Spatial Analysis Tasks Research Articles

Remote detection of asbestos-cement roofs: Evaluating a QGIS plugin in a low- and middle-income country

Machine learning, a subset of artificial intelligence, has emerged as a powerful tool for generating new knowledge from observations. In the realm of geographic information systems (GIS), machine learning techniques have become essential for spatial analysis tasks. Satellite image classification methods offer valuable decision-making support, particularly in land-use planning and identifying asbestos cement roofs, which pose significant health risks. In Colombia, where asbestos has been used for decades, the detection and management of installed asbestos is critical. This study evaluates the effectiveness of the RoofClassify plugin, a machine learning-based GIS tool, in detecting asbestos cement roofs in Sibaté, Colombia. By employing high-resolution satellite imagery, the study assesses the plugin's accuracy and performance. Results indicate that RoofClassify demonstrates promising capabilities in detecting asbestos cement roofs, achieving an overall accuracy score of 69.73%. This shows potential for identifying areas with the presence of asbestos and informing decision-makers. However, false positives remain a challenge, necessitating further on-site verification. The study underscores the importance of cautious interpretation of classification results and the need for tailored approaches to address specific contextual factors. Overall, RoofClassify presents a valuable tool for identifying asbestos cement roofs, aiding in asbestos management strategies.

SAFE

Kernel density visualization (KDV) has been the de facto method in many spatial analysis tasks, including ecological modeling, crime hotspot detection, traffic accident hotspot detection, and disease outbreak detection. In these tasks, domain experts usually generate multiple KDVs with different bandwidth values. However, generating a single KDV, let alone multiple KDVs, is time-consuming. In this paper, we develop a share-and-aggregate framework, namely SAFE, to reduce the time complexity of generating multiple KDVs given a set of bandwidth values. On the other hand, domain experts can specify bandwidth values on the fly. To tackle this issue, we further extend SAFE and develop the exact method SAFE all and the 2-approximation method SAFE exp which reduce the time complexity under this setting. Experimental results on four large-scale datasets (up to 4.33M data points) show that these three methods achieve at least one-order-of-magnitude speedup for generating multiple KDVs in most of the cases without degrading the visualization quality.

KDV-explorer

Kernel density visualization (KDV) is a commonly used visualization tool for many spatial analysis tasks, including disease outbreak detection, crime hotspot detection, and traffic accident hotspot detection. Although the most popular geographical information systems, e.g., QGIS, and ArcGIS, can also support this operation, these solutions are not scalable to generate a single KDV for datasets with million-scale data points, let alone to support exploratory operations (e.g., zoom in, zoom out, and panning operations) with KDV in near real-time (< 5 sec). In this demonstration, we develop a near real-time visualization system, called KDV-Explorer, that is built on top of our prior study on the efficient kernel density computation. Participants will be invited to conduct some kernel density analysis on three large-scale datasets (up to 1.3 million data points), including the traffic accident dataset, crime dataset and COVID-19 dataset. We will also compare the performance of our solution and the solutions in QGIS and ArcGIS.

Motivations, design, and preliminary testing for a 360° vision simulator

Contemporary virtual reality systems enable academics to more efficiently explore and analyze complex three-dimensional (3D) content, but their utility is limited by visual short-term memory. Janus, a geometry agnostic shader script, circumvents this cognitive limitation by automatically rendering complex object meshes to fit entirely within the field-of-view of consumer head-mounted displays. The resulting 360° vision experience represents an advantage over existing scientific data visualization tools, which have sought to replicate real-world viewing experiences but have inadvertently replicated associated limitations as well. By presenting data in such a way so as to effectively circumvent cognitive loads associated with body (or object) movement, academics can use the Janus shader to more readily engage in the exploratory analysis of complex 3D data sets, thereby facilitating scientific insight. This paper explores the motivations and design of the Janus shader and describes preliminary results from user testing conducted under controlled conditions. For the 24 study participants (N = 24), statistically significant time-to-completion decreases were observed for spatial analysis tasks taking place in intervention (Janus-enabled) VR scenes of low-to-moderate complexity.

W: SPATIAL WEIGHT MATRIX DIVERSITY AND ITS IMPACT ON SPATIAL ANALYSIS RESULTS

Spatial econometrics presents irreplaceable tool for regional analysis. Omitting additional information about geographical location of observed units could neglect some important influences. The spatial weight matrix W determining neighbourhood relations and degree of influence between observed units belongs to the main components of spatial analysis. Various specification approaches of this non-stochastic matrix could be applied. There is a commonly held belief that spatial regression models are sensitive to spatial weight structure. Some analytics consider it as a myth and points out incorrect interpretation of the model coefficients or misspecified models. Does it really matter what kind of specification is used? This contribution brings an empirical example of several approaches to neighbourhood specification and compares obtained results. According to findings of this analysis, especially spillover effects are incomparable. That confirms unequal performance of spatial structures. The W matrix should be built carefully at the beginning of each spatial analysis task.

