Parcel Shape Research Articles

Spatially Explicit Analysis of Landscape Structures, Urban Growth, and Economic Dynamics in Metropolitan Regions

Assuming that settlement morphologies and landscape structures are the result of economic transformations, the present study illustrates a statistical framework investigating metropolitan growth due to the inherent changes in landscape configurations vis à vis socio-demographic functions. Focusing on the evolution of their spatial drivers over time, metropolitan development was studied by adopting land parcels (or ‘patches’, as they are referred to in the ecological literature) as the elementary analysis unit—with the individual surface area and a specific shape indicator as the dependent variables and background socioeconomic attributes as predictors of landscape change over time. We specifically ran a Multiscale Geographically Weighted Regression (MGWR) testing the spatial dependence of the size and shape of landscape parcels on a vast ensemble of socioeconomic factors in a dense region (metropolitan Athens, Greece) with natural landscapes exposed to increasing human pressure. To investigate the spatial direction and intensity of the settlement expansion and landscape change, local regressions using the parcel area and fractal index (perimeter-to-area ratio) as the dependent variables and the elevation, distance from selected economic nodes, transport infrastructures, and natural amenities as the predictors were run separately for 1990 and 2018, representative of, respectively, a mono-centric configuration and a moderately polycentric organization of economic spaces. In a strictly mono-centric setting (1990), the parcel size showed a linear dependence on the distance from business districts, elevation, and wealth. Changes in the relationship between the parcel size and spatial (economic and non-economic) drivers may suggest a latent process of settlement de-concentration, and a possible shift toward polycentric development (2018), as documented in earlier studies. By integrating socioeconomic and ecological dimensions of landscape analysis and land evaluation, the empirical results of this study outline the increased complexity of dispersed landscape structures within dense metropolitan regions and along urban–rural gradients in Europe.

SEGMENTATION OF PARCEL BOUNDARY INDICATIONS IN VERY HIGH-RESOLUTION ORTHOPHOTO MOSAICS FOR CONTROL POINT IDENTIFICATION

Several Land Parcel Geometry issues in Indonesia's Land Registration Process, such as parcel overlapping, gaps between parcels, and incorrect parcel shapes and sizes, are currently being addressed through a block adjustment approach. One crucial aspect of the block adjustment process is determining control points that tie the parcel geometry to the land coordinate system. Detailed Observations and measurements of parcel points in the field and aerial photographs established these control or tie points. Rectifying land parcels requires many control points, requiring substantial time and effort. The automation phase is critical to expedite the control point identification process. This research uses artificial intelligence techniques to identify control points in very high-resolution orthophoto mosaics. The method employed for control point identification involves the Segment Anything Model (SAM) algorithm to segment parcel boundary indications accurately. Enhance the quality of segmentation results conducted by fine-tuning, followed by centerline extraction and refinement of the extracted data. Based on the segmentation, a SAM model capable of accurately segmenting building objects is attained, After the centerline extraction process and modifications to the existing geometric operations within the GIS Tool, at the edges of buildings, fences, and walls derived points. These points can serve as control point indications in the block adjustment process.

Deciphering the plot preferences of forest contractors when purchasing stumpage through conjoint analysis

In order to have sustainable wood value chains, it is essential to understand the factors that determine the incorporation of wood resources into them. Forest contractors are among the key players in this process. This study evaluates the preferences of forest contractors when purchasing forest parcels. The variables considered are: slope of the terrain, distance to the nearest road, parcel size, parcel shape, and the fragmentation of the surrounding land. The study area is Galicia, a region in northwestern Spain that is dominated by small-scale family forestry. An Attribute Levels Survey was designed to establish threshold values of the considered variables to afterwards perform a Choice Based Conjoint Analysis (CBCA). The CBCA allowed to analyze the preferences of forest contractors in relation to the values of these variables and their relative importance. Also, it allowed to generate a map of the level of preference for all the Galician forest parcels with individual, private ownership. The most noteworthy result of this survey was that size greatly impacts the preference of timber contractors, preceded by slope and followed by distance to roads. These results will aid in the design of landscape-scale policies in a geospatial dimension, like the promotion of forest associations, and will lead to an improvement in the sustainability of wood supply.

