Visual Analysis Method Research Articles

Effectiveness of Interactive Learning Media Based on Articulate Storyline in Increasing the Ability to Understand the Concept of a Healthy Home in Children with Cerebral Palsy

This research was motivated by problems that occurred in one cerebral palsy student who was not yet able to understand the concept of a healthy home in class VIII. To overcome this, researchers aim to improve the ability to recognize the concept of a healthy home in children with cerebral palsy by using articulate storyline based interactive learning media. This type of research is experimental research using a quantitative approach in the form of Single Subject Research (SSR) with A-B-A design. Variable X in this research is interactive learning media based on an articulate storyline, while variable Y in this research is the ability to recognize the concept of a healthy home. The data collection techniques used are observation, tests and documentation. The data collection tool in this research is a test instrument in the form of a checklist. Data analysis was carried out using visual analysis methods. The research results showed that there was an increase in the ability to recognize the concept of a healthy home in children with cerebral palsy by using articulate storyline-based interactive learning media. This is proven by condition A1, during 3 meetings the child obtained a score of 45.4%, 45.4%, 45.4%. Condition B during 7 meetings the child obtained a score of 51.5%, 63.6%, 66.6%, 87.6%, 100%, 100%, 100%. Then, in condition A2 for 3 meetings the child obtained a score of 96.9%, 96.6%, 96.6%. After analysis, articulate storyline based interactive learning media was proven to be able to increase the ability to recognize the concept of a healthy home in children with cerebral palsy.

Visual analysis method for unmanned pumping stations on dynamic platforms based on data fusion technology

As the scale of water conservancy projects continues to expand, the amount and complexity of analytical data have also correspondingly increased. At present, it is difficult to realize project management decision support based on a single data source, and most manual analysis methods not only have high labor costs, but also are prone to the risk of misjudgment, resulting in huge property losses. Based on this problem, this paper proposes visual analysis method for unmanned pumping stations on dynamic platforms based on data fusion technology. First, the method uses the transfer learning method to enable ResNet18 obtain generalization ability. Secondly, the method uses ResNet18 to extract image features, and outputs fixed length sequence data as the input of long short-term memory (LSTM). Finally, the method uses LSTM outputs the classification results. The experimental results demonstrate that the algorithm model can achieve an impressive accuracy of 99.032%, outperforming the combination of traditional feature extraction and machine learning methods. This model effectively recognizes and classifies images of pumping stations, significantly reducing the risk of accidents in these facilities.

Intellectual Structure of Global Value Chain Research: Visualization and Bibliometric Analysis Based on VOSviewer

Over the past few decades, numerous scholars have conducted research on global value chains, and the amount of literature in this field has grown rapidly to become “big data.” In order to deeply analyze and explore the current research status and development trends in the field of global value chains, this article conducted a systematic and quantitative study on the global value chain. Based on the Web of Science Core Collection SCIE and SSCI databases, this article searched 8273 articles published between 2001 and 2021 with the theme of “Global Value Chain” and used visualization and bibliometric analysis methods. The research field was analyzed using VOSviewer software. Research has shown that the number of papers published in the global value chain field has been continuously increasing over time and has entered a period of rapid development since 2016. The main disciplines are economics and management. A few researchers and research institutions have a strong influence, but the cooperation between scholars and institutions is relatively weak. The United States, the United Kingdom, and China dominate the research field. The complex impact of the natural environment and public health on the global value chain, and the impact of big data and artificial intelligence on the global value chain, has become a new direction for future research. Countries should actively promote the development of the digital economy and green economy, promote the effective allocation of global value chain resources by enterprises, and adhere to the development of good bilateral political relations to reduce the uncertainty of enterprise participation in the global value chain caused by political risks.

Tissue distribution of metabolites in Cordyceps cicadae determinedby DESI-MSI analysis.

