Advancing contiguous environmental land allocation analysis, planning and modeling

Uncertainty has always been a significant yet unresolved issue in geography. Uncertainty in spatial optimization models, particularly in coverage maximization problems, stems from the limitations of binary coverage metrics for areal-based demand units. Traditional models classify units as either fully covered or not, ignoring partial coverage scenarios. This binary approach creates discrepancies between modeled and actual service demand, as even marginally covered units may count as fully serviced. The propagation of such errors is amplified by variations in the unit size and coverage thresholds, leading to suboptimal solutions in urban planning tasks such as urban greenway planning. To address these limitations, we proposed a deep reinforcement learning (DRL)-driven optimization framework that quantifies the optimality gap caused by spatially induced uncertainty and provides superior solutions. By employing a three-module structure that incorporates a graph neural network (GNN)-driven agent for data representation, a policy and a value network for decision-making, and a stochastic environment for simulating individual-level demand distributions, our model dynamically adapts to spatial uncertainty and generates robust solutions. We tested our framework in solving the maximal covering location problem for lines (MCLP-Line) for urban greenway route planning. The experimental results demonstrated that traditional mixed integer linear programming (MILP) methods yielded highly variable outcomes with different coverage metrics, whereas our proposed model consistently outperformed MILP across diverse scenarios. Our findings underscore the potential of modern AI-driven approaches in mitigating spatial uncertainty and achieving optimized solutions in geographic applications.

Bioethanol supply chain network design considering land characteristics

Towards balanced development stage: Regulating the spatial pattern of agglomeration with collaborative optimal allocation of urban land

Aim of study: This study aims to investigate the interactions between land use classes, population change, and forest crimes in the Tekirdağ Forest Planning Unit for the period 2007-2023. Area of study: Tekirdağ was chosen as the case study area because it is one of the provinces that receive the most immigration in Türkiye. Material and method: Stand maps, population information, and forest crime numbers were obtained for use in the study. Land use classes for 2008 and 2016 were created using stand maps. Additionally, forest crime numbers for the 2002-2023 period and population data for the 2007-2023 period were provided. Changes in land use classes, population, and forest crimes were examined according to the relevant years, and the relationships between them were investigated. The relationships between population and forest crimes were determined by Pearson's correlation coefficient. Main results: The most significant changes in land use classes were determined as an increase of 6138.8 ha in broadleaf forests and a decrease of 13673.0 ha in agricultural areas. During the 2007-2023 period, the population increased continuously at variable rates. The relationships between annual population and forest crimes were examined in the 2007-2023 period, and it was determined that the highest relationship was negative between population and grazing crime (r=-0.641). Research highlights: In-migration in the study area and the decrease in the rural population have been effective in forest crimes committed in the region. In-migration has led to a decrease in grazing crime and an increase in individual occupation crimes in rural areas.

One of the aims of the Seveso III Directive is to grant safe and coherent land use in areas exposed to major accident risk coming from industrial plants.The rules for land use planning have been coded in the Italian Decree (May 9, 2001) having the title "Minimum safety requirements for civil and industrial land planning in area affected by major accidents risk".The authors will analyze some substantial problems: many residential areas were built in the sixties just adjacent to industrial areas, with the aim of granting short trips for workers from home to work.The Seveso III Directive now aims to grant safety in land planning, but it is often too late when the land is overexploited, and you have to choose between industrial and civil rights.No new industry or change of existing installations can impose limitation in land use, which is ruled by a number of different local Authorities, each having its role and responsibility and often using different criteria for land use planning, different standards and guidelines, different ways, "languages" and time schedules to code the land use planning rules and different competences (process engineers, architects, land use managers, advocates, etc.).All the Authorities having jurisdiction should collaborate in order to address the compatibility criteria and identify the 'sensible receptors'.A Decision Support System (DSS) has been designed in order to make all the different participants work with their competence in their "language" and manage the land use planning issues in a coherent way as a team.

The general land use planning is to arrange all kinds of land use behaviors in an orderly manner, and its own disorder will inevitably affect the scientific and authoritative nature of the general land use planning, which makes the effect of land management unsatisfactory. The reason is that there are differences in the spatial scale of general land use planning, which makes the analysis of land information not comprehensive enough. Therefore, this paper puts forward the dynamic planning of sustainable land use based on GIS and symmetry algorithm. The collected land information is preprocessed by ArcGIS, and the land remote sensing images are classified by decision tree combined with ENVI (Visual Image Environment), using local sparse coding to extract land use features, with enhancement and reconstruction of remote sensing images using symmetric algorithm. On this basis, the factors that limit the spatial expansion of land are analyzed, and the land planning is completed by substituting them into Arc Map software. Experiments show that the average accuracy rate of this method for land planning reaches 91.22%, which can effectively complete land planning.