Analyzing Land Shape Typologies in South Korean Apartment Complexes Using Machine Learning and Deep Learning Techniques

In South Korea, the configuration of land parcels within apartment complexes plays a pivotal role in optimizing land use and facility placement. Given the significant impact of land shape on architectural and urban planning outcomes, its analysis is essential. However, studies on land shape have been limited due to the lack of definitive survey criteria. To address these challenges, this study utilized a map application programming interface (API) to gather raw data on apartment complex layouts in South Korea and processed these images using a Python-based image library. An initial analysis involved categorizing the data through K-means clustering. Each cluster’s average image was classified into four distinct groups for comparison with the existing literature. Shape indices were employed to analyze land configurations and assess consistency across classes. These classes were annotated on a parcel level using the Roboflow API, and YOLOv8s-cls was developed to classify the parcels effectively. The evaluation of this model involved calculating accuracy, precision, recall, and F1-score from a confusion matrix. The results show a strong correlation between the identified and established classes, with the YOLO model achieving an accuracy of 86% and demonstrating robust prediction capabilities across classes. This confirms the effective typification of land shapes in the studied apartment complexes. This study introduces a methodology for analyzing parcel shapes through machine learning and deep learning. It asserts that this approach transcends the confines of South Korean apartment complexes, extending its applicability to architectural and urban design planning on a global scale. Analyzing land shapes earmarked for construction enables the formulation of diverse design strategies for building placement and external space arrangement. This highlights the potential for innovative design approaches in architectural and urban planning worldwide.

Using spatial elimination and ranking methods in the renewable energy investment parcel search process

The study presents a framework for an elimination algorithm aimed at determining potential investment locations for photovoltaic farms in three separate counties located in northeastern Poland. The research focuses on identifying environmental and economic criteria for solar farm locations, establishing boundary values, and discussing the outcomes of multi-criteria decision support using the Boolean method and Borda ranking methods. Through the amalgamation of the elimination algorithm and a multi-criteria decision-making model, ten different location variants were derived, ranging from the best (Variant 1) to the worst (Variant 10), out of a total of 1024 variants. The results highlight five critical criteria that persisted after the elimination algorithm: distance from medium voltage lines, distance from roads, parcel shape, average parcel width, and average slope. The analysis revealed that the analysed counties have between 150 and 1336 optimal locations (depending on the analysed area) that satisfy all the specified conditions. This method, which enables the swift and reproducible identification of suitable areas for photovoltaic farm construction, can be applied in various European regions.

The effects of land consolidation projects on pressurized irrigation system design and the cost: A case study from Türkiye

Land consolidation projects (LCP) has been applied in various countries to decrease land fragmentation, to give field road and to create more appropriate regular parcel shapes besides many benefits to farmers. Moreover, LCP help to decrease the cost of irrigation system establishment. Türkiye aims to modernize open irrigation systems into pressurized systems with LCPs to save water due to climate change and global warming. However, previously published studies on the effect of LCP on designing pressurized irrigation network are not adequate. In this study, Eymir Village LCP in Türkiye was chosen as the material to investigate how can LCP affect the cost of the establishment of a pressurized irrigation system. In this context, two similation of irrigation systems are designed with help of geographic information systems (GIS) and are analyzed by COPAM (Combined Optimization and Performance Analysis Model) to optimize the hydraulic performance. Results show that land consolidation proved to be an effective tool to solve spatial problems related to improving the irrigation systems, technical problems related to the performance of the networks, and the equity of services (guaranteeing 98% satisfaction of users). And, it has the most important effect on the economic feasibility of pressurized irrigation network establishment whose cost can be decreased by 13.6% by LCP.

Long short term memory deep net performance on fused Planet-Scope and Sentinel-2 imagery for detection of agricultural crop.

