Changes In Spatial Data Research Articles

Recognizing the Wadi Fluvial Structure and Stream Network in the Qena Bend of the Nile River, Egypt, on Landsat 8-9 OLI Images

With methods for processing remote sensing data becoming widely available, the ability to quantify changes in spatial data and to evaluate the distribution of diverse landforms across target areas in datasets becomes increasingly important. One way to approach this problem is through satellite image processing. In this paper, we primarily focus on the methods of the unsupervised classification of the Landsat OLI/TIRS images covering the region of the Qena governorate in Upper Egypt. The Qena Bend of the Nile River presents a remarkable morphological feature in Upper Egypt, including a dense drainage network of wadi aquifer systems and plateaus largely dissected by numerous valleys of dry rivers. To identify the fluvial structure and stream network of the Wadi Qena region, this study addresses the problem of interpreting the relevant space-borne data using R, with an aim to visualize the land surface structures corresponding to various land cover types. To this effect, high-resolution 2D and 3D topographic and geologic maps were used for the analysis of the geomorphological setting of the Qena region. The information was extracted from the space-borne data for the comparative analysis of the distribution of wadi streams in the Qena Bend area over several years: 2013, 2015, 2016, 2019, 2022, and 2023. Six images were processed using computer vision methods made available by R libraries. The results of the k-means clustering of each scene retrieved from the multi-temporal images covering the Qena Bend of the Nile River were thus compared to visualize changes in landforms caused by the cumulative effects of geomorphological disasters and climate–environmental processes. The proposed method, tied together through the use of R scripts, runs effectively and performs favorably in computer vision tasks aimed at geospatial image processing and the analysis of remote sensing data.

Hierarchical stroke mesh: a new progressive matching method for detecting multi-scale road network changes using OpenStreetMap

Spatial feature matching is the key to detecting incremental changes in spatial data and extracting the updated information. The accuracy of spatial feature matching can depend on the structural organization of the data being compared; inconsistent data structures make comparison more difficult. OpenStreetMap (OSM) road network data, for example, is updated frequently to the point of being unstable, making the matching process used in information extraction susceptible to interference. To use OSM for comparison with other road data sources, this problem must be addressed. This paper proposes a new multi-scale dynamic matching algorithm based on a hierarchical stroke mesh (HSM) to detect matches between OSM data and professional surveying and mapping data and to update the change information. By improving the integrity and continuity of the stroke generation method and its algorithm for evaluating the importance of information, the algorithm proposed in this paper identifies the spatial hierarchy contained in the road network and abstracts the road network. The result is the HSM. The algorithm is based on multi-scale matching constraint rules designed from coarse to fine in terms of both resolution and granularity. It is used to detect one-to-one or one-to-many mapping relationships among different mesh levels (mesh, mesh boundary segment, and mesh inner segment). This allows progressive iterative matching between the older survey data and the newer OSM data. The results show that the HSM algorithm proposed in this paper can detect incremental changes between the two vector data sources quickly and accurately. Compared with others, this algorithm can effectively improve matching accuracy while sacrificing little performance.

A Hybrid Approach for Analysis of Dynamic Changes in Spatial Data

A Hybrid Approach for Analysis of Dynamic Changes in Spatial Data

A Hybrid Approach for Analysis of Dynamic Changes in Spatial Data

Any geographic location undergoes changes over a period of time. These changes can be observed by naked eye, only if they are huge in number spread over a small area. However, when the changes are small and spread over a large area, it is very difficult to observe or extract the changes. Presently, there are few methods available for tackling these types of problems, such as GRID, DBSCAN etc. However, these existing mechanisms are not adequate for finding an accurate changes or observation which is essential with respect to most important geometrical changes such as deforestations and land grabbing etc.,. This paper proposes new mechanism to solve the above problem. In this proposed method, spatial image changes are compared over a period of time taken by the satellite. Partitioning the satellite image in to grids, employed in the proposed hybrid method, provides finer details of the image which are responsible for improving the precision of clustering compared to whole image manipulation, used in DBSCAN, at a time .The simplicity of DBSCAN explored while processing portioned grid portion.

A MODEL TO DETECT CARTOGRAPHIC BASE CHANGES AFFECTING THE MAP UPDATE

Spatial data of cartographic bases are often used for nautical and aeronautical charts, reference maps, or other thematic maps. Traditionally, such spatial data sets of different scales are created apart from each other, are stored in different databases, and, in many cases, have no relations. This makes the update of the spatial data a complex and costly task. Meanwhile, automatic detection of changes in spatial data, when older versions of data are compared to more recent version of data, saves time in this process. To model such a process, two major tasks are solved, namely, detection of a change in spatial data and identification of its significance to answer whether the change detected is significant enough that an update of the data will be effective. The model also takes into account the requirements for spatial data, the scale of the data, and update methods.

Toward a Differential Calculus for Temporal Map Analysis

Investigators in many fields are analyzing temporal change in spatial data. Such analyses are typically conducted by comparing the value of some metric (e. g., area, contagion, or diversity indices) measured at time T1 with the value of the same metric measured at time T2 . These comparisons typically include the use of simple interpolation models to estimate the value of the metric of interest at points in time between observations, followed by applications of differential calculus to investigate the rates at which the metric is changing. Unfortunately, these techniques treat the values of the metrics being analyzed as if they were observed values, when in fact the metrics are derived from more fundamental spatial data. The consequence of treating metrics as observed values is a significant reduction in the degrees of freedom in spatial change over time. This results in an oversimplified view of spatio-temporal change. A more accurate view can be produced by (1) applying temporal interpolation models to observed spatial data rather than derived spatial metrics; (2) expanding the metric of interest's computational equation by replacing the terms relating to the observed spatial data with their temporal interpolation equations; and (3) differentiating the expanded computational equation. This alternative, three-step spatio-temporal analysis technique will be described and justified. The alternative technique will be compared to the conventional approach using common metrics and a sample data set.

Visual Evaluation of GIS Algorithms Using a Reference Grid

Geographic Information Systems (GIS) algorithms are used to simplify, edge match, and overlay large data sets. Some of these GIS processes can cause considerable positional changes to spatial data which are sometimes difficult to assess. This study presents a visualization technique for the evaluation of GIS algorithms and their positional effects on spatial data. The technique is applicable to vector representations and can be used with any GIS operation that changes vector geometry. The technique employs a uniform reference grid to exploit the visual skills of human operators in the evaluation of positional changes in spatial databases after applying GIS transformations. Changes in grid cell length, area, and shape, along with a set of displacement vectors, can be analyzed to evaluate positional changes in spatial data and to compare the behaviors of different algorithms. The technique can assist GIS users in the documentation of positional changes and in the comparison and selection of algorithms for v...