Research on handheld LiDAR data has recently been proliferated due to the emergence of digital twins and indoor mapping. However, most of the existing studies cannot be reproduced in another computational environment. Computational reproducibility requires data, code/software, and computational environment (e.g. versions, settings, etc.) to be openly available. Although there are an increasing number of researches that contribute towards open data, there are still few studies investigating the remaining two aspects. One of the common tasks in digital twin research is the estimation of indoor space areas. This paper contributes to the computational reproducibility of estimating the area of indoor spaces on a handheld LiDAR dataset using the DBSCAN algorithm. The collected dataset -representing the Geomatics Engineering Department of Hacettepe University, code, and the computational environment was made openly available to satisfy the requirements of computational reproducibility. Three different experiments have been carried out: i) identification of the optimal DBSCAN parameter values for a single indoor space, ii) evaluating to what extent these values are applicable to other rooms, and iii) investigating the effect of room enter/exit times on the estimated room sizes. The main finding of this paper is that the simple consideration of an open-door, which reduces data collection time, the uncertainty of a wall’s coordinates, and imperfect choice of DBSCAN parameters, may substantially increase the estimated indoor space size ranging between approximately 40% to 300%. Consequently, relying solely on the DBSCAN algorithm for indoor space area estimation should not be considered as a valid approach.

Automatic building footprint detection from aerial LiDAR point cloud data is an important research area that has use in various research areas ranging from city modeling to disaster management. Various steps must be carried out to obtain building footprints represented as polygons. Specifically, Digital Surface Model (DSM) and Digital Terrain Model (DTM) should be estimated, and then different filters (e.g. adaptive threshold, morphological open/close) and indices (e.g. terrain ruggedness index) are used to remove noise and vegetation. However, there has been lack of open-source software to realize these steps. Consequently, computational reproducibility of the findings has remained limited. The 2022 ACM SigSpatial GIS Cup contributed to this issue by openly providing aerial LiDAR point cloud data for 20 test sites, and evaluated the submissions performance by relying on a modified Intersection over Union (IoU) metric. The aim of this demo paper is to describe the development of a computationally reproducible framework to realize the GIS Cup's objective. First, the developed Python package lasbuildseg is described, and then its use on five different test sites from the GIS Cup are evaluated.

With recent developments in information and communication technologies (ICT), it has become possible to shop for products that require 'same day delivery'. Individuals can access food products suitable for instant consumption at any time of the day with online supermarket applications, without changing their physical environment. It has been seen that the sociodemographic characteristics of individuals, their tendency to use the internet, and the urban factors in the environment they live in, are the main reasons for choosing online shopping. In the study, the relationship between the online supermarket shopping preferences of individuals in the district of Çankaya, and the location and accessibility of retail markets, was investigated. Findings were obtained by analyzing online survey data and spatial data. The chi-square test, a descriptive statistics test, was applied to the data obtained by the questionnaire. As a result of the analysis, it was determined which variables were statistically significantly when correlated to online grocery shopping. Service areas were created for pedestrians and vehicles by using the market locations in the district. Accessibility to markets was analyzed with the help of slope and transportation networks in the district. At the end of the study, it was concluded from the survey data that there is a statistically significant relationship between online grocery shopping preferences, the gender of the participants, their years of residence in their neighborhoods, their difficulties in accessing markets and carrying packages, and curfews. As a result of the spatial analyzes within the scope of market service areas and accessibility, it has been determined that there are no markets that individuals can reach by walking in Ahlatlıbel, Alacaatlı, Beytepe, Konutkent and Koru neighborhoods, where residential areas are located in the southwest direction of the district of Çankaya. It has been determined that some of the locations of the markets located in Çayyolu, Dodurga, Hilal, Keklikpınarı, Kırkkonaklar and Yaşamkent neighborhoods in the district have a slope of 20 to 35 percent.

The widespread use of Global Navigation Satellite System (GNSS) receivers for monitoring people, vehicles, and animals has generated large amounts of space–time point data. One of the important analyses of such data is hot spot detection. This could be realised by first aggregating the data into a Space Time Cube (STC), and then applying statistics like Getis–Ord Gi∗ or local Moran's I on the cells of the STC. Existing open-source software to realise this either focuses on the spatial aspect of the phenomenon or does not provide a Graphical User Interface (GUI). This paper proposes, i) a QGIS plugin, and ii) a Python package to determine the localised clusters in space and time by aggregating data into an STC. While the former provides a GUI that is easy to use, the latter is suitable for sensitivity analysis. The experiments on the openly available New York City taxi data demonstrate that the detected hot spots change depending on whether the number of passengers is used as the weight for each data point, and the statistic (i.e. Getis–Ord Gi∗ or local Moran's I). The main advantages of the developed software are two-fold. First, they contribute to free and open-source software for geospatial (FOSS4G). Second, users of varying expertise can utilise them on a potpourri of use cases ranging from transportation to criminology.

