User Density Research Articles

Introducing big data to measure the spatial heterogeneity of human activities for optimizing the ecological security pattern: A case study from Guangzhou City, China

Spatial heterogeneity of human activities (SHHA) is part of the heterogeneity of urban ecosystems, which influences the understanding of ecological processes and landscape functions. Few ecological security pattern (ESP) studies have comprehensively measured SHHA and explicitly explained its effect on ESP planning. It affects the efficiency and landscape functions of ESP planning, leading to challenges in maximizing urban development and ecosystem benefits. In this study, Tencent user density (TUD) data considering human activity for all time periods and point of interest (POI) density with fine-scale human activity location information were fused by using wavelet transform as the spatial distribution of human activities. We applied it to correct the resistance surface for ESP planning, then proposed targeted restoration and conservation policies at a fine scale by combining human activities and POIs. The results revealed that the corrected resistance surface could approach the heterogeneous megacity for spatial and functional structures. More importantly, ESP planning based on the corrected resistance surface could enhance efficiency and landscape functions. This research strengthens our understanding of the effect of SHHA on ESP planning. It may provide important insights for policymakers concerning integrated human-natural systems in landscape planning.

Exploiting Collaborative Computing to Improve Downlink Sum Rate in Satellite Integrated Terrestrial Networks

In this paper, we target at improving the downlink user sum rate in satellite integrated terrestrial network (SITN). Wherein, the densely deployed low earth orbit (LEO) satellites provide backhaul to terrestrial base stations (TBSs) and TBSs serve the ground users (GUs) over different bands. Note that the user sum rate is dependent on the user association and backhaul capacity. Moreover, the backhaul capacity is determined by multi-connectivity feature between the TBSs and LEO satellites. However, the densification and high dynamics of SITN aggravate the inter-satellite interference, which degrades the backhaul capacity. To this end, we propose a joint interference management and user association (JIMUA) scheme, which exploits collaborative computing to enhance the backhaul capacity and satisfy the user demands. The service demands of GUs and dwell duration of LEO satellites can be captured by introducing a collaborative computing coefficient. With this aid, the TBSs can predict the highly dynamic trajectory of dense deployed LEO satellites. Accordingly, the searching space of satellites can be reduced, the interference can be mitigated and backhaul capacity can be improved with high time efficiency. Moreover, the user association is achieved by dividing GUs into different groups, while the required user demands can be satisfied. Considering the dense deployment of LEO satellites, the user sum rate of SITN could be enhanced by JIMUA compared with the benchmark. With the well tuned collaborative computing coefficient, the backhaul capacity can be increased over 12%. Consequently, the user sum rate can be improved over 7%. Moreover, with the increasing user density, the gap between the user sum rate of JIMUA and the optimal solution can be reduced to less than 4%.

Cross-Cultural Comparison of Urban Green Space through Crowdsourced Big Data: A Natural Language Processing and Image Recognition Approach

Understanding the relationship between environmental features and perceptions of urban green spaces (UGS) is crucial for UGS design and management. However, quantifying park perceptions on a large spatial and temporal scale is challenging, and it remains unclear which environmental features lead to different perceptions in cross-cultural comparisons. This study addressed this issue by collecting 11,782 valid social media comments and photos covering 36 UGSs from 2020 to 2022 using a Python 3.6-based crawler. Natural language processing and image recognition methods from Google were then utilized to quantify UGS perceptions. This study obtained 32 high-frequency feature words through sentiment analysis and quantified 17 environmental feature factors that emerged using object and scene recognition techniques for photos. The results show that users generally perceive Japanese UGSs as more positive than Chinese UGSs. Chinese UGS users prioritize plant green design and UGS user density, whereas Japanese UGS focuses on integrating specific cultural elements. Therefore, when designing and managing urban greenspace systems, local environmental and cultural characteristics must be considered to meet the needs of residents and visitors. This study offers a replicable and systematic approach for researchers investigating the utilization of UGS on a global scale.

