Points Of Interest Research Articles

Influencing Factors of Street Vitality in Historic Districts Based on Multisource Data: Evidence from China

Amid urban expansion, historic districts face challenges such as declining vitality and deteriorating spatial quality. Using the streets of Xi’an’s historical and cultural district as examples, this research utilizes multisource data, including points of interest (POIs), street view images, and Baidu heatmaps, alongside analytical techniques such as machine learning. This study explores the determinants of street vitality from the dual perspectives of its external manifestation and spatial carriers. A quantitative framework for measuring street vitality in historic districts is established, thoroughly examining the driving factors behind street vitality. Additionally, the relationship between built environment indicators and street vitality is elucidated through statistical analysis methods. The findings reveal significant, time-varying influences of these spatial carriers on human vitality, with distinct spatial distribution patterns of human activity across different times, and the significance of the influence of external representations of human vitality and various types of spatial carriers varies over time. Based on these insights, this paper proposes strategies for enhancing the vitality of historic streets, aiming to rejuvenate and sustain the diverse and dynamic energy of these districts. It provides a foundation for revitalizing the vigor of cultural heritage zones and offers strategies applicable to similar urban contexts.

A 10-m scale chemical industrial parks map along the Yangtze River in 2021 based on machine learning

Strengthening industrial pollution control in the Yangtze River is a fundamental national policy of China. There is a lack of detailed distribution of chemical industrial parks (CIPs). This Study utilized random forest (RF) and active learning to generate the distribution map of CIPs along the Yangtze River at 10-m resolution. Based on Sentinel-2 imagery, spectral and texture features are extracted. Combined with the Points of Interest (POI), a multidimensional feature space is constructed. By employing partitioned training, classification of CIPs map is achieved on Google Earth Engine (GEE). Technical validation along the entire Yangtze River demonstrates a model accuracy of 80%. Compared to traditional manual survey methods, this approach saves significant time and economic costs while also being timelier. As the first publicly available CIPs map within a 5-km range along the Yangtze River, this research will provide a scientific basis for the fine governance of chemical industries in the region. Additionally, it offers a model guide for the accurate identification of the chemical industry.

Spatial association between chronic respiratory disease mortality rates and industrial manufacturing enterprises: A case study of Chongqing, China

Industry has long been a driving force in the national economy of Chongqing, a traditional industrial hub in China. Current research provides limited understanding of the spatial associations between industrial manufacturing enterprises and CRD mortality in urban areas of Western China. This study employs location data of CRD death patients and Points of Interest (POI) representing industrial manufacturing enterprises from 2012 to 2020. It explores their spatial patterns and associations through spatial clustering identification, co-location, and association effects. Key spatial association characteristics identified include: (1) High-risk CRD mortality areas are predominantly located in Tongnan District and Kaizhou District in the western and northeastern parts of Chongqing, with an increase in CRD mortality rates correlated with age and a higher prevalence among females. (2) The optimal bandwidths for spatial association characteristics are 0.18 m and 0.475 m, respectively. (3) The construction and mining industries exert a more substantial impact on the mortality rates of younger CRD patients, with a notably stronger spatial impact on male patients compared to female patients. These insights are critical for guiding public health policy and spatial planning of industrial manufacturing enterprises in Chongqing.

Advancements in field development planning through mathematical analysis for reserves estimation, casing design, accidental events and carbon dioxide storage