In this project, a literature review on smart campus navigation has been made within the scope of smart cities, which has been an academic research topic recently, and it is aimed to implement such a study on a web page.There are buildings within the campus structure, passages connecting the departments, stairs and side roads unknown to visitors who are unfamiliar with the campus.Navigation infrastructure was designed and developed for four different users.These are i) pedestrians, ii) cars, iii) bicycles and iv) disabled individuals.In the designed system, transportation to the desired destination can be provided in the shortest way.For those who want to use the navigation system by car, the full/empty data that can be dynamically collected from the car parks has been evaluated.The algorithm of the smart parking system works to direct the user to another parking lot with the nearest empty space if the parking lot at the destination is full.The mathematical model of finding the shortest path, which is the working principle of the algorithm in the smart parking system, is provided with the help of the Djikstra algorithm.The algorithms designed and developed, the data of the road to be used to create the route, in accordance with the spatial and geographical information standards, are transferred to the web interface and presented to the users at the address map.hacettepe.edu.tr.Work on improving the interface continues based on user experiences.

The use of drones in an urban environment, especially in the context of logistics, is expected to increase substantially in the coming years. This emerging technology poses a potential threat to other aircraft in the airspace, as well as to human beings and other assets on the ground. In order to mitigate these risks, it is vital to know where an automated drone is, where it is allowed to fly and when. Spatial Data Infrastructures (SDI) can be used as a useful tool to standardize automated drone flight data and facilitate the integration of drones into an airspace in a safe manner. This paper proposes an extension to the Infrastructure for Spatial Information in the European Community (INSPIRE). The proposed model is based on the relevant regulations of European Union (EU) and the Unites States of America (USA), and considers the state-of-the-art literature on drone logistics in an urban environment.

Trajectory data constitute location of objects at specified time intervals. The continuous availability of GNSS signals, or discrete availability of sensor systems such as license plate recognition cameras are used to generate trajectory data. Consequently, in a smart city context, big trajectory data are being generated on a daily basis. The analysis of big trajectory data entails the use of a distributed environment to conduct analysis, and at least two data sources. The literature review conducted in this paper shows that the two Vs of big data, Volume and Variety, may not be satisfied since researchers usually rely on a centralised computing environment, and analyse data coming from a single data source. Out of the 17 papers published from 2020 in Scopus, only five of them relied on a distributed computing environment, and two of them utilised more than one data source.

Geospatial software developers often rely on Git to collaborate with each other and manage source code in an efficient way. Yet, most GIS programming courses do not prepare students for such a work environment. This article proposes a typology that a GIS programming course could follow consisting of three components: group organization, project and evaluation. Based on the typology, a GIS programming course was designed where randomly formed pairs develop a state‐of‐the‐art QGIS plugin. GitHub Classroom was used to facilitate collaboration among students, which also allowed the lecturer to monitor the progress of groups and provide timely feedback. Five out of the six groups were successful in completing the projects, and a substantial majority of the students were satisfied with the course. A strengths–weaknesses–opportunities–threats analysis reveals insights that other lecturers may find useful when designing their GIS programming courses.

Günümüzde bilgi iletişim teknolojileri (BİT) altyapılarındaki gelişmeler ve mobil cihaz kullanımının yaygınlaşmasıyla internet kullanımı artmıştır. İnternetin ticaret kanalı olarak kullanılmasıyla yüz yüze yapılan alışveriş sanal ortamlara doğru kaymış, bireylerin alışveriş yapma alışkanlıkları da değişmeye başlamıştır. İlgili literatür incelendiğinde; çevrimiçi alışveriş yapan bireylerin mekânsal dağılımını açıklayan iki hipotez belirtilmektedir; verimlilik ve yenillik hipotezleri. Verimlilik hipotezi; çevrimiçi alışverişe olan talebin ticari alanlara erişilebilirliğin düşük olmasından kaynaklandığını belirtirken, yenilik hipotezi teknolojik altyapının daha fazla olduğu bölgelerde bireylerin çevrimiçi alışveriş yapmaya daha eğilimli olduğunu savunmaktadır. Bu hipotezlerle kentsel ve kırsal alanlardaki çevrimiçi alışveriş tercihlerinin sebepleri ve coğrafya(mekân) ile nedensel ilişkileri bilimsel olarak ortaya konmaktadır. Çevrimiçi alışverişe olan talebin uzun vadede kentteki ulaşım planlaması ve perakende sektörüne etkileri olacağı düşünülmektedir. Bununla birlikte, COVID-19 salgını nedeniyle getirilen kısıtlamalar ve "evde kal" önlemleri nedeniyle internet kullanımı artmıştır. Bu çalışmada çevrimiçi alışverişin mekânsal dağılımıyla ilgili pandemiden önce ve sonra olmak üzere yapılan çalışmaların incelenerek, pandeminin çevrimiçi alışveriş ve mekân kullanımına etkisi ortaya çıkarılacaktır.