Spatial Non-Stationarity of Influencing Factors of China’s County Economic Development Base on a Multiscale Geographically Weighted Regression Model

The development of the county economy in China is a complicated process that is influenced by many factors in different ways. This study is based on multi-source big data, such as Tencent user density (TUD) data and point of interest (POI) data, to calculate the different influencing factors, and employed a multiscale geographically weighted regression (MGWR) model to explore their spatial non-stationarity impact on China’s county economic development. The results showed that the multi-source big data can be useful to calculate the influencing factor of China’s county economy because they have a significant correlation with county GDP and have a good models fitting performance. Besides, the MGWR model had prominent advantages over the ordinary least squares (OLS) and geographically weighted regression (GWR) models because it could provide covariate-specific optimized bandwidths to incorporate the spatial scale effect of the independent variables. Moreover, the effects of various factors on the development of the county economy in China exhibited obvious spatial non-stationarity. In particular, the Yangtze River Delta, the Pearl River Delta, and the Beijing-Tianjin-Hebei urban agglomerations showed different characteristics. The findings revealed in this study can furnish a scientific foundation for future regional economic planning in China.

Adaptive and Fair Deployment Approach to Balance Offload Traffic in Multi-UAV Cellular Networks

Unmanned aerial vehicle-aided communication (UAB-BS) is a promising solution to establish rapid wireless connectivity in sudden/temporary crowded events because of its more flexibility and mobility features than conventional ground base station (GBS). Because of these benefits, UAV-BSs can easily be deployed at high altitudes to provide more line of sight (LoS) links than GBS. Therefore, users on the ground can obtain more reliable wireless channels. In practice, the mobile nature of the ground user can create uneven user density at different times and spaces. This phenomenon leads to unbalanced user associations among UAV-BSs and may cause frequent UAV-BS overload. We propose a three-dimensional adaptive and fair deployment approach to solve this problem. The proposed approach can jointly optimize the altitude and transmission power of UAV-BS to offload the traffic from overloaded UAV-BSs. The simulation results show that the network performance improves by 37.71% in total capacity, 37.48% in total energy efficiency and 16.12% in the Jain fairness index compared to the straightforward greedy approach.

Identifying Adversary Impact Using End User Verifiable Key with Permutation Framework

In the Internet of Things (IoT), security is a crucial aspect that ensures secure communication, transactions, and authentication for different applications. In IoT security, maintaining the user interface and platform security is a critical issue that needs to be addressed due to leaky security distribution. During communication, synchronisation and security are important problems. The security problems are caused by the adversary impact and vulnerable attacks, leading to service failure. Therefore, the Permutated Security Framework (PSF) is designed to manage security in the IoT by providing secure communication, transactions, and authentication for different applications. The PSF uses time intervals to manage transaction security. These intervals are secured using end-verifiable keys generated using the conventional Rivest–Shamir–Adleman (RSA) technique in IoT-based communication-related applications. In this approach, the key validity is first provided for the interval, and in the latter, the access permitted time modifies its validity. The security of transactions is managed by dividing time into smaller intervals and providing different levels of security for each interval. By using time intervals, the framework is adaptable and adjustable to changes in the system, such as user density and service allocation rate, adapting parallel transactions per support vector classifications’ recommendations. The proposed framework aims to synchronise interval security, service allocation, and user flexibility to mitigate adversary impact, service failures, and service delays while improving the access rate and transactions. This allows for more flexibility and better management of transaction security. The proposed framework reduces adversary impact (10.98%), service failure (11.82%), and service delay (10.19%) and improves the access rate by 7.73% for different transactions.

Effective and Privacy-Preserving Estimation of the Density Distribution of LBS Users under Geo-Indistinguishability

With the widespread use of mobile devices, location-based services (LBSs), which provide useful services adjusted to users’ locations, have become indispensable to our daily lives. However, along with several benefits, LBSs also create problems for users because to use LBSs, users are required to disclose their sensitive location information to the service providers. Hence, several studies have focused on protecting the location privacy of individual users when using LBSs. Geo-indistinguishability (Geo-I), which is based on the well-known differential privacy, has recently emerged as a de-facto privacy definition for the protection of location data in LBSs. However, LBS providers require aggregate statistics, such as user density distribution, for the purpose of improving their service quality, and deriving them accurately from the location dataset received from users is difficult owing to the data perturbation of Geo-I. Thus, in this study, we investigated two different approaches, the expectation-maximization (EM) algorithm and the deep learning based approaches, with the aim of precisely computing the density distribution of LBS users while preserving the privacy of location datasets. The evaluation results show that the deep learning approach significantly outperforms other alternatives at all privacy protection levels. Furthermore, when a low level of privacy protection is sufficient, the approach based on the EM algorithm shows performance results similar to those of the deep learning solution. Thus, it can be used instead of a deep learning approach, particularly when training datasets are not available.