Why do societies implement land policies? A number of arguments have been put forward in the literature, ranging from economic conceptions based on market failure and the problem of negative externalities to a more social conception based on welfare distribution and collaborative planning. However, neither all societies with similar market failures or negative externalities develop and implement land planning nor implemented land planning always results from collaborative planning. The arguments found in the literature seem not to fit the reality and, in most cases, cannot explain why societies create or undertake innovations in land planning. Within the framework of institutional change theory and based on the analysis of the emergence of two land planning devices—a land use law and a land banking law—in Galicia, Northwest Spain, this paper argues that land planning is developed to tackle negative outcomes of former institutional setups. However, the negativity of such outcomes is measured not in terms of economic performance, but in terms of social acceptance. The search for a workable definition of property within the Galician society seems to be the main driver of institutional change in land use planning analysed in this paper.

Land management and planning is crucial for present and future use of land and the sustainability of land resources. Physical, biological and cultural characteristics of land can be used to define Land Management Units (LMUs) that aid in decision making for managing land and communicating information between different research and application domains. This study aims to describe the classification of ecologically relevant land units that are suitable for land management, planning and conservation purposes. Relying on the idea of strong correlation between landform and potential landcover, a conceptual model for creating Land Management Units (LMUs) from topographic data and biophysical information is presented. The proposed method employs a multi-level object-based classification of Digital Terrain Models (DTMs) to derive landform units. The sensitivity of landform units to changes in segmentation scale is examined, and the outcome of the landform classification is evaluated. Landform classes are then aggregated with landcover information to produce ecologically relevant landform/landcover assemblages. These conceptual units that constitute a framework of connected entities are finally enriched given available socio-economic information e.g., land use, ownership, protection status, etc. to generate LMUs. LMUs attached to a geographic database enable the retrieval of information at various levels to support decision making for land management at various scales. LMUs that are created present a basis for conservation and management in a biodiverse area in the Black Sea region of Turkey.

Transparency and uniformity of registration of land use restrictions are interdependent and complementary aspects critical for ensuring effective management of land resources. The interaction of transparency and uniformity creates conditions for accessibility, comprehensibility, and better perception of data. The study analyses land use restrictions identification during their registration in the State Land and Urban Planning Cadastres to determine the uniformity of the registration. The analysis mainly focuses on the main attributes of the land use restrictions, such as code and name. Based on the research results, we established that the classification of land use restrictions for registration in various state cadastral systems, particularly State Land and Urban Planning Cadastres, is mostly not identical and requires coordination. There is an unjustified duplication of some land use restrictions and ambiguity in assigning them to specific categories. A particular problem is coding land use restrictions as registration objects without considering the hierarchical spatial organisation of restriction zones and the principle of logical construction. There is also an inexplicable duplication of some categories of land use restrictions, as in: the protected archaeological territory (codes 06.06.3 and 16.04), the historic and cultural protected territory (codes 06.06.5 and 16.03), the historical and cultural reserve (codes 06.06.4 and 16.02), the historical area of ​​the inhabited place (codes 06.06.2 and 16.07). In addition, there are minor differences in the names. It is worth noting that correctly identifying land use restrictions is essential for developing local urban planning documents due to the requirements of integrating information from various sources and updating them regularly. Transparency and uniformity of registration of land use restrictions in the State Land Cadastre and Urban Planning Cadastre are the main factors contributing to the effective management of land resources, ensuring legal certainty, attracting investments, and protecting the rights of land owners. They also contribute to reducing corruption, increasing trust in public authorities, and protecting the environment. Keywords: restrictions, code, state land cadastre, urban planning cadastre.