In this paper, we establish an in situ visualization analysis method to image the spatial distribution of metabolites in different parts (sclerotium, coremium) and different microregions of Cordyceps cicadae (C. cicadae) to achieve the in situ visual characterization of tissues for a variety of metabolites such as nucleosides, amino acids, polysaccharides, organic acids, fatty acids, and so on. The study included LC-MS chemical composition identification, preparation of C. cicadae tissue sections, DEDI-MSI analysis, DESI combined with Q-TOF/MS to obtain high-resolution imaging of mass-to-charge ratio and space, imaging of C. cicadae in positive-negative ion mode with a spatial resolution of 100μm, and localizing and identifying its chemical compositions based on its precise mass. A total of 62 compounds were identified; nucleosides were mainly distributed in the coremium, L-threonine and DL-isoleucine, and other essential amino acids; peptides were mainly distributed in the sclerotium of C. cicadae; and the rest of the amino acids did not have a clear pattern; sugars and sugar alcohols were mainly distributed in the coremium of C. cicadae; organic acids and fatty acids were distributed in the nucleus of C. cicadae more than in the sclerotium, and the mass spectrometry imaging method is established in the research. The mass spectrometry imaging method established in this study is simple and fast and can visualize and analyse the spatial distribution of metabolites of C. cicadae, which is of great significance in characterizing the metabolic network of C. cicadae, and provides support for the quality evaluation of C. cicadae and the study of the temporal and spatial metabolic network of chemical compounds.

Predicting the thermal protective performance of flame-retardant fabric based on machine learning

PurposeThe purpose of this study is to predict the thermal protective performance (TPP) of flame-retardant fabric more economically using machine learning and analyze the factors affecting the TPP using model visualization.Design/methodology/approachA total of 13 machine learning models were trained by collecting 414 datasets of typical flame-retardant fabric from current literature. The optimal performance model was used for feature importance ranking and correlation variable analysis through model visualization.FindingsFive models with better performance were screened, all of which showed R2 greater than 0.96 and root mean squared error less than 3.0. Heat map results revealed that the TPP of fabrics differed significantly under different types of thermal exposure. The effect of fabric weight was more apparent in the flame or low thermal radiation environment. The increase in fabric weight, fabric thickness, air gap width and relative humidity of the air gap improved the TPP of the fabric.Practical implicationsThe findings suggested that the visual analysis method of machine learning can intuitively understand the change trend and range of second-degree burn time under the influence of multiple variables. The established models can be used to predict the TPP of fabrics, providing a reference for researchers to carry out relevant research.Originality/valueThe findings of this study contribute directional insights for optimizing the structure of thermal protective clothing, and introduce innovative perspectives and methodologies for advancing heat transfer modeling in thermal protective clothing.

Multi-Modal Information Fusion for Localization of Emergency Vehicles

In urban and city environments, road transportation contributes significantly to the generation of substantial traffic. However, this surge in vehicles leads to complex issues, including hindered emergency vehicle movement due to high density and congestion. Scarcity of human personnel amplifies these challenges. As traffic conditions worsen, the need for automated solutions to manage emergency situations becomes more evident. Intelligent traffic monitoring can identify and prioritize emergency vehicles, potentially saving lives. However, categorizing emergency vehicles through visual analysis faces difficulties such as clutter, occlusions, and traffic variations. Visual-based techniques for vehicle detection rely on clear rear views, but this is problematic in dense traffic. In contrast, audio-based methods are resilient to the Doppler Effect from moving vehicles, but handling diverse background noises remains unexplored. Using acoustics for emergency vehicle localization presents challenges related to sensor range and real-world noise. Addressing these issues, this study introduces a novel solution: combining visual and audio data for enhanced detection and localization of emergency vehicles in road networks. Leveraging this multi-modal approach aims to bolster accuracy and robustness in emergency vehicle management. The proposed methodology consists of several key steps. The presence of an emergency vehicle is initially detected through the preprocessing of visual images, involving the removal of clutter and occlusions via an adaptive background model. Subsequently, a cell-wise classification strategy utilizing a customized Visual Geometry Group Network (VGGNet) deep learning model is employed to determine the presence of emergency vehicles within individual cells. To further reinforce the accuracy of emergency vehicle presence detection, the outcomes from the audio data analysis are integrated. This involves the extraction of spectral features from audio streams, followed by classification utilizing a support vector machine (SVM) model. The fusion of information derived from both visual and audio sources is utilized in the construction of a more comprehensive and refined traffic state map. This augmented map facilitates the effective management of emergency vehicle transit. In empirical evaluations, the proposed solution demonstrates its capability to mitigate challenges like visual clutter, occlusions, and variations in traffic density common issues encountered in traditional visual analysis methods. Notably, the proposed approach achieves an impressive accuracy rate of approximately 98.15% in the localization of emergency vehicles.