Reinforcement Learning Based Topology Control for UAV Networks

The recent development of unmanned aerial vehicle (UAV) technology has shown the possibility of using UAVs in many research and industrial fields. One of them is for UAVs moving in swarms to provide wireless networks in environments where there is no network infrastructure. Although this method has the advantage of being able to provide a network quickly and at a low cost, it may cause scalability problems in multi-hop connectivity and UAV control when trying to cover a large area. Therefore, as more UAVs are used to form drone networks, the problem of efficiently controlling the network topology must be solved. To solve this problem, we propose a topology control system for drone networks, which analyzes relative positions among UAVs within a swarm, then optimizes connectivity among them in perspective of both interference and energy consumption, and finally reshapes a logical structure of drone networks by choosing neighbors per UAV and mapping data flows over them. The most important function in the scheme is the connectivity optimization because it should be adaptively conducted according to the dynamically changing complex network conditions, which includes network characteristics such as user density and UAV characteristics such as power consumption. Since neither a simple mathematical framework nor a network simulation tool for optimization can be a solution, we need to resort to reinforcement learning, specifically DDPG, with which each UAV can adjust its connectivity to other drones. In addition, the proposed system minimizes the learning time by flexibly changing the number of steps used for parameter learning according to the deployment of new UAVs. The performance of the proposed system was verified through simulation experiments and theoretical analysis on various topologies consisting of multiple UAVs.

Deep Learning and Embedding Based Latent Factor Model for Collaborative Recommender Systems

A collaborative recommender system based on a latent factor model has achieved significant success in the field of personalized recommender systems. However, the latent factor model suffers from sparsity problems. It is also limited in its ability to extract non-linear data features, resulting in poor recommendation performance. Inspired by the success of deep learning in different application areas, we incorporate deep learning into our proposed method to overcome the above problems. In this paper, we propose a dual deep learning and embedding-based latent factor model that considers dense user and item feature vectors. The model combines the existing deep learning and latent factor models to extract deep abstractions and non-linear feature representations of the data for rating prediction. The core idea is to map the dense user and item vectors generated by embedding techniques into dual, fully connected deep neural network architectures. In these two separate architectures, it learns the non-linear representation of the input data. The method then predicts the rating score by integrating the factors obtained from the two independent structures using the inner product. From the experimental result, we observe that the proposed model outperformed state-of-the-art existing models in real-world datasets (MovieLens ML-100K and ML-1M).

Impact of various System Design Parameters on Coverage in Heterogeneous Cellular Networks

Recent cellular systems are moving towards heterogeneous cellular networks (HCNs) that consist of a mixture of miniature cells and legacy macro-cells to meet the requirements of wireless data traffic, owing to the immense amount of multi-purpose mobile applications. The inclusion of small cells is a cost-effective solution for enhancing the size and coverage of the existing macro-cellular network. This article assumes a heterogeneous cellular network consisting of two tiers of base stations (BSs): large-scale (macro) and small-scale (pico) BSs. The users are evenly distributed, and each tier of BSs and users create a uniform Poisson point process (PPP). Practical third-generation partnership project (3GPP) models for path loss are considered, and three camping/association criteria are utilized to relate user equipment (UEs) to large or small-scale BSs, including coupled and decoupled camping criteria to study coverage. The impact of several system design parameters on coverage is investigated using the aforementioned heterogeneous cellular network, association criteria, and 3GPP path loss models. Our simulation results provide insights into the effect of infrastructure sharing between macro and pico-cells and user density on coverage. We also explore the impact of fractional power control (FPC) and signaling limits on coverage under all considered association strategies. Finally, we investigate the effect of open-loop UE transmission power, pico-density, and biasing on coverage. Specifically, we thoroughly explore the effect of empty BSs on coverage under all system design parameters.

Energy-Efficient Sleep Mode Schemes for Cell-Less RAN in 5G and Beyond 5G Networks

In 5G and beyond 5G networks, the new cell-less radio access network architecture is adopted to overcome the extreme network capacity challenges generated by massive wireless devices used for diverse scenarios and various applications. At the same time, the evolution of mobile communications faces the important challenge of increased network power consumption. To fulfill user demands for various user densities and meanwhile reduce the power consumption, we present a novel energy-efficiency enhancement scheme, i.e., <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(3\times E)$ </tex-math></inline-formula> to increase the transmission rate per energy unit, with stable performance within the cell-less radio access network (RAN) architecture. Our proposed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(3\times E)$ </tex-math></inline-formula> scheme activates two-step sleep modes (i.e., certain phase and conditional phase) through the intelligent interference management for temporarily switching access points (APs) to sleep, optimizing the network energy efficiency (EE) in highly loaded scenarios, as well as in scenarios with lower load. An intelligent control over underutilized/unused APs is considered, taking their interference contribution into account as the primary main criteria in addition to load-based conditional criteria. Therefore, our proposed scheme assures a stable performance enhancement and maintains an efficient power saving when the number of UEs increases, improving existing works not addressing this performance stability in peak-traffic hours. Simulation results show that the network EE is improved up to 30% compared to the reference algorithm and up to 60% with respect to the baseline algorithm in which all APs are active all the time.

Cloudlet Based Computing Optimization Using Variable-Length Whale Optimization and Differential Evolution

Cloudlet-based optimization involves deploying a set of cloudlets in an environment and assigning user tasks to optimize various metrics, including energy consumption, quality of service (QoS), and cost. Typically, approaches deal with them separately, which might cause sub-optimality. Furthermore, assuming the fixed location of the cloudlets will limit the dynamic adaptability of the problem. Enabling more optimality and adaptability to the dynamic nature of cloudlet-based computing, we propose a novel Variable-Length multi-objective Whale optimization Integrated with Differential Evolution designated as VL-WIDE. Unlike the existing optimization algorithm, VL-WIDE features the capability of searching different lengths of solutions to cover the variable number of cloudlets for deployment. Furthermore, it enables a non-dominated evaluation of solutions based on four objectives using crowding distance for selection. It provides an application-oriented solutions repair operator for repairing non-valid solutions and assuring that all solutions are generated in the feasible region. The proposed algorithm enables moving the cloudlets among pre-defined locations to increase the quality of service according to the change in the user density caused by user mobility. Comparing this developed algorithm with other algorithms shows its superiority in multi-objective optimization (MOO) evaluation metrics. VL-WIDE has provided the best in a number of non-dominated solutions and delta metrics and was competitive in other metrics.

Obstacle Avoidance System for Autonomous Vehicles

Recent cellular systems are moving towards heterogeneous cellular networks (HCNs) that consist of a mixture of miniature cells and legacy macro-cells to meet the requirements of wireless data traffic, owing to the immense amount of multi-purpose mobile applications. The inclusion of small cells is a cost-effective solution for enhancing the size and coverage of the existing macro-cellular network. This article assumes a heterogeneous cellular network consisting of two tiers of base stations (BSs): large-scale (macro) and small-scale (pico) BSs. The users are evenly distributed, and each tier of BSs and users creates a uniform Poisson point process (PPP). Practical third-generation partnership project (3GPP) models for path loss are considered, and three camping/association criteria are utilized to relate user equipment (UEs) to large or small-scale BSs, including coupled and decoupled camping criteria to study coverage. The impact of several system design parameters on coverage is investigated using the aforementioned heterogeneous cellular network, association criteria, and 3GPP path loss models. Our simulation results provide insights into the effect of infrastructure sharing between macro and pico-cells and user density on coverage. We also explore the impact of fractional power control (FPC) and signaling limits on coverage under all considered association strategies. Finally, we investigate the effect of open-loop UE transmission power, pico-density, and biasing on coverage. Specifically, we thoroughly explore the effect of empty BSs on coverage under all system design parameters.

Modeling and Analysis of UAV-Assisted Mobile Network with Imperfect Beam Alignment

With the rapid development of emerging 5G and beyond (B5G), Unmanned Aerial Vehicles (UAVs) are increasingly important to improve the performance of dense cellular networks. As a conventional metric, coverage probability has been widely studied in communication systems due to the increasing density of users and complexity of the heterogeneous environment. In recent years, stochastic geometry has attracted more attention as a mathematical tool for modeling mobile network systems. In this paper, an analytical approach to the coverage probability analysis of UAV-assisted cellular networks with imperfect beam alignment has been proposed. An assumption was considered that all users are distributed according to Poisson Cluster Process (PCP) around base stations, in particular, Thomas Cluster Process (TCP). Using this model, the impact of beam alignment errors on the coverage probability was investigated. Initially, the Probability Density Function (PDF) of directional antenna gain between the user and its serving base station was obtained. Then, association probability with each tier was achieved. A tractable expression was derived for coverage probability in both Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) condition links. Numerical results demonstrated that at low UAVs altitude, beam alignment errors significantly degrade coverage performance. Moreover, for a small cluster size, alignment errors do not necessarily affect the coverage performance.

Çocuk Oyun Alanlarındaki Gürültünün Değerlendirilmesi: Dokuma Park Örneği

The increase in the world population and rapid urbanization cause different types of pollution such as air, water, soil and noise. Today, this situation significantly affects the quality of life in densely populated settlements both in the world and in Turkey. Noise pollution, which is one of the important types of pollution, especially in urban areas, is increasing day by day due to anthropogenic effects. Studies conducted by the European Noise Directive (END) show that noise pollution is one of the important factors that cause health problems. Parks are places that allow children trapped in the structural environment in urban life to get to know nature and play games in open spaces. In particular, the children's playgrounds in the parks allow children to play and socialize in the open area and also allow parents to rest. However, park users may be exposed to noise pollution due to high-level noises originating from vehicle traffic, user density, and children's playgrounds in these places. This situation adversely affects the physical and mental health of the park users. The aim of the research is to determine the noise level in children's playgrounds in parks and to develop suggestions for reducing this noise level. As a result of the preliminary studies carried out in this context, Antalya/Kepez Dokuma Park was determined as a research area. Zones were created at certain intervals around the children's playground in the research area. And noise levels were measured on weekdays and weekends from the noise measurement points determined in these zones. The results obtained show that the noise pollution caused by the children's playgrounds in the parks and environmental factors is at a high level. In this context, landscape design and planning proposals have&#x0D; been developed to reduce noise pollution from children's playgrounds and environmental factors.

Dynamics of a Discrete Lotka–Volterra Information Diffusion Model

To explore the process of online social network information interaction, in this paper, we analyze the dynamics of a discrete Lotka–Volterra information diffusion model. Using the center manifold theorem, the conditions for transcritical bifurcation and flip bifurcation are obtained. With the help of approximation by a flow and Picard iteration, we explore the qualitative structures and stability of degenerate fixed point of the model with eigenvalues [Formula: see text]. What’s interesting is that our results reveal a new and complex qualitative structure for fixed point, which are different from the previous reports and called degenerate saddle point. Additionally, the qualitative structures provide a new idea for investigation the stability of degenerate fixed point. Meanwhile, near the maximum user density, the dynamic results of degenerate fixed point indicate that if the intervention rate is greater than the inverse of the maximum user density, then the higher user density decreases, the lower user density increases when intrinsic growth rates are small (between 0 and 2). However, when the intrinsic growth rate is greater than 2, the high user density will continue to increase until it approaches the maximum user density indefinitely, while the small user density will approach 0, which provide us with new insights into information diffusion. Finally, we show the results of the model by numerical simulations, and the characteristics of information diffusion near the degenerate fixed point are predicted by theoretical analysis.

Implementasi Arsitektur Firewall Sebagai Sistem Keamanan Jaringan Wifi 802.11ax Aruba Software Defined Wan (SD-Wan)

The concept of WLAN technology continues to evolve so as to be able to keep up with the increasing number of user densities, schemes with density Access Point (AP) and User Stations (STA), IEEE 802.11 creates an IEEE 802.11ax (TGax) to develop a set of physical layer specifications (PHY) and Media Access New Control (MAC). IEEE 802.11ax is called a high efficiency WLAN (HE). currently in the concept of its application in the implementation of the firewall architecture on the 802.11ax WIFI network security system. This study aims to determine the concept of an 802.11ax Wifi network security system architecture developed by Aruba with the latest Access Point system that is able to support the latest Wi-Fi standards and is a wireless Access Point product that may be the first to get Wi-Fi Alliance (WFA) certification in Indonesia. the field of the latest WPA3 and Enhanced Open security system standards, and can provide much stronger encryption services and have simpler IoT security configuration settings. This research uses the literature method where the researcher collects data - information data related to the subject of the research conducted by the researcher. As well as using the data analysis technique used is using content analysis in the form of written documentation and organizing data into categories, describing it into units, what is meant by data analysis techniques. The results of this study.

A Multiprotocol Controller Deployment in SDN-Based IoMT Architecture

Internet of Medical Things (IoMT) as a next-generation network requires heterogeneous services, technologies, and equipment infrastructure management resulting in more complex systems. The software-defined networking (SDN) approach has emerged as a promising solution to reduce this complexity by proposing a vendor-independent structure that disaggregates the control and data planes. In this study, an architecture based on the SDN is proposed for such heterogeneous and complex IoMT networks. A new controller that supports different wireless communication protocols has been developed for the control plane. We propose machine learning (ML)-based load balancing and time-sensitive prioritization (MLA) algorithms for dense and dynamic networks. An SDN-based IoMT network that consists of IEEE 802.15.6, TDMA, and IEEE 802.11 protocols is analyzed in a simulation program simultaneously using various scenarios in terms of throughput, delay, packet loss ratio, bit error rate, and user density parameters. In addition, in this study, a new data set is created for load balancing. The performances of support vector machine (SVM), ensemble of decision trees, k-NN, and Naive Bayes ML algorithms are compared, and SVM gives the best result with 95.1% accuracy.

Cooperative MEC With Non-Uniform User Density: Resource Planning and State Control Optimization

Mobile edge computing (MEC) allows mobile devices to offload computationally intensive tasks to nearby base stations equipped with computational resources. MEC also provides a solution for supporting low latency responses. However, MEC increases the system power consumption by adding extra computational units. It is important to carefully plan computational resources to achieve a balance between quality of service (QoS) and power consumption. Moreover, the MEC system control should shut down unused resources when traffic rates fall to further decrease the power consumption. In this letter, we investigate a workload-based core power state control for cooperative MEC systems and a heterogeneous resource planning method. We also formulate a joint resource planning and workload-threshold optimization problem for power consumption minimization. The problem is solved using a hybrid method using metaheuristic and heuristic algorithms. The problem is first solved for an equivalent uniform system using integer gaining and sharing knowledge (IGSK). The resources are then redistributed over the base stations using a heuristic that considers the effect of power state control on core distribution. The results show that the iterative algorithm can accomplish a satisfactory QoS with the same number of resources obtained by other approaches or less. Additionally, threshold control reduced the system power consumption by 19.6%.

Ekosistem hizmeti algısı ve kullanıcı tercihlerine yönelik bir araştırma: Arhavi Sahil Parkı örneği

Kentsel ekosistemlerin önemli bir parçası olan kent parkları, kente ve kentlilere doğrudan ve dolaylı ekosistem hizmetleri sağlayan alanlardır. Bu alanların ve sağladığı ekosistem hizmetlerinin sürdürülebilirliği için alan kullanıcılarının tercihlerinin ve alanların sağladığı servislere ilişkin algıların belirlenmesi de önem arz etmektedir. Buradan hareketle çalışma alanı olarak Arhavi Sahil Parkı seçilmiştir. Çalışmada kullanıcı yoğunluğunu ve tercihlerini belirlemek için gözlem tekniğinden, kullanıcı tercihlerini ve algılarını belirlemek için de anket tekniğinden faydalanılmıştır. Anket çalışması çevrimiçi olarak 310 katılımcı ile yürütülmüştür. Elde edilen veriler doğrultusunda, çalışma alanının haftada en az bir defa kullanıldığı, öğleden sonra ve akşam saatlerinde daha yoğun kullanıldığı, daha çok yaz mevsiminde kullanıldığı, yürüyüş, koşu vb. etkinlikler için daha çok tercih edildiği tespit edilmiştir. Mekânsal olarak ise yine yürüyüş yolları ve çocuk oyun alanı ve çevresinin tercih edildiği belirlenmiştir. Elde edilen bu bulgular, gözlem çalışmasından elde edilen bulgularla örtüşmektedir. Anket çalışmasından elde edilen bulgular ışığında, sahil parkı kullanıcılarının parkın sağladığı kaynak sağlayan, düzenleyici, destekleyici ve özellikle kültürel ekosistem hizmetlerini algıladıkları söylenebilir. Sonuç olarak, parkın dört mevsim kullanılabileceği mekânsal çözümlerin yanı sıra sağladığı ekosistem hizmetlerinin algı düzeylerinin arttırılması yönünde de çeşitli öneriler getirilmiştir. Tercih ve algı çalışmalarının tasarımcılar, planlamacılar ve alan yöneticileri için yol gösterici olacağı düşünülmektedir.