insight into passengers’ travel patterns at station level and then identify the dynamic changes in their corresponding urban functional areas. Our results show that Beijing can be clustered into five different functional areas based on the analysis of corresponding transit station functions: multimodal interchange hub and leisure area; residential area; employment area; mixed but mainly residential area; and mixed residential and employment area. In addition, we found that urban functional areas have experienced slight changes between 2014 and 2017. The findings can be used to inform urban planning strategies designed to tackle urban spatial structure issues, as well as guiding future policy evaluation of urban landscape pattern use.

5. A contextual hybrid model for vessel movement prediction

   Saeed Mehri, Ali Asghar Alesheikh, and Ana Basiri

   IEEE Access, Mar 2021

   Abs

   Predicting the movement of the vessels can significantly improve the management of safety. While the movement can be a function of geographic contexts, the current systems and methods rarely incorporate contextual information into the analysis. This paper initially proposes a novel context-aware trajectories’ simplification method to embed the effects of geographic context which guarantees the logical consistency of the compressed trajectories, and further suggests a hybrid method that is built upon a curvilinear model and deep neural networks. The proposed method employs contextual information to check the logical consistency of the curvilinear method and then, constructs a Context-aware Long Short-Term Memory (CLSTM) network that can take into account contextual variables, such as the vessel types. The proposed method can enhance the prediction accuracy while maintaining the logical consistency, through a recursive feedback loop. The implementations of the proposed approach on the Automatic Identification System (AIS) dataset, from the eastern coast of the United States of America which was collected, from November to December 2017, demonstrates the effectiveness and better compression, i.e. 80% compression ratio while maintaining the logical consistency. The estimated compressed trajectories are 23% more similar to their original trajectories compared to currently used simplification methods. Furthermore, the overall accuracy of the implemented hybrid method is 15.68% higher than the ordinary Long Short-Term Memory (LSTM) network which is currently used by various maritime systems and applications, including collision avoidance, vessel route planning, and anomaly detection systems

6. Inclusivity and diversity of navigation services

   Anahid Basiri

   Journal of Navigation, Mar 2021

   Abs

   Our car seats, watch straps, seatbelts, and gym equipment are all adjustable because of the ’jaggedness principle’, which basically says that nobody is average. If you gather many people or things and collect data about different aspects and features of them, you will find that none of the very people or items can match perfectly with the ’average’. That is why we have watch adjustment holes and adjustable car seats. But this also means, there will be no average individual with size of the average measurements. But if there is no average person to use the technologies, then how can we design devices and technologies that can be used by everybody? What would happen if, for example, we designed navigation devices, path-finding services and assistive technologies for an ’average user’ and then expected everybody to use them? Would it be as dangerous as a car without an adjustable seatbelt, or is it just a minor difference that can be ignored by our forgiving end-user? This editorial looks at the importance of human factors, inclusivity and diversity-by-design in navigation services and will look at some examples where jaggedness principle has introduced challenges and problems to our navigation services.

7. How fast can our horses go? Measuring the quality of positioning technologies

   Anahid Basiri

   Journal of Navigation, Jan 2021

   Abs

   Whether Henry Ford or someone else gave us this famous quote, ’If I had asked people what they wanted, they would have said faster horses’, we may agree that it implies there is a limit to what we can expect from the performance of an existing solution. Science and technology always try to push the boundaries and ’improve’; improving the quality of our lives or improving the quality of technologies. We, as researchers in the area of navigation, are no exception; we want to improve the quality of navigation services. And there are many ways to do so, and challenges and limitations to those attempts. Some researchers look to improve the accuracy, the reliability, the integrity through different approaches. Some try to reduce or model noise, some try to minimise human error, and some use novel techniques and algorithms for better prediction. Of course, when ’our horses cannot go any faster’ and there is not much space for improvement for a certain technology or service, researchers may come up with a completely new solution, such as an automobile. Almost all new technologies go through the same exploration period; at the beginning, we want to see how and if it works so we try simple tasks, but then we become more ambitious (or greedier!) and so we introduce it to more difficult challenges until it hits the breaking point. At this point, curious researchers and inventors try to push the boundaries and make the technology better, and if improvement is not possible, they build (invent) a new solution. But what is the ’quality’ that many of us want to improve? How the quality of a technology or service can be measured in the first place?