In view of the challenges faced by organizations and departments concerned with agricultural capacity observations, we collected In-Situ data consisting of diverse crops (More than 11 consumable vegetation types) in our pilot region of Harichand Charsadda, Khyber Pakhtunkhwa (KP), Pakistan. Our proposed Long Short-Term Memory based Deep Neural network model was trained for land cover land use statistics generation using the acquired ground truth data, for a synergy between Planet-Scope Dove and European Space Agency's Sentinel-2. Total of 4 bands from both sentinel-2 and planet scope including Red, Green, Near-Infrared (NIR) and Normalised Difference Vegetation Index (NDVI) were used for classification purpose. Using short temporal frame of Sentinel-2 comprising 5 date images, we propose an realistic and implementable procedure for generating accurate crop statistics using remote sensing. Our self collected data-set consists of a total number of 107,899 pixels which was further split into 70% and 30% for training and testing purpose of the model respectively. The collected data is in the shape of field parcels, which has been further split for training, validation and test sets, to avoid spatial auto-correlation. To ensure the quality and accuracy 15% of the training data was left out for validation purpose, and 15% for testing. Prediction was also performed on our trained model and visual analysis of the area from the image showed significant results. Further more a comparison between Sentinel-2 time series is performed separately from the fused Planet-Scope and Sentinel-2 time-series data sets. The results achieved shows a weighted average of 93% for Sentinel-2 time series and 97% for fused Planet-Scope and Sentinel-2 time series.

Tekirdağ İli Tarım Parsellerinin Arazi Toplulaştırma Açısından Şekil Analizi ile Değerlendirilmesi

Shape irregularity is one of the most important factors that reduce agricultural efficiency and the sustainability of rural development. In the study, the shape of 346.740 agricultural parcels in Tekirdağ province was measured by the Parcel Shape Index (PSI) method, which integrates six different criteria (length of sides, acute angles, reflex angles, boundary points, compactness and regularity). The PSI method analyzes geometry more comprehensively than other classical shape methods. Determining the shape suitability of the parcels with this method and then the corrections to be made in the deformed parcels ensure that the negative effects of agricultural mechanization are minimized. In the research, shape suitability degrees were categorized into four groups; namely, highly irregular shapes, irregular shapes, regular shapes and near optimum shapes. It was determined that 17.27% of the parcels in the province have a highly irregular shape, 35.21% have an irregular shape, 35.54% have a regular shape and 11.98% have near optimum shape geometry. Land consolidation studies should be carried out urgently due to the shape irregularity affecting production throughout the province. PSI method can provide great benefits in the site selection before the land consolidation projects planned in Türkiye, at the scale of basin, village or rural county.

Social Aspects in Land Consolidation Processes

Land consolidation is an instrument that readjusts land parcel shapes and reallocates land rights in order to minimize farmland fragmentation, optimize agricultural output, and generate optimal living and working conditions in rural areas. The optimization and reallocation algorithms typically rely on monetarized values of land parcels, soil quality, and compensation amounts. Yet, land management interventions also need instruments for socio-spatial optimization, which may be in conflict with the monetary ones. Many non-monetary values are qualitative in nature. Hence, there is a research gap in how such values can be detected and incorporated, such that they can create a multi-dimensional land consolidation outcome. This study applies a situational analytical approach to investigate how, where, and when social values and belief systems play a role in land consolidation cases in three different study areas. This process enables the qualitative detection of which types of social values are central during land consolidations and which ones are most essential when evaluating outcomes of land consolidation. The synthesis derives that the incorporation of aims—such as addressing socio-spatial affinity, need for equity and fairness, human recognition, and good neighborship—is possible through an innovation in land consolidation practices, social valuation methods, and/or socially responsive land consolidation laws.

Arazi Toplulaştırma Projelerinde Parsel Şekillerinin Değerlendirilmesi: Yeni Şekil İndeksi Yaklaşımı

Land consolidation (LC) is of significant importance and is an application that decreases land fragmentation level and deformed parcels, which reduce agricultural production, increasing fuel consumption and labor cost in the agriculture sector. The present research focuses on parcel shapes and investigates a new index measuring complexity of parcels. The most commonly used indices (fractal dimension, shape index, form factor, areal form factor, area perimeter ratios, and the number of points) are compared with the new shape index. The new shape index is calculated with the parcel's area and minimum bounding geometry of the parcel using geographic information systems. The new approach was applied to the cadastral data consisting of different types of parcel shapes before LC in a village in Mersin, Turkey. The new shape index showed a good performance in terms of measuring shape complexity and is easily applicable. The presented method may be used to assess LC projects or to determine priority areas for LC.