The objective of this work is to introduce novel methodologies in specific parts of Field Development Plans (FDPs) by leveraging contemporary technological advancements, industry best practices, and accumulated knowledge. The proposed methodologies comprise volumetric analysis by conducting stochastic simulations within a Monte Carlo framework to evaluate the associated uncertainties and a piston-like CO2 storage scheme (based on the material balance approach) aiming to enhance natural gas production and at the same time store CO2, mitigating climate change. Optimization of drilling operations based on appropriate material selection from commercial casing tables and assignment of a failure probability of casing configuration are also included. Another proposed methodology involves spatial risk analysis simulating accidental events based on the points of interest (POIs) and the distance from the event. Finally, a stochastic analysis of project economics is conducted showing uncertainties of cashflows throughout the economic life of the project and assessing the outcomes using various probabilistic performance metrics. The integration of these methodologies offers a robust framework for enhancing the effectiveness of FDPs and their applications, aligning with the evolving demands and complexities of the oil and gas sector. Such methodologies, rooted in mathematical tools aiming to quantify uncertainty in various ways, can increase production, potentially reducing costs through carbon tax incentives and benefit the environment. The applicability of the proposed methodology is demonstrated at the Frigg field, a natural gas reservoir situated in the Norwegian sector of the North Sea. Given its historical development in the 1970s and 1980s, the Frigg field serves as a rich repository of data and information for comparative analysis and fine-tuning of the proposed methodologies. The present study bridges the gap between theoretical advancements and practical applications, promoting innovation and efficiency within the oil and gas field development processes.

Quantifying city freight mobility segregation associated with truck multi-tours behavior

Freight mobility segregation, a phenomenon like residential social segregation, is a crucial aspect of city landscapes that influences the livability and sustainability of cities. However, there is a deficiency in understanding the intrinsic complexity of freight mobility segregation, particularly regarding the micro-level truck behaviors. In this study, we develop a new approach to assess the degree of freight mobility segregation within cities by leveraging large-scale truck GPS data in Chinese cities. The analysis indicates the existence of freight mobility segregation in cities, where certain groups of trucks serve high-demand areas, while another group of trucks serves low-demand areas. The activity spaces of distinct truck groups are largely non-overlapping or segregated. To uncover the correlations between mobility segregation and truck operational patterns, we introduce two metrics to characterize truck multi-tours behavior, focusing on tour pattern predictability and activity explorability. By employing freight point-of-interest (POI) data, we further reveal the influence of local economic structures and industrial compositions on mobility segregation in cities. These findings enrich our understanding of the dynamics of city freight systems, offering implications for improving logistics efficiency and fostering sustainable city development.

Neighborhood Racial Composition and Unequal Exposure to Violent Crime in Everyday Contexts.

Exposure to violence is a critical aspect of contemporary racial inequality in the United States. While extensive research has examined variations in violent crime rates across neighborhoods, less attention has been given to understanding individuals' everyday exposure to violent crimes. This study investigates patterns of exposure to violent crimes among neighborhood residents using cell phone mobility data and violent crime reports from Chicago. The analysis reveals a positive association between the proportion of Black residents in a neighborhood and the level of exposure to violent crimes experienced by residents. Controlling for a neighborhood's level of residential disadvantage and other neighborhood characteristics did not substantially diminish the relationship between racial composition and exposure to violent crimes in everyday life. Even after controlling for violence within residents' neighborhoods, individuals residing in Black neighborhoods continue to experience significantly higher levels of violence in their day-to-day contexts compared to those living in White neighborhoods. This suggests that racial segregation in everyday exposures, rather than residential segregation, plays a central role in racial inequality in exposure to violence. Additionally, the analysis suggests that neighborhoods with more Hispanic and Asian residents are exposed to less and more violent crime, respectively, compared to neighborhoods with more White residents. However, this is only observed when not adjusting for the volume of visits points of interest receive; otherwise, the finding is reversed. This study offers valuable insights into potentially novel sources of racial disparities in exposure to violent crimes in everyday contexts, highlighting the need for further investigation.

KineWheel-DeepLabCut Automated Paw Annotation Using Alternating Stroboscopic UV and White Light Illumination.