In the present study, adopting of citizen science approach for monitoring water quality of a lake (Uzungöl, Turkey) was investigated. The study consisted of selection of sampling points and water quality parameters, training of volunteers, sampling and analysis by volunteers, and development of a mobile application for data collection and storage. In the scope of the study, four measurement points around the lake were selected and elementary school students were trained both theoretically and practically to collect water quality data. During the project (June-December, 2018), volunteers collected water samples and measured temperature, pH, nitrate, and phosphate with the given test kits. The mobile application was developed using open source code and used to collect and store the volunteer data. According to the volunteer data, temperature values were between 8.5 and 15.5 °C, pH values were between 2 and 8.5, nitrate values were between 0.5 and 2.5 mg/L, and phosphate values were between 0.5 and 5 mg/L in the lake. Most of the pH and temperature results were compatible with field measurements done by research group during field visits. The motivation of the volunteers and mobile application development were the achievements of the project. Low number of the data and its reliability were the main limitations. Therefore, this study showed that citizen science has both capabilities and constraints for collection of water quality data, however; outstanding potential of citizen science is obvious.

Bu makalede, katılımcı bir yaklaşımla güncellenen Türkiye Ulusal Coğrafi Bilgi Sistemi (TUCBS) Projesi ‘ulaşım’ coğrafi veri teması incelenmektedir. Bu kapsamda kurum temsilcileri ile gerçekleştirilen ve kurumların coğrafi veri üretim ve paylaşım durumunun daha iyi anlaşılmasını sağlayan anketin sonuçları tartışılacaktır. Bu çalışmada aynı zamanda 2013 yılında belirlenen TUCBS ulaşım teması ile katılımcı yaklaşımla 2018’de güncellenen tema arasındaki benzerlikler ve farklılıklar da tespit edilmiştir. Güncellemeler kapsamında detay tipi (nesne tipi) sayısının 50’den 131’e çıktığı, ön tanımlı veri tablosu (codelist) sayısının 33’ten 42’ye çıktığı, 26 tane nesne tipinin ve 17 tane ön tanımlı veri tablosunun da her iki çalışmada ortak olduğu tespit edilmiştir. Katılımcı bir yaklaşımın coğrafi veri altyapılarının güncellenmesinde gerekli olduğu, ancak halen istenilen düzeyde olmadığı da değerlendirilmektedir. Kamu kurumu ve özel sektör temsilcilerinin ortak aklıyla ulaştırma temasının etkin bir şekilde belirlenebileceği ve güncel tutulabileceği ön görülmektedir.

The purpose of this article is to examine the 'transport' geographic data theme of the Turkish National Geographic Information System (TUCBS) Project which is updated with a participatory approach. In this context, the results of the survey conducted with the representatives of the institutions, which provide a better understanding of the geographical data production and sharing status of the institutions, are analysed. In this study, similarities and differences between the TUCBS transport theme that is initially determined in 2013 and updated in 2018 with the participatory approach is also identified. Within the scope of the updates, it is determined that the number of feature types increased from 50 to 131, the number code lists increased from 33 to 42 and 26 feature types and 17 code lists remain common in both studies. It is necessary to update the spatial data infrastructures with a participatory approach, however, it is still not at the desired level yet. It is foreseen that the contribution of all the stakeholders in standardizing the transportation theme will make the standards be more operational.

The aim of this paper is to assess the spatial accuracy of OpenStreetMap (OSM) with respect to the Turkey Topographic Vector Database (TOPOVT) within the context of ‘building’ layer. Being an open-platform, anyone can access to OSM and add geographic entities as well as update them. Since there is no stringent standards, spatial accuracy assessment of OSM is an open research area. TOPOVT, on the other hand, is produced by the General Directorate of Mapping by following a standard procedure, where the maps are produced for 1:25000 scale or larger scale. Updating this database is a costly process and could only be conducted at specific time intervals. Therefore, automatic detection of the locations requiring update in TOPOVT would be an effective operation, which would eventually reduce the overall cost of the database update. However, the spatial accuracy of the geographical features have to be analysed in order to support such a motivation. Therefore, the aim of this paper is to assess the spatial accuracy of ‘building’ layer by calculating the Hausdorff distance between the matching (homologous) polygons in OSM and TOPOVT. The proposed methodology consists of two methods to detect the matching polygons: ‘overlap method’ and ‘centroid method’. Hausdorff distance is calculated for only those intersecting buildings in both of the layers. Since it is safe to assume that the intersecting polygons refer to the same geographic object, the calculated distance could be used to indicate the spatial accuracy of the building. The developed software is tested on an urban and a rural environment in Ankara, Turkey. The results indicate that the quality of OSM could well match with TOPOVT. Specifically, the average Hausdorff distance is approximately the same for both of the methods: approximately 9.5 metres. Considering that OSM and TOPOVT are generated through completely different processes’, the spatial accuracy is considered to be ‘good’ and ‘useful’ for many practical and operational purposes. In order to increase the effectiveness of the developed methodology in a real-life context, the whole process is integrated into an ArcMap extension and the code is made available on GitHub.

Database management systems (DBMSs) are widely used to store, query and visualise large amounts of spatial data. Consequently, teaching DBMSs at both undergraduate and graduate level is a core component within most of the geoinformatics related departments’ curricula. Traditional teaching material mainly covers relational DBMS, due to their strong linkage with GIS. However, due to the emergence of web-based systems, employers also require prospective graduates to have experience on non-relational DBMS (NoSQL). This chapter develops an open-source library to analyse the query performance of two renowned DBMS: PostgreSQL/PostGIS and MongoDB, a relational and NoSQL DBMS respectively. An open-source Python library is developed to facilitate systematic performance analyses between these DBMSs. The experiments are carried out on New York City’s openly available taxi trip origin–destination dataset. The performance of two spatial queries (k-nearest neighbour and point-in-polygon) are investigated in terms of run-time and spatial accuracy. The results indicate the superiority of MongoDB. It outperformed Postgres in terms of run-time in both of the investigated queries. In addition, it is more accurate in terms of detecting k-nearest neighbours. The developed open-source library is utilised to investigate journey time variations between two airports of New York City, which demonstrates its effectiveness in terms of teaching DBMS or GIS modules.

Graphs describing the relation between nodes and edges are common in geographic information science. One of the algorithms that operate on graphs is ‘Minimum Spanning Tree (MST)’, which is a tree that connects all the nodes of a graph with minimum cost. There is no built-in functionality in QGIS, an open-source Geographical Information System (GIS) software, which can determine MST. This paper proposes a QGIS plugin that determines MST on geographical data using Kruskal’s algorithm. The updated version of the plugin (v2.0) offers three substantial improvements with respect to its former version (v1.0). First, the updated version is much faster in execution. The execution time of the two versions was assessed by determining MST on a randomly generated dataset consisting of 5000 polygons and New York City’s census blocks consisting of 38799 polygons. The updated version determined MSTs much faster, reaching up to 30-fold improvements. Second, the updated version can handle raster data. In this way, researchers might consider continuous geographical characteristics while estimating the costs of edges in addition to the discrete measure distance. Third, a barrier (obstacle) might be provided to ensure that the MST is fit for purpose as political boundaries or other restrictive socio-economic issues can be considered.

Mobile spatial data collection is one of the major tasks carried out under the United Nations (UN) to quantify targets of various Sustainable Development Goals (SDGs). Even though there are readily available proprietary solutions to ease mobile spatial data collection, there is often limited educational resources that rely on Free and Open Source Software for Geospatial (FOSS4G). The ongoing efforts of UN OSGeo Committee are to satisfy the requirements of UN operations in terms of identifying and developing open source geospatial software and services. The aim of this paper is to describe and discuss the educational material (tutorial) that is prepared to address the 2019 UN OSGeo Committee Educational Challenge entitled ‘Open geospatial data and software for UN SDG 16, Peace justice and open institution’. The educational material utilised various technologies, including QGIS, QField, PostgreSQL/PostGIS and NodeJS, by relying on a simple mobile spatial data collection scenario, which is collecting information about trees. The scenario serves as an analogy for many different UN operations that might fall under SDG 16, such as interviews with a victim or a vulnerable person or estimating to what extent decision-making is inclusive and responsive. The developed educational material addresses some of the practical requirements of mobile spatial data collection, including handling multiple users, offline data collection, attaching photos to recorded events, editing of spatial features in the field and displaying the collected data on the internet using Heroku. Even though the educational material intends to be self-explanatory, quantitative and qualitative measures are needed to evaluate its efficacy. Therefore, a group of geomatics engineering students were trained with the educational material and their feedbacks were collected through a satisfaction questionnaire as well as the completion time of the tutorial. The outcomes of this paper will be useful in terms of guiding researchers and practitioners who develop online educational material, especially in the geospatial domain.