Multivariate Data Explanation by Jumping Emerging Patterns Visualization.

Multivariate or multidimensional visualization plays an essential role in exploratory data analysis by allowing users to derive insights and formulate hypotheses. Despite their popularity, it is usually users' responsibility to (visually) discover the data patterns, which can be cumbersome and time-consuming. Visual Analytics (VA) and machine learning techniques can be instrumental in mitigating this problem by automatically discovering and representing such patterns. One example is the integration of classification models with (visual) interpretability strategies, where models are used as surrogates for data patterns so that understanding a model enables understanding the phenomenon represented by the data. Although useful and inspiring, the few proposed solutions are based on visual representations of so-called black-box models, so the interpretation of the patterns captured by the models is not straightforward, requiring mechanisms to transform them into human-understandable pieces of information. This paper presents multiVariate dAta eXplanation (VAX), a new VA method to support identifying and visual interpreting patterns in multivariate datasets. Unlike the existing similar approaches, VAX uses the concept of Jumping Emerging Patterns, inherent interpretable logic statements representing class-variable relationships (patterns) derived from random Decision Trees. The potential of VAX is shown through use cases employing two real-world datasets covering different scenarios where intricate patterns are discovered and represented, something challenging to be done using usual exploratory approaches.

Visual Analysis Method for Traffic Trajectory with Dynamic Topic Movement Patterns Based on the Improved Markov Decision Process

The visual analysis of trajectory topics is helpful for mining potential trajectory patterns, but the traditional visual analysis method ignores the evolution of the temporal coherence of the topic. In this paper, a novel visual analysis method for dynamic topic analysis of traffic trajectory is proposed, which is used to explore and analyze the traffic trajectory topic and evolution. Firstly, the spatial information is integrated into trajectory words, calculating the dynamic trajectory topic model based on dynamic analysis modeling and, consequently, correlating the evolution of the trajectory topic between adjacent time slices. Secondly, in the trajectory topic, a representative trajectory sequence is generated to overcome the problem of the trajectory topic model not considering the word order, based on the improved Markov Decision Process. Subsequently, a set of meaningful visual codes is designed to analyze the trajectory topic and its evolution through the parallel window visual model from a spatial-temporal perspective. Finally, a case evaluation shows that the proposed method is effective in analyzing potential trajectory movement patterns.

Multimodal diagnosis model of Alzheimer’s disease based on improved Transformer

PurposeRecent technological advancements in data acquisition tools allowed neuroscientists to acquire different modality data to diagnosis Alzheimer’s disease (AD). However, how to fuse these enormous amount different modality data to improve recognizing rate and find significance brain regions is still challenging.MethodsThe algorithm used multimodal medical images [structural magnetic resonance imaging (sMRI) and positron emission tomography (PET)] as experimental data. Deep feature representations of sMRI and PET images are extracted by 3D convolution neural network (3DCNN). An improved Transformer is then used to progressively learn global correlation information among features. Finally, the information from different modalities is fused for identification. A model-based visualization method is used to explain the decisions of the model and identify brain regions related to AD.ResultsThe model attained a noteworthy classification accuracy of 98.1% for Alzheimer’s disease (AD) using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. Upon examining the visualization results, distinct brain regions associated with AD diagnosis were observed across different image modalities. Notably, the left parahippocampal region emerged consistently as a prominent and significant brain area.ConclusionsA large number of comparative experiments have been carried out for the model, and the experimental results verify the reliability of the model. In addition, the model adopts a visualization analysis method based on the characteristics of the model, which improves the interpretability of the model. Some disease-related brain regions were found in the visualization results, which provides reliable information for AD clinical research.

Effectiveness of a Communication Skills Training Protocol on Improving Marital Burnout

Objective: The aim of this research was to investigate marital burnout and to determine the effectiveness of a communication skills training protocol. Methods: The experimental method employed was a single-subject design with multiple baselines, involving couples seeking counseling at the Raz City Clinic in Shiraz and the Clinic of the General Directorate of Exceptional Education in Fars Province, during the autumn of 2021. Among these couples, three pairs exhibiting the highest levels of marital burnout were selected using a purposive sampling method. The therapeutic program of the communication skills training protocol was conducted by the researcher over 10 sessions, followed by a follow-up in two stages, one month apart. The participants responded to the Marital Burnout Scale at the baseline, post-treatment, and follow-up stages. Data were analyzed using visual analysis methods, the recovery percentage formula, and the reliable change index. Findings: Findings indicated that the communication skills training protocol enhanced marital quality and reduced marital burnout, with these effects being maintained during the follow-up phase. Conclusion: The treatment protocol proved to be effective, and couple therapists can utilize these communication skills in their work with couples to reduce marital burnout and enhance marital quality, particularly during the early years of marriage, where it can have a significant impact.