2019

1. Simulating and Modeling the Signal Attenuation of Wireless Local Area Network for Indoor Positioning

   Terence Lines, and Anahid Basiri

   Nov 2019

   Abs

   Location is a key filter for mobile services, including navigation or advertising. However, positioning and localization inside buildings and in indoor spaces, where users spend most of their time and where the signals of the most widely-used positioning system, i.e. Global Navigation Satellite Systems such as GPS (Global Positioning System), are not available, can be challenging. In this regard, Wireless Local Area Networks (WLAN), e.g. Wi-Fi, can be used for positioning purposes by using a WLAN-enabled device, e.g. a smartphone, to measure and match the Received Signal Strength (RSS) of a signal broadcast by an access point. The challenges of this approach are that accurate maps of RSS are required, and that measuring RSS can be affected by many factors, including the dynamics of the environment and the orientation and type of a device. This paper provides a path-loss model to produce RSS maps automatically from floor plans and introduces an agent-based simulation approach to investigate different positioning methods. This provides a pathway to reduce the time and effort associated with WLAN positioning research.

2. Signal Attenuation Modelling in WLAN Positioning

   Terry Lines, and Anahid Basiri

   Oct 2019

   Abs

   Wireless Local Area Networks (WLAN), as the most widely used indoor positioning technology, can localise users by measuring the Received Signal Strength (RSS) from multiple Access Points (AP). The challenges of this approach are that measuring RSS can be easily affected by several parameters, including how the users hold the device, e.g. device orientation, and that accurate maps of RSS are required. This paper (A) introduces a bell-curve model of signal attenuation from orientation, allowing more accurate RSS measurement, and (B) identifies collinearity issues with a path-loss model used to automatically create RSS maps, suggesting a simpler and more robust alternative.

3. Crowdsourced geospatial data quality: challenges and future directions

   Anahid Basiri, Muki Haklay, Giles Foody, and 1 more author

   International Journal of Geographical Information Science, Oct 2019

   Abs

   No abstract available.

2017

1. Indoor location based services challenges, requirements and usability of current solutions

   Anahid Basiri, Elena Simona Lohan, Terry Moore, and 5 more authors

   Computer Science Review, May 2017

   Abs

   Indoor Location Based Services (LBS), such as indoor navigation and tracking, still have to deal with both technical and non-technical challenges. For this reason, they have not yet found a prominent position in people’s everyday lives. Reliability and availability of indoor positioning technologies, the availability of up-to-date indoor maps, and privacy concerns associated with location data are some of the biggest challenges to their development. If these challenges were solved, or at least minimized, there would be more penetration into the user market. This paper studies the requirements of LBS applications, through a survey conducted by the authors, identifies the current challenges of indoor LBS, and reviews the available solutions that address the most important challenge, that of providing seamless indoor/outdoor positioning. The paper also looks at the potential of emerging solutions and the technologies that may help to handle this challenge.

2016

1. Quality assessment of OpenStreetMap data using trajectory mining

   Anahid Basiri, Mike Jackson, Pouria Amirian, and 5 more authors

   Geo-Spatial Information Science, May 2016

   Abs

   OpenStreetMap (OSM) data are widely used but their reliability is still variable. Many contributors to OSM have not been trained in geography or surveying and consequently their contributions, including geometry and attribute data inserts, deletions, and updates, can be inaccurate, incomplete, inconsistent, or vague. There are some mechanisms and applications dedicated to discovering bugs and errors in OSM data. Such systems can remove errors through user-checks and applying predefined rules but they need an extra control process to check the real-world validity of suspected errors and bugs. This paper focuses on finding bugs and errors based on patterns and rules extracted from the tracking data of users. The underlying idea is that certain characteristics of user trajectories are directly linked to the type of feature. Using such rules, some sets of potential bugs and errors can be identified and stored for further investigations.