One of the common problems at the intersection of geographical information science and transportation science is the estimation of origin-destination (OD) matrices. The emergence of sensor technologies offers unprecedented opportunities in this regard since massive amounts of traffic data can be collected in an easy way. Researchers and practitioners need to choose a suitable DataBase Management System (DBMS) among alternatives, such that storing and analysing traffic data to estimate the OD matrix is feasible. The aim of this paper is to compare the performance of two such notable DBMSs, PostgreSQL and MongoDB, in the context of OD matrix estimation. The experiments are carried out on New York City’s openly available taxi data on two different polygon sets: taxi zones and census blocks. These polygon layers consist of 263 and 38794 features respectively. The results suggest that Postgres outperforms MongoDB by generating the OD matrix instantly. The run time of MongoDB varies depending on the analysed time interval and follows a trip demand curve. As there are more trips involved in the generation of the OD matrix, so does the execution time increases in MongoDB. On the other hand, the query results are the same. Finally, the origin points of the taxi trips are visualised in QGIS using the ‘TimeManager’ plugin, and results are presented through a web-interface.

This paper investigates the use of Augmented Reality (AR) in pedestrian wayfinding in two aspects. First, an experiment was conducted to understand whether an AR-based mobile platform improves finding the direction of a query destination compared to a paper map. A total of 54 participants were enrolled to represent each group, in which the task was to show the direction of a query point-of-interest (POI). The experiments were carried out at the Beytepe Campus of Hacettepe University. The results suggest that AR-based platform significantly improves the task completion time compared to a paper map. Second, an online questionnaire was conducted to understand the preference of participants in terms of visualising the distances of POIs on an AR-based platform. Four different methods were utilised which vary the colour and size of a POI depending on its distance to the user. The results suggest that the majority of the participants preferred visualising POIs with the same colour but with different sizes depending on their distance to the user. This finding adds further support to the default visualisation adopted in Mapbox, the technology that was used to develop the AR-based platform.

Şehir içi ulaşım ağlarında bir noktadan başka bir noktaya yapılan yolculukların seyahat sürelerinin istatistiksel olarak modellenmesi ve bunların güvenirliğinin analiz edilmesi büyük önem arz etmektedir. Gerçekleştirilen kimi çalışmalarda seyahat süresini modellemek için normal dağılım gibi teorik olarak uygun olmayan dağılımlar tercih edilmiştir. Bunun yanında birçok çalışma haftanın günleri hafta içi ve hafta sonu olmak üzere iki farklı sınıfta değerlendirmektedir. Gelişen sensör teknolojileri ile elde edilen büyük ulaşım verisi üzerinden seyahat sürelerinin istatiksel dağılımı ve güvenirliği gibi şehir hayatına etki eden faktörler analiz edilebilmektedir. Bu çalışmada New York şehrine ait açık ulaşım verisi analiz edilerek, birçok çalışmanın varsaydığı hafta içi-hafta sonu ayrımının gerçekçi olmayabileceği ve bir gün içindeki farklı zaman dilimlerinde istatistiksel sonuçların farklı çıkabileceği tespit edilmiştir. John F. Kennedy ve LaGuardia havalimanları arasında 07:00-19:00 arasında gerçekleşen taksi yolculuklarının seyahat süresi değişkenliği ve dağılımları incelendiğinde, seyahat sürelerinin Log-Lojistik (3P) dağılımına uyduğu tespit edilmiştir. Bu dağılım Salı ve Çarşamba günleri baskın dağılım değildir ve bu günlerde seyahat süresi güvenilirliğinin düşük olduğu zaman dilimlerinin bulunduğu tespit edilmiştir.

As the number, volume and resolution of spatio-temporal datasets increases, traditional statistical methods for dealing with such data are becoming overwhelmed. Nevertheless, the spatio-temporal data are rich sources of information and knowledge, waiting to be discovered. The field of spatio-temporal data mining (STDM) emerged out of a need to create effective and efficient techniques in order to turn the massive data into meaningful information and knowledge. This chapter reviews the state of the art in STDM research and applications, with emphasis placed on three key areas, including spatio-temporal prediction and forecasting, spatio-temporal clustering and spatio-temporal visualization. The future direction and research challenges of STDM are discussed at the end of this chapter.