FIXED DRUG ERUPTION TO FLUCONAZOLE IN A 28 YEAR OLD WOMAN WITH RECURRENT VAGINAL CANDIDIASIS

This thesis presents the outcomes of a comprehensive research study undertaken to investigate the influence of rainfall and catchment characteristics on urban stormwater quality. The knowledge created is expected to contribute to a greater understanding of urban stormwater quality and thereby enhance the design of stormwater quality treatment systems. The research study was undertaken based on selected urban catchments in Gold Coast, Australia. The research methodology included field investigations, laboratory testing, computer modelling and data analysis. Both univariate and multivariate data analysis techniques were used to investigate the influence of rainfall and catchment characteristics on urban stormwater quality. The rainfall characteristics investigated included average rainfall intensity and rainfall duration whilst catchment characteristics included land use, impervious area percentage, urban form and pervious area location. The catchment scale data for the analysis was obtained from four residential catchments, including rainfall-runoff records, drainage network data, stormwater quality data and land use and land cover data. Pollutants build-up samples were collected from twelve road surfaces in residential, commercial and industrial land use areas. The relationships between rainfall characteristics, catchment characteristics and urban stormwater quality were investigated based on residential catchments and then extended to other land uses. Based on the influence rainfall characteristics exert on urban stormwater quality, rainfall events can be classified into three different types, namely, high average intensity-short duration (Type 1), high average intensity-long duration (Type 2) and low average intensity-long duration (Type 3). This provides an innovative approach to conventional modelling which does not commonly relate stormwater quality to rainfall characteristics. Additionally, it was found that the threshold intensity for pollutant wash-off from urban catchments is much less than for rural catchments. High average intensity-short duration rainfall events are cumulatively responsible for the generation of a major fraction of the annual pollutants load compared to the other rainfall event types. Additionally, rainfall events less than 1 year ARI such as 6- month ARI should be considered for treatment design as they generate a significant fraction of the annual runoff volume and by implication a significant fraction of the pollutants load. This implies that stormwater treatment designs based on larger rainfall events would not be feasible in the context of cost-effectiveness, efficiency in treatment performance and possible savings in land area needed. This also suggests that the simulation of long-term continuous rainfall events for stormwater treatment design may not be needed and that event based simulations would be adequate. The investigations into the relationship between catchment characteristics and urban stormwater quality found that other than conventional catchment characteristics such as land use and impervious area percentage, other catchment characteristics such as urban form and pervious area location also play important roles in influencing urban stormwater quality. These outcomes point to the fact that the conventional modelling approach in the design of stormwater quality treatment systems which is commonly based on land use and impervious area percentage would be inadequate. It was also noted that the small uniformly urbanised areas within a larger mixed catchment produce relatively lower variations in stormwater quality and as expected lower runoff volume with the opposite being the case for large mixed use urbanised catchments. Therefore, a decentralised approach to water quality treatment would be more effective rather than an end-of-pipe approach. The investigation of pollutants build-up on different land uses showed that pollutant build-up characteristics vary even within the same land use. Therefore, the conventional approach in stormwater quality modelling, which is based solely on land use, may prove to be inappropriate. Industrial land use has relatively higher variability in maximum pollutant build-up, build-up rate and particle size distribution than the other two land uses. However, commercial and residential land uses had relatively higher variations of nutrients and organic carbon build-up. Additionally, it was found that particle size distribution had a relatively higher variability for all three land uses compared to the other build-up parameters. The high variability in particle size distribution for all land uses illustrate the dissimilarities associated with the fine and coarse particle size fractions even within the same land use and hence the variations in stormwater quality in relation to pollutants adsorbing to different sizes of particles.

Regional electric power system planning using mixed integer linear programming

This article is about the current agricultural scenario, where large‐scale production causes large amounts of food to be transported to various points of consumption, causing substantial emissions of so‐called greenhouse gases and increasing the carbon footprint. Land use optimization and land parcel allocation are essential areas of agriculture research that currently represent relevant challenges and are classified as combinatorial optimization problems. In this context, the Selection and Allocation of Land Parcels Problem (SA‐LPP) is proposed; its goal is to optimize the selection and allocation of land parcels with rectangular shapes in small areas available for food production. We propose a reformulation of the SA‐LPP as a variant of the two‐dimensional orthogonal packing problem (2OPP), called Group‐2OPP. This problem was solved through a Mixed‐Integer Linear Programming (MILP) model, but due to the model complexity, we also propose a Greedy Randomized Adaptive Search Procedure metaheuristic approach. Some sensitivity analyses were performed as well to evaluate the impact of parameters on the solutions. Computational results show that the proposed metaheuristic outperforms the MILP model in terms of solution quality and computational times.

This study examined the intra-urban freight trips pattern in Lagos State, Nigeria. Major traffic bottlenecks along selected roads served as data collection stations for this study. At each station, data were collected on type of vehicle, origin of trip, destination of trip, land use at origin and land use at destination by trained field assistants. A combination of observatory survey and questionnaire administration to freight vehicle drivers was used for the data collection. A total of 12,533 drivers of freight vehicles who at the time of the survey were conveying goods in their vehicles for delivery were randomly sampled. Both descriptive and inferential statistical techniques were employed for data analysis. Simple percentages, averages, tabulation and charts are some of the descriptive statistical techniques employed to measure the spread of observations. Two-Way Analysis of Variance (ANOVA) was used to test the hypothesis that “freight vehicle’s trips do not vary by land use types in the study area”. Urban land use was found to significantly influence freight trips. The study revealed that commercial, industrial, residential and circulatory land uses are the pivots upon which urban freight movement revolves in the study area. Commercial land use is the largest generator (42%) and attractor (34%) of urban freight trips. Industrial land use generates 28% and attracts 20% of the urban freight trips, while residential land use generates 4% and attracts 26%. Also, circulatory land use generates 17% and attracts 8% of the urban freight trips. The analysis also revealed that small trucks are the commonly used vehicles for intra-urban freight movement in the study area accounting for about 47% of the freight trips. Also, freight vehicles’ trips significantly varied by land use types, F (4, 1) = 3097.39, P = 0.13. This study concludes that land use types played a significant role in the pattern of freight traffic distribution in the study area. It was therefore recommended that efforts should be directed towards effective freight transport planning and management in the study area.

Land use, scale, and bird distributions in the Phoenix metropolitan area