Uncovering the relationships between neural circuits, behavior, and neural dysfunction may require rodent pose tracking. While open-source toolkits such as DeepLabCut have revolutionized markerless pose estimation using deep neural networks, the training process still requires human intervention for annotating key points of interest in video data. To further reduce human labor for neural network training, we developed a method that automatically generates annotated image datasets of rodent paw placement in a laboratory setting. It uses invisible but fluorescent markers that become temporarily visible under UV light. Through stroboscopic alternating illumination, adjacent video frames taken at 720 Hz are either UV or white light illuminated. After color filtering the UV-exposed video frames, the UV markings are identified and the paw locations are deterministically mapped. This paw information is then transferred to automatically annotate paw positions in the next white light-exposed frame that is later used for training the neural network. We demonstrate the effectiveness of our method using a KineWheel-DeepLabCut setup for the markerless tracking of the four paws of a harness-fixed mouse running on top of the transparent wheel with mirror. Our automated approach, made available open-source, achieves high-quality position annotations and significantly reduces the need for human involvement in the neural network training process, paving the way for more efficient and streamlined rodent pose tracking in neuroscience research.

Neighbourhood Environments and Lifestyle Behaviours in Adolescents With Type 1 Diabetes

ObjectivesEarly prevention strategies are needed to mitigate the high risk of cardiovascular disease in adolescents with type 1 diabetes (T1D). Residential neighbourhood features can promote healthy lifestyle behaviours and reduce cardiovascular risk, but less is known about their role in lifestyle behaviours in adolescents with T1D, and no studies used comparisons to healthy controls. MethodsWe examined associations between residential neighbourhood features and lifestyle behaviours in adolescents with T1D and healthy controls. Data were analyzed from the CARdiovascular Disease risk factors in pEdiatric type 1 diAbetes (CARDEA) study, a cross-sectional investigation of 100 adolescents with T1D (14 to 18 years) from a pediatric diabetes clinic in Montréal, Canada, and 97 healthy controls. Outcomes included physical activity and sedentary behaviour (accelerometry), screen time and sleep duration (questionnaires), and dietary habits (24-hour recalls). Cluster analysis of selected neighbourhood indicators computed for participants’ postal codes resulted in 2 neighbourhood types: central urban and peri-urban. Central urban neighbourhoods were characterized by very high population density, high active living index, numerous points of interest, higher social deprivation, higher residential mobility, and lower median household income compared with peri-urban neighbourhoods. Associations of neighbourhood type with lifestyle behaviours were estimated with multiple linear regressions and interactions by T1D status were tested. ResultsLiving in central urban neighbourhoods was associated with greater daily minutes of moderate-to-vigourous physical activity (beta = 8.61, 95% confidence interval 1.79 to 15.44) compared with living in peri-urban neighbourhoods. No associations were observed for other lifestyle behaviours, and no statistically significant interactions were found between neighbourhood type and T1D status. ConclusionFeatures that characterize central urban built environments appear to promote physical activity in adolescents, regardless of T1D status.

Parking Lot Traffic Prediction Based on Fusion of Multifaceted Spatio-Temporal Features.

With the rapid growth of population and vehicles, issues such as traffic congestion are becoming increasingly apparent. Parking guidance and information (PGI) systems are becoming more critical, with one of the most important tasks being the prediction of traffic flow in parking lots. Predicting parking traffic can effectively improve parking efficiency and alleviate traffic congestion, traffic accidents, and other problems. However, due to the complex characteristics of parking spatio-temporal data, high levels of noise, and the intricate influence of external factors, there are three challenges to predicting parking traffic in a city effectively: (1) how to better model the nonlinear, asymmetric, and complex spatial relationships among parking lots; (2) how to model the temporal autocorrelation of parking flow more accurately for each parking lot, whether periodic or aperiodic; and (3) how to model the correlation between external influences, such as holiday weekends, POIs (points of interest), and weather factors. In this context, this paper proposes a parking lot traffic prediction model based on the fusion of multifaceted spatio-temporal features (MFF-STGCN). The model consists of a feature embedding module, a spatio-temporal attention mechanism module, