Centroid-amenities

Estimation of time dependent scour depth around circular bridge piers: Application of ensemble machine learning methods

Urban parks are essential components of urban ecosystems, providing recreation and relaxation places to residents. Measuring the spatial accessibility to urban parks serves as an initial step in urban planning and developing urban development strategies to improve social and environmental justice. This study aims to evaluate measures of spatial accessibility to urban parks by comparing three geographic information systems (GIS)-based approaches, accounting for network complexity, transport modes, distance thresholds, and destination choices. Taking Ipswich City (Australia) and Enschede (the Netherlands) as two testbeds, we examine the spatial patterns of a total of 21 accessibility measures in the two cities and conduct a correlation and principal component analysis to unravel the interrelationship between these measures. The results suggest that among all measures under the three approaches, the selection of distance thresholds and transport modes matter more to accessibility measures than the destination choices. Furthermore, when distance threshold and transport mode are held constant, the network-based and entrance-based methods provide more realistic accessibility measures than other methods. We also discuss the generality of the entrance-based method we propose and suggest ways to choose the most appropriate accessibility measure for use in different contexts.

In Part II of this book you will see how to put third-party components together with Cocoa and its frameworks. These components can be concepts, standards, or open source tools such as JSON, or they can be specific tools, such as the login using Facebook that will be described in Part II.

The impacts of accessibility measure choice on public transit project evaluation: A comparative study of cumulative, gravity-based, and hybrid approaches

Prosimos is an open-source tool that discovers business process simulation models from execution data (event logs) and that enables users to perform what-if analysis using the resulting models. Prosimos distinguishes itself from other data-driven business process simulation approaches in the way it models resources. Existing data-driven simulation approaches treat resources as undifferentiated entities, grouped into resource pools, and assume that all resources in a pool have the same performance and availability calendars. In contrast, Prosimos allows resources (within a pool) to have different performance and availability profiles. For example, instead of treating all claims officers in an insurance claims handling process as having the same performance and availability, Prosimos may capture scenarios where senior resources perform some tasks faster than junior ones, or scenarios where some resources work part-time. To this end, Prosimos integrates algorithms for discovering differentiated resource profiles from event logs.

Major sports infrastructure investments represent significant commitments of public and private capital, yet their spatial accessibility — the degree to which surrounding communities can physically access the venue — remains inadequately quantified in the urban planning literature. This study presents a geospatial analysis of the Allegiant Stadium in Las Vegas, Nevada, examining the spatial distribution of 517 towns across the state relative to the stadium using geodesic distance computation. The analysis employs a Geographic Information System (GIS) pipeline utilizing U.S. Census Bureau shapefiles processed with the GeoPandas library, coordinate reference system transformation from NAD 1983 Albers to WGS84, and geodesic distance calculation via the Geopy library's WGS84 ellipsoid model. Results reveal a highly uneven accessibility distribution, with 20 towns located within 26 kilometers of the stadium — predominantly in the Las Vegas metropolitan area — while rural communities in northern Nevada are situated over 500 kilometers away. Distance-based heatmap and bar chart visualizations illustrate the concentration of accessible communities and the sharp accessibility gradient across the state. The findings contribute to the growing literature on infrastructure accessibility assessment and provide a reproducible methodological framework applicable to spatial analysis of major infrastructure projects in urban and regional planning contexts. The methodology demonstrates the practical utility of open-source geospatial tools for quantifying the catchment area and regional reach of large-scale public amenities.

The study aims at developing an applicable methodology to produce the functional land-use map using only free and open-source data. Top-view Sentinel image and ground-view Open Street Map (OSM) data are chosen due to their extensive availability. The three-stage framework, including object-based image analysis, OSM data cleaning, and ontology-based decision fusion, is proposed and implemented with open-source tools. We applied the developed approach to districts 1, 4, and 7 of HoChiMinh city, representing the complexities of the dynamic change in big cities. The result showed a good functional land use map with 78.70% overall accuracy. The outcome presents the mismatch between the data-driven approach and human knowledge, which can be improved by ontology-based fusion with OSM data. The ontology-based framework comprises the common urban land-use classes and OSM attributes, which can be applied and extended in other urban areas. Additional text attributes may be applicable only locally and can be modified in our open-source framework. Object-based image analysis takes advantage of Google Earth Engine computing power, whereas ontology-based processing works well on a local computer. In future studies, adopted natural language processing to pre-process OSM data and ontology-based fusion will be implemented on the cloud-computing platform to enhance computational efficiency.

Within the broad field of walkability research, a key area of focus has been the relationship between urban form and capacities for walking. Measures of walkable access can be grouped into two key types: permeability measures that quantify the ease of movement through an urban fabric, and catchment measures, quantifying the potential to reach destinations within walking distance. Of numerous street network measures in use, it has been shown that many are poor proxies of permeability and catchment. Instead, two new measures have been proposed: the area-weighted average perimeter (AwaP) and interface catchment (IC), that, combined, better capture the capacities of urban morphologies to enable and attract pedestrian movement. In this paper, we present the QGIS tool AwaP-IC, developed to overcome the difficulty of computing these measures. Unlike GIS tools based on models that abstract streets to axial lines, by employing new algorithms and spatial computation techniques, AwaP-IC analyses actual urban morphologies, based on cadastral maps delineating public and private land. This can empower a new stream of urban morphological studies with the computational power of GIS. As an open-source tool, it can be further developed for use in urban mapping and to streamline the analysis of large datasets.

Green parks and open spaces are indispensable parts of an urban environment as they play important roles in promoting public health, protecting natural ecosystems and improving socioeconomic conditions of the city dwellers. Hence, the information of green and open spaces in a city needs to be provided to its governing authority and people for better management and utilisation. WebGIS technology is capable of maintaining and disseminating green park information among its users over the web. The availability of open source geospatial software and tools made this technology more cost effective and acceptable to the user community. In present study an interactive and user friendly WebGIS enabled solution is developed for green parks of Kolkata city that facilitates city dwellers and decision makers to search and visualise green parks both on map and satellite image along with park information and ground photo. It is an easy deployable solution that helps in urban planning for creating new as well as preserving the existing green spaces.

Choropleth mapping is an essential visualization technique for exploratory spatial data analysis. Visualizing multiple choropleth maps is a technique that spatial analysts use to reveal spatiotemporal patterns of one variable or to compare the geographical distributions of multiple variables. Critical features for effective exploration of multiple choropleth maps are (1) automated computation of the same class intervals for shading different choropleth maps, (2) dynamic visualization of local variation in a variable, and (3) linking for synchronous exploration of multiple choropleth maps. Since the 1990s, these features have been developed and are now included in many commercial geographic information system (GIS) software packages. However, many choropleth mapping tools include only one or two of the three features described above. On the other hand, freely available mapping tools that support side-by-side multiple choropleth map visualizations are usually desktop software only. As a result, most existing tools supporting multiple choropleth-map visualizations cannot be easily integrated with Web-based and open-source data visualization libraries, which have become mainstream in visual analytics and geovisualization. To fill this gap, we introduce an open-source Web-based choropleth mapping tool called the Adaptive Choropleth Mapper (ACM), which combines the three critical features for flexible choropleth mapping.

In a simulation-based and data-driven engineering approach, open-access met-ocean data sets and open-source simulation tools are critical elements for marine structure designs. In this work, the authors propose a combined approach for down-scale numerical modelling from offshore met-ocean data to wave loads on structures. The large-scale hind-cast database NORA3 and NORA10 from the Norwegian Meteorology Institute (MET) are used as offshore inputs. The open-source spectral wave model SWAN is used to simulate offshore waves in combination with local wind forcing. The spectral modelling results are then used as inputs in the open-source hydrodynamics framework REEF3D. Its fully nonlinear potential flow (FNPF) module REEF3D::FNPF is used for phase-resolving coastal wave propagation and nonlinear transformation simulations. The arbitrary Eulerian-Lagrangian (ALE) method is integrated into the FNPF model for highly efficient multi-body farm-scale force calculations and extreme events identifications. The extreme events are then closely examined using the hydrodynamic coupling (HDC) approach by transferring the flow kinematics from REEF3D::FNPF to the high-fidelity computational fluid dynamics (CFD) model REEF3D::CFD for fluid-structure interaction analysis. The down-scale approach using the open-access met-ocean data NORA3 and open-source numerical tools and techniques SWAN-REEF3D-ALE-HDC (SARAH) leads to the open and efficient preliminary and detailed design procedure: the NORA-SARAH procedure. This article elaborates on the procedure and demonstrates its effectiveness with an example of a navigational signal tower assessment on the Norwegian coast.

Geographic analysis has long supported transport plans that are appropriate to local contexts. Many incumbent ‘tools of the trade’ are proprietary and were developed to support growth in motor traffic, limiting their utility for transport planners who have been tasked with twenty-first century objectives such as enabling citizen participation, reducing pollution, and increasing levels of physical activity by getting more people walking and cycling. Geographic techniques—such as route analysis, network editing, localised impact assessment and interactive map visualisation—have great potential to support modern transport planning priorities. The aim of this paper is to explore emerging open source tools for geographic analysis in transport planning, with reference to the literature and a review of open source tools that are already being used. A key finding is that a growing number of options exist, challenging the current landscape of proprietary tools. These can be classified as command-line interface, graphical user interface or web-based user interface tools and by the framework in which they were implemented, with numerous tools released as R, Python and JavaScript packages, and QGIS plugins. The review found a diverse and rapidly evolving ‘ecosystem’ tools, with 25 tools that were designed for geographic analysis to support transport planning outlined in terms of their popularity and functionality based on online documentation. They ranged in size from single-purpose tools such as the QGIS plugin AwaP to sophisticated stand-alone multi-modal traffic simulation software such as MATSim, SUMO and Veins. Building on their ability to re-use the most effective components from other open source projects, developers of open source transport planning tools can avoid ‘reinventing the wheel’ and focus on innovation, the ‘gamified’ A/B Street https://github.com/dabreegster/abstreet/#abstreet simulation software, based on OpenStreetMap, a case in point. The paper, the source code of which can be found at https://github.com/robinlovelace/open-gat, concludes that, although many of the tools reviewed are still evolving and further research is needed to understand their relative strengths and barriers to uptake, open source tools for geographic analysis in transport planning already hold great potential to help generate the strategic visions of change and evidence that is needed by transport planners in the twenty-first century.

SAT-039 Socio-Demographic Characteristics, Out of Pocket Expenditure, Quality of Life and Six Months Treatment Outcomes of Haemodialysis Patients in India

The urban planning system in Serbia currently lacks substantial public participation, resulting in cities shaped by private interests and exacerbating spatial conflicts and inequalities. This chapter investigates the potential of a collaborative approach to urban planning, based on deliberative democracy principles to transform the planning process and ensure inclusive and legitimate decision-making that represents the interests and needs of the local community. Drawing on insights from deliberative forums conducted in 2021 and 2022 on three specific urban plans, the study supports the claim that the deliberative approach can enhance inclusion, diversity and legitimacy compared to existing planning procedures. The findings also highlight the impact of different elements of deliberation design, including recruitment and formulation of topics, on the quality of discussions and outcomes of the deliberation process. Furthermore, the study emphasizes the influence of the provision of adequate and timely information, characteristic of deliberative formats, on the process and outcomes of planning. This research thus contributes to the discourse on democratizing urban planning by emphasizing the importance of deliberation in developing more legitimate and community-oriented planning solutions. It also aims to support the long-term institutionalization of deliberative methods in urban planning in Serbia.

This chapter explores the transformative role of geospatial analysis and Explainable Artificial Intelligence (XAI) in addressing real-world challenges across diverse domains such as urban planning, transportation, agriculture, environmental monitoring, public health, disaster management, and defense. Geospatial tools have become integral to identifying, mapping, and analyzing Earth’s features, enabling informed decision-making through spatial visualization and modeling. The increasing integration of AI into geospatial systems introduces new opportunities for automation and prediction but also raises concerns about transparency and interpretability. To address this, the chapter emphasizes the emerging importance of XAI in geospatial applications, highlighting how hybrid models, causal inference techniques, visual explanation tools, and uncertainty quantification can enhance the trust and reliability of AI-driven insights. Future trends such as edge computing, federated learning, and human-AI collaboration are examined for their potential to create interpretable, privacy-preserving, and real-time geospatial systems. Through practical insights and theoretical foundations, this chapter provides a forward-looking perspective on building transparent, accountable, and sustainable geospatial intelligence systems powered by explainable AI.