EvoVis: A Visual Analytics Method to Understand the Labeling Iterations in Data Programming.

Obtaining high-quality labeled training data poses a significant bottleneck in the domain of machine learning. Data programming has emerged as a new paradigm to address this issue by converting human knowledge into labeling functions(LFs) to quickly produce low-cost probabilistic labels. To ensure the quality of labeled data, data programmers commonly iterate LFs for many rounds until satisfactory performance is achieved. However, the challenge in understanding the labeling iterations stems from interpreting the intricate relationships between data programming elements, exacerbated by their many-to-many and directed characteristics, inconsistent formats, and the large scale of data typically involved in labeling tasks. These complexities may impede the evaluation of label quality, identification of areas for improvement, and the effective optimization of LFs for acquiring high-quality labeled data. In this paper, we introduce EvoVis, a visual analytics method for multi-class text labeling tasks. It seamlessly integrates relationship analysis and temporal overview to display contextual and historical information on a single screen, aiding in explaining the labeling iterations in data programming. We assessed its utility and effectiveness through case studies and user studies. The results indicate that EvoVis can effectively assist data programmers in understanding labeling iterations and improving the quality of labeled data, as evidenced by an increase of 0.16 in the average F1 score when compared to the default analysis tool.

THE SPECIES DIVERSITY AND LIFE CONDITIONS OF DENDROFLORA OF THE CLINICAL HOSPITAL OF UKRZALIZNYTSIA JSC, DNIPRO CITY

The role of the landscaping of hospital areas is considered. The purpose of the study is to identify the species composition and assess the life conditions of green spaces of the Clinical Hospital On Railway Transport of the “Healthcare Centre” Branch of Ukrzaliznytsia JSC inDniproCity. To achieve it, the methods of route and visual surveys, analysis and calculations are applied. The range of tree and shrub vegetation is examined and 44 species belonging to 35 genera and 18 families are found. The heterogeneity of dendroflora in terms of species diversity depending on the place of plant growth on the territory of the hospital is established. The rate of coniferous and broadleaved species in green spaces with the distribution of life forms is illustrated. The range of gymnosperms is assessed as insufficient. The most diverse in terms of taxonomic and the most numerous in terms of quantitative composition of the family are determined – Rosaceae and Cupressaceae, respectively. Trees and shrubs are singled out for their decorative properties. Attention is focused on the problem caused by uncontrolled self-reproduction of some tree species: Robinia pseudoacacia, Acer negundo, Acer ptatanoides, Morus alba, Fraxinus lanceolata, Ulmus pumila, Padus racemosa. It is found that the landscaping of the specified area is formed mainly by introduced species. The representatives of North America are dominant. The most common species are found to be Thuja occidentalis, Robinia pseudoacacia, Acer negundo. An analysis of the compatibility of trees and shrubs in relation to moisture is made: mesophytes are identified as the dominant group (40.5% of the total number of plants), xerophytes, xeromesophytes and mesoxerophytes cover 56.7%. The data of measurements of trunk diameters (in the range of 2 to 106 cm) and plant height with justification of its variation, depending on age, are published. The tallest individuals are found among the Populus nigra and P. Simonii species. The life conditions of green spaces are assessed based on the distribution by appropriate categories. The number of healthy specimens is recorded at the level of 63.7%. The rest (36.3%) are represented by the species of varying degrees of weakening and damage. Among them, the lion’s share of plants (23%) belongs to the first category. Recommendations for the optimization of the range of studied dendroflora are given and the perspective of further scientific research is outlined.

Computational fluid dynamics analysis for improving the uniformity of flow field in heat air drying kilns

Because the uniformity of the wind speed flow field in the hot air drying kiln directly affects the distribution of the temperature field in the kiln, to improve the uniformity of air distribution in the existing wood hot air drying kiln, CFD software was used to analyze the three-dimensional flow field of each improved scheme. A combination of visual comparison and analysis methods and data comparison and analysis methods be used, to serve as the basis for assessing the feasibility and rationality of the improvement schemes. Without adding baffle plates or altering the structure to incorporate right-angle surfaces, the kiln's structure was optimized by adjusting the position of the air outlet and installing airflow distribution chambers. This paper innovatively introduces the Theory of Inventive Problem Solving?TRIZ ?to analyze and solve the flow field optimization problem in the kiln. The difference between this method and the existing research methods is that the solution process is more logical and the solution to the fundamental problem is more accurate. The experimental results show that when the air supply speed of scheme S8 is 3m/s~7m/s, the flow field of kiln Zone C (material drying area) meets the ideal range of 1m/s~3m/s. In addition, under the same working condition (3m/s), the velocity differences of scheme S8 is closer to 0 than that of scheme S0, and the velocity unevenness coefficient is reduced by 18.44%.

IVESA - Visual Analysis of Time-Stamped Event Sequences.

Time-stamped event sequences (TSEQs) are time-oriented data without value information, shifting the focus of users to the exploration of temporal event occurrences. TSEQs exist in application domains, such as sleeping behavior, earthquake aftershocks, and stock market crashes. Domain experts face four challenges, for which they could use interactive and visual data analysis methods. First, TSEQs can be large with respect to both the number of sequences and events, often leading to millions of events. Second, domain experts need validated metrics and features to identify interesting patterns. Third, after identifying interesting patterns, domain experts contextualize the patterns to foster sensemaking. Finally, domain experts seek to reduce data complexity by data simplification and machine learning support. We present IVESA, a visual analytics approach for TSEQs. It supports the analysis of TSEQs at the granularities of sequences and events, supported with metrics and feature analysis tools. IVESA has multiple linked views that support overview, sort+filter, comparison, details-on-demand, and metadata relation-seeking tasks, as well as data simplification through feature analysis, interactive clustering, filtering, and motif detection and simplification. We evaluated IVESA with three case studies and a user study with six domain experts working with six different datasets and applications. Results demonstrate the usability and generalizability of IVESA across applications and cases that had up to 1,000,000 events.

Detection of malicious requests aimed at disrupting the availability of cyber-physical systems

The work is devoted to solving the problem of algorithmization of the security management processes of cyber-physical systems by detecting malicious requests aimed at disrupting the availability of management interfaces. Particular attention is paid to attacks aimed at denial of service of cyber-physical systems by sending HTTP-flood to web management interfaces. This paper proposes algorithmic provision for comprehensive adaptive analysis of incoming requests. The proposed algorithm for the detection of malicious requests analyses the activity of the investigated components of the cyber-physical system's web service at various network levels. The work applies a visual analysis and data processing method based on the representation as a single normalized set. The raw data of the analysed queries is grouped in a special way to detect a particular anomaly as a suspected threat. Examples of data changes and security responses are given. The experimental results confirm that the proposed algorithmic software achieves first- and second-order error reduction compared to the commonly used regression models in modern application-layer firewalls. The results obtained can be applied to the further development of the theory of information security, in particular the information security of cyber-physical systems and systems of processing of especially protected confidential information.

Green synthesis of metal nanoparticles and application of nanoadditives in diesel fuel: bibliometric analysis

The problem of obtaining metal nanoparticles considering environmental risks in connection with the use of synthesis products in mixtures of new types of diesel fuel has been studied. An analysis of existing research on these topics was carried out. Based on the data sets obtained using the Dimensions and OpenAlex services, the characteristics of the studies were determined. To analyze the problem of green synthesis of nanoparticles, a dataset of 6,062 journal articles published from 2001 to October 2023 was used. The use of nanoparticles as diesel fuel components was investigated using a dataset of 540 journal articles published from 2005 to October 2023. The leading countries and organizations by the number of publications and citations have been identified. Based on the analysis of coauthorship, the issue of international cooperation is considered. The most significant publications were determined by the number of citations. Using methods of visual analysis of data on research keywords, their trends and prospects were determined. In addition, an assessment was made of the development of the problem of using “green” methods for obtaining nanoparticles used as components of diesel fuel.