Mapping tillage direction and contour farming by object-based analysis of UAV images

Tillage is a primary agricultural task that causes progressive soil movement and, consequently, severe erosion in sloping farmland, with a high impact on crop productivity, soil quality and landscape features. Governments and public administrations acknowledge the need to control tillage operations for a better soil conservation in vulnerable areas, recommending the practice of contour farming. Despite this importance, there is currently no mechanism for the effective and large-scale monitoring of tillage patterns at the field or regional scale. Accordingly, this research combined aerial images taken with unmanned aerial vehicles and object-based image analysis (OBIA) to develop an innovative OBIA4tillage procedure with three main objectives: (i) analysing plowed agricultural fields, identifying and mapping the tillage marks, and automatically computing the main direction of the tillage furrows, (ii) validating the procedure quality in different scenarios by evaluating the accuracy of the results as affected by the sensor used (visible-light vs. multispectral), background soil hue, and ground vegetation density; and (iii) mapping contour farming and non-contour farming areas as indicators of potential low and high soil erosion risk, respectively. Twenty olive parcels from two different locations with a wide range of tree sizes, soil hue, parcel shapes and land slopes were selected as model systems to develop and validate the procedure. The OBIA4tillage procedure produced tillage maps with very high accuracy for both RGB and multispectral images (R2 of 0.99 and 0.93, respectively), as obtained from the linear equation between estimated and ground-truth values. The results were similar in clear and dark soils (R2 of 0.96 in both cases), although there were notable differences between areas of dense ground vegetation or bare soil (R2 of 0.99 in both cases) and areas of medium vegetation cover (R2 of 0.81). The application of contour farming in the study region was moderate at location 1 (42.35% of the study area) but more widespread at location 2 (72.60% of the study area), which revealed the uneven involvement of the local farmers in the challenge of controlling soil erosion risks. The valuable information in the OBIA4tillage outputs can also be used to study tillage events at various spatial and temporal scales, feed soil erosion models and promote good agricultural and environmental conditions measures implemented at the parcel scale.

The evaluation of agricultural landowner inputs before and after land consolidation: The Kesik Village example

The task of land consolidation projects is to bring scattered and degraded parcels to the appropriate parcel shape and size for agricultural management. Thus, direct access to each parcel road and irrigation system can be achieved. Thus, land operating costs are reduced in areas where land consolidation is applied. The present study aimed to determine the changes in input costs before and after land consolidation. As factors affecting input cost, land consolidation ratio, parcel shape index, size and number, transportation time to/from parcels and fuel consumption were considered. The material of the study consists of the land consolidation project area in the Kesik Village of Yeşilhisar District in Kayseri, Turkey. The total number of parcels of 30 randomly selected landowners decreased from 85 to 35 with land consolidation process. However, the average parcel size of selected landowners increased from 20.3 da to 41.5 da. After consolidation in the study area, the road length decreased by 343.5 km. With the decrease in the length of the road, there has been a decrease in the transportation fuel cost and time to the parcels. It was found that 151.14 L fuel and 163.234 USD cost were saved for the selected landowners and a 17 h and 18 min time gain.

The impact of the variability of parameters related to transport costs and parcel shape on land reallocation results

Land reallocation is a time-consuming but vital stage of land-consolidation projects, hence the many attempts to automate and optimize this process. Transport costs related to distances from parcels to farm buildings and costs related to areas and shapes of parcels reduce the profitability of agricultural production. These factors are taken into consideration in some of land reallocation algorithms as separate, independent parameters.The objective of the study was to demonstrate that it is possible to simplify the optimization of land reallocation with a single, dimensionless parameter, a ratio of costs related to the shape and area of a parcel to transport costs. The linear programming method employed uses binary variables, making it a heuristic research method. The optimization model considers several additional conditions to reflect actual land-consolidation projects better: the result of interviews with landowners; equal holding values before and after reallocation; the reduction of the average distance to parcels.The study has demonstrated how the variability of the proposed parameter affects results of optimization in the form of an objective function of the parcel area, shape, and distance to buildings. The proposed modification improves the versatility of the land reallocation model as it allows the researcher to employ a single parameter, which may vary in time, by region, by initial land fragmentation parameters, or changes in farming inputs prices, including fuel and labour. The results proved that very high variability of the examined parameter translates into no more than 10% increase in the value of the objective function in relation to its minimal, optimal value. The research has demonstrated that it is sufficient to assume a specific ratio of costs related to parcel shape to transport costs to devise a proposal of an optimal boundary arrangement for given economic conditions and area.