High-Performance Geospatial Big Data Processing System Based on MapReduce

With the rapid development of Internet of Things (IoT) technologies, the increasing volume and diversity of sources of geospatial big data have created challenges in storing, managing, and processing data. In addition to the general characteristics of big data, the unique properties of spatial data make the handling of geospatial big data even more complicated. To facilitate users implementing geospatial big data applications in a MapReduce framework, several big data processing systems have extended the original Hadoop to support spatial properties. Most of those platforms, however, have included spatial functionalities by embedding them as a form of plug-in. Although offering a convenient way to add new features to an existing system, the plug-in has several limitations. In particular, while executing spatial and nonspatial operations by alternating between the existing system and the plug-in, additional read and write overheads have to be added to the workflow, significantly reducing performance efficiency. To address this issue, we have developed Marmot, a high-performance, geospatial big data processing system based on MapReduce. Marmot extends Hadoop at a low level to support seamless integration between spatial and nonspatial operations of a solid framework, allowing improved performance of geoprocessing workflow. This paper explains the overall architecture and data model of Marmot as well as the main algorithm for automatic construction of MapReduce jobs from a given spatial analysis task. To illustrate how Marmot transforms a sequence of operators for spatial analysis to map and reduce functions in a way to achieve better performance, this paper presents an example of spatial analysis retrieving the number of subway stations per city in Korea. This paper also experimentally demonstrates that Marmot generally outperforms SpatialHadoop, one of the top plug-in based spatial big data frameworks, particularly in dealing with complex and time-intensive queries involving spatial index.

Research of GIS-services applicability for solution of spatial analysis tasks.

Experiments for working out the areas of applying various gis-services in the tasks of spatial analysis are discussed in this paper. Google Maps, Yandex Maps, Microsoft SQL Server are used as services of spatial analysis. All services have shown a comparable speed of analyzing the spatial data when carrying out elemental spatial requests (building up the buffer zone of a point object) as well as the preferences of Microsoft SQL Server in operating with more complicated spatial requests. When building up elemental spatial requests, internet-services show higher efficiency due to cliental data handling with JavaScript-subprograms. A weak point of public internet-services is an impossibility to handle data on a server side and a barren variety of spatial analysis functions. Microsoft SQL Server offers a large variety of functions needed for spatial analysis on the server side. The authors conclude that when solving practical problems, the capabilities of internet-services used in building up routes and completing other functions with spatial analysis with Microsoft SQL Server should be involved.

A Geospatial Cyberinfrastructure for Urban Economic Analysis and Spatial Decision-Making

Urban economic modeling and effective spatial planning are critical tools towards achieving urban sustainability. However, in practice, many technical obstacles, such as information islands, poor documentation of data and lack of software platforms to facilitate virtual collaboration, are challenging the effectiveness of decision-making processes. In this paper, we report on our efforts to design and develop a geospatial cyberinfrastructure (GCI) for urban economic analysis and simulation. This GCI provides an operational graphic user interface, built upon a service-oriented architecture to allow (1) widespread sharing and seamless integration of distributed geospatial data; (2) an effective way to address the uncertainty and positional errors encountered in fusing data from diverse sources; (3) the decomposition of complex planning questions into atomic spatial analysis tasks and the generation of a web service chain to tackle such complex problems; and (4) capturing and representing provenance of geospatial data to trace its flow in the modeling task. The Greater Los Angeles Region serves as the test bed. We expect this work to contribute to effective spatial policy analysis and decision-making through the adoption of advanced GCI and to broaden the application coverage of GCI to include urban economic simulations.

Remote Evaluation of the Execution of Spatial Analysis Tasks with Interactive Web Maps: A Functional and Quantitative Approach

The main aim of this paper is to measure the acceptability and effectiveness of three spatial analysis tools applied to functional map interfaces, by means of answering if the common web map user can make use of these tools and if this use is beneficial to a spatial task accomplishment. Also it was verified what user’s attributes influence the performance. To achieve this, a quantitative statistical analysis with a random user sample was held, with tests carried out remotely. Results showed that previous map experience and higher educational levels may influence positively the manipulation of the interface and that there is not a clear indication that the presence of the spatial analysis tools analysed had a positive influence on the tasks’ results. For the other side, users that choose to use these tools had significantly better performance than those who did not use them.

Estimating O–D travel time matrix by Google Maps API: implementation, advantages, and implications

Many spatial analysis tasks call for the use of travel time between multiple origins and destinations, that is, O–D travel time matrix. Commercial geographical information systems (GIS) software requires the input of a well-defined road network dataset and significant efforts in implementing the task. However, road network data are often outdated, miss critical road condition details, or are expensive to acquire; and skillful usage of related software is a major obstacle for researchers without advanced training in GIS. This research develops a desktop tool for implementing the task by calling the Google Maps Application Programming Interface (API). By doing so, we are able to tap into the dynamically updated transportation network data and the routing rules maintained by Google and obtain a reliable estimate of O–D travel time matrix. The results are compared with those computed by the ArcGIS Network Analyst module to demonstrate its advantages. A case study in accessibility analysis is presented to illustrate the implications.