Learning Optimizer-Based Visual Analytics Method to Detect Targets in Autonomous Unmanned Aerial Vehicles

Visual analytics is vital for identifying targets by differentiating their structure and texture from unmanned aerial vehicles (UAVs). Object sensing disturbance and swift texture differentiation are tedious due to the UAV displacements. For improving the accuracy of target detection, this article introduces a learning optimizer-based visual analytics method. The proposed method assimilates deep learning and a gradient descent algorithm for feature differentiation and error minimization concurrently. The captured images are identified using multiple structural feature variations and are correlated with similar stored images. The features are extracted at different displacement and structural changing instances for leveraging accuracy. The learning process trains the similarity features during different differentiation factors. In the feature extraction, the minimum slope points are identified using a gradient descent algorithm by assigning random weights. As the differentiation increases using similar features, the minimum similarity value is detection. Postdetection, the weights are incremental and linear across different feature slopes. Therefore, the accuracy increases under varying displacement instances, preventing target misdetection. The gradient function is invariable between the minimum and maximum values for identifying high-precision features. This ensures optimal detection of different buildings and structures with high accuracy.

LEVA: Using Large Language Models to Enhance Visual Analytics.

Visual analytics supports data analysis tasks within complex domain problems. However, due to the richness of data types, visual designs, and interaction designs, users need to recall and process a significant amount of information when they visually analyze data. These challenges emphasize the need for more intelligent visual analytics methods. Large language models have demonstrated the ability to interpret various forms of textual data, offering the potential to facilitate intelligent support for visual analytics. We propose LEVA, a framework that uses large language models to enhance users' VA workflows at multiple stages: onboarding, exploration, and summarization. To support onboarding, we use large language models to interpret visualization designs and view relationships based on system specifications. For exploration, we use large language models to recommend insights based on the analysis of system status and data to facilitate mixed-initiative exploration. For summarization, we present a selective reporting strategy to retrace analysis history through a stream visualization and generate insight reports with the help of large language models. We demonstrate how LEVA can be integrated into existing visual analytics systems. Two usage scenarios and a user study suggest that LEVA effectively aids users in conducting visual analytics.

Team-Scouter: Simulative Visual Analytics of Soccer Player Scouting.

In soccer, player scouting aims to find players suitable for a team to increase the winning chance in future matches. To scout suitable players, coaches and analysts need to consider whether the players will perform well in a new team, which is hard to learn directly from their historical performances. Match simulation methods have been introduced to scout players by estimating their expected contributions to a new team. However, they usually focus on the simulation of match results and hardly support interactive analysis to navigate potential target players and compare them in fine-grained simulated behaviors. In this work, we propose a visual analytics method to assist soccer player scouting based on match simulation. We construct a two-level match simulation framework for estimating both match results and player behaviors when a player comes to a new team. Based on the framework, we develop a visual analytics system, Team-Scouter, to facilitate the simulative-based soccer player scouting process through player navigation, comparison, and investigation. With our system, coaches and analysts can find potential players suitable for the team and compare them on historical and expected performances. For an in-depth investigation of the players' expected performances, the system provides a visual comparison between the simulated behaviors of the player and the actual ones. The usefulness and effectiveness of the system are demonstrated by two case studies on a real-world dataset and an expert interview.

URBAN ACUPUNCTURE IN TAKSIM GEZI PARK: IMPROVING SOCIAL LIFE FOR COMMUNITIES

Urban acupuncture provides an adaptive framework for the design and regeneration of neglected public spaces (urban voids, lost places) to strengthen the social infrastructure of a city. Urban acupuncture believes urban design and regeneration should occur through a bottom-up, democratic, pluralistic, inclusive, innovative and interdisciplinary approach. Small-scale, low-cost, fast implementable, innovative and problem-solving idea projects are developed using this new planning approach to produce fast and punctual solutions to urgent needs against the slow traditional planning processes and create exceptional places by making small touches in the urban landscape. This study aims to improve the social life of communities in Taksim Gezi Park in Istanbul, Tu?rkiye, developed through an urban acupuncture idea project. This paper introduces urban acupuncture and the explanation of the study area. Data were collected by case study and observation methods and analysed using document analysis, visual material analysis and SWOT analysis methods. The paper introduces the urban acupuncture approach and then the information of the case study -Taksim Gezi Park. A SWOT analysis of Gezi Park based on fieldwork and observation is presented. From spatial analyses, the site-specific urban acupuncture idea projects were developed to improve Taksim Gezi Park to be more comfortable and beneficial for social communities, including plants and animals.