Evaluation of land consolidation projects with parcel shape and dispersion

Similar to many of the world's countries, Turkey has spent a large amount of money, time and effort on land consolidation (LC) work each year to provide the infrastructure required by agricultural lands, to minimize the problem of land fragmentation, to ensure the protection of agricultural lands, to increase agricultural production, to provide socioeconomic development in rural areas and to protect the rural landscape. However, not enough attention is given to the assessment of LC success. In this study, an approach is presented to assess the success of LC environmentally as measured by the number of parcels and parcel shape, which are important parameters related to LC project success. The EiCEN, EiLAP, and MBG_DoP indices were used to determine the dispersion of parcels owned by landowners. Minimum bounding geometry (i.e., the PAMPA, PPMBD and MBLW indices) was used for the analysis of parcel shape before and after LC. The indices proposed by this study are easily calculated parameters and can be important indicators in determining the success of a project after its termination.

Kernel Density Analysis of Parcel Size and Shapes Before and After Land Consolidation: A Case Study from Aşağısümenli Village in Malatya, Turkey

Land consolidation (LC) projects are a set of applications that improve the economics of enterprises by assembling fragmented, dispersed, and irregular parcels. As the parcel densities coalesce around the village centre, the operation becomes easier, and fuel costs are reduced. Besides, the size of the parcel is one of the most important factors that increase the income of the enterprises, as well as the plant pattern, agricultural production form, soil quality, talents, labour force and technology features. The aim of the current study conducted within Asagisumenli LC project in Malatya, Turkey, was to assess the density of small parcels around the village centre by using kernel density analysis as one of the geospatial analyses and to investigate the spatial distribution of irregular parcels with shape index. To identify the smallest parcels spatial distribution, 50%, 75% and 90% bandwidths were determined. Before LC, the average parcel area within 50%, 75% and 90% bandwidth was 0.69, 0.93 and 1.07 ha; after LC was 0.89, 1.45 and 1.63 ha, respectively. The area averages of parcels between 50% and 75% bandwidths before LC were 1.79 between 75% and 90% bandwidths and 4.77 ha out of 90% bandwidth; after LC, 1.60, 2.47 and 3.13 ha, respectively. As a result, the small parcels after LC were more concentrated around the village centre than before LC. Moreover, it can be said that the density of the small rectangular shaped parcels around the centre of the village is a positive result in terms of reducing the operation cost.

ASSESSMENT OF FACTORS AFFECTING REAL ESTATE PRICE IN HUE CITY, THUA THIEN HUE PROVINCE

This study focuses on determining the impact of factors on property prices in Hue city, Thua Thien Hue province. The study used quantitative and qualitative analysis in SPSS to statistically characterize the investigated subjects; using Likert scale with five levels and regression method to evaluate the influences of relevant factors on real estate; surveying 115 samples in 03 wards of An Tay, An Cuu and An Dong of Hue city. The results showed that, 6 factors affecting real estate prices were identified, including: (i) Location, (ii) Topographic and shape, (iii) Legal, (iv) Social factors, (v) Infrastructure and (vi) Environment. The impact level of 6 factors on real estate prices are: Environment (0.416), Terrain (0.408), Location (0.225), Infrastructure (0.197), Legal (0.195) and Social factors (0.120). For factors affecting real estate prices, depending on the type of street that the factors affecting real estate prices have different roles. For streets with good business potential, the important factor is that the location of the land plot provides high profitability and stability leading to high buyer demand, followed by parcel shape factors, infrastructure conditions, living environment. The main purpose of a street to build houses is the natural environment, social factors, infrastructure, followed by shape and area. Among the 3 surveyed wards, An Cuu ward had the highest real estate price; followed by An Dong and An Tay wards.

Agricultural land partitioning model based on irrigation efficiency using a multi‐objective artificial bee colony algorithm

AbstractIn the process of agricultural land consolidation, the land parcels are optimally redesigned and rearranged in such a way that the dimensions of the resulting parcels are proportional to agricultural criteria such as irrigation discharge, soil texture, and cropping pattern. Besides these criteria, spatial factors like slope, road accessibility, volume of earthwork, and geometrical factors such as size and shape of parcels are also included in the design process of agricultural land partitioning. In this study, a land partitioning model was proposed using a multi‐objective artificial bee colony algorithm (MOABC‐LP) taking into consideration the mentioned factors. Initially, a feasible dimension range of parcels in a block was calculated based on irrigation efficiency. Two partitioning layouts were defined according to the topography and geometry of blocks. The proposed method was applied to a real study area and the results suggest that the land partitioning plan obtained by the MOABC‐LP model, in comparison with a designer's plan, not only makes the shape and size of parcels more compatible with the topographical and agricultural conditions of each block, but also reduces their cut‐and‐fill ratio.

Pemanfaatan Smart PTSL Sebagai Instrumen Pengumpul, Pengolah dan Pengintegrasi Data Fisik dan Data Yuridis Pendaftaran Tanah Sistematis Lengkap

One of the obstacles in achieving the target of PTSL is the difficulty in integrating physical and juridical data, as effect of inefficiency of data collection. This research aims to explain the benefit of using SMART PTSL as an instrument to collect, process and integrate physical and juridical data and to test relative accuracy of area, shape and position of land parcels mapped using SMART PTSL, compared to land parcels download from Geo-KKP. Mixed method by combining quantitative and qualitative methods was implemented. The sample was determined using purposive sampling method. The results show that SMART PTSL give benefit in (a) simplify the process of physical and juridical data integration; (b) shorten the process of physical and juridical data verification; (c) simplify data entry to KKP PTSL. The result of relative accuracy comparison of area, shape and location show that from 34 land parcels, 11 parcels were meet the tolerance, while 23 parcels were not. The tolerance was calculated using equation 0,5 ?L. Moreover, the result of tolerance calculation was affected by measurement method.

Analysis of the effect of land consolidation projects in terms of land fragmentation and parcel shapes: the case of Konya, Turkey

Agricultural land fragmentation and irregularity of parcel shapes are a structural land management problem that prevents the development of modern agriculture. Januszewski (JI) and Simmons (SI) indices are widely used to determine agricultural land parcel fragmentation. Shape index (SHI) and fractal dimension (FD) are also commonly used to evaluate parcel shapes. In this study, it is aimed to evaluate the land consolidation project in terms of fragmentation and change of parcel shapes. The purpose of land consolidation projects is to ensure the optimum use of the land and to obtain maximum benefit. The success level of land consolidation projects should be evaluated. Therefore, changes of land parcel fragmentation and parcel shape changes before and after the LC were analyzed using Candir District project data. In addition, the effects of LC project on parcel shape and size, the effect on parcel boundaries, and the effect of change of the distance between parcels on fuel saving and changes due to irrigation were investigated. As a result, according to Januszewski and Simmons indices, the ratio of farm enterprise whose index values were less than 0.40 was 1.17% and 3.7% before the LC, respectively, and decreased to 0.6% and 2.3% after the LC. The obtained values showed that the land parcel fragmentation decreased in Candir District. In addition, the ratio of farm enterprises whose SHI were greater than 1.60, which implies non-uniform geometric parcel shapes, was 6.5% before the LC, but this ratio decreased to 5.8% after the LC. While the ratio of farm enterprises having parcels, which were non-uniform according to FD values, was 1.6% before the LC, this rate decreased to 0.9% after the LC. These results show that JI and SI indexes can be used in land consolidation projects. In addition, SHI and FD indices are generally parameters that can measure how close the parcel shapes are to the smooth geometric shapes, but they do not give successful results in each parcel. Finally, according to the results of the survey conducted with the farmers, the LC provided a significant economic gain to farm enterprise owners in this region.

FACTORS EXPLAINING URBAN LAND VALUE VARIABILITY: A CASE STUDY IN ATAKUM DISTRICT, SAMSUN-TURKEY

The objective of this study was to determine the factors affecting urban land prices in Atakum district of Samsun province, Turkey. Agricultural lands in Atakum changed to urban lands especially within the last 20 years due to increasing population and public facilities such as; roads, tram line, schools, etc. Data used in this study were collected in questionnaires on 64 land parcels sold by real estate agents in 2017. The factors affecting urban land prices were determined with Hedonic Price Analysis. The significance of model parameters was tested, and the variability of land prices was verified for the hypothesized relationships between the significant parameters and the parcel price. The significant factors affecting urban land price were distance to coast, floor area ratio, parcel size, parcel shape, distance to tram station, post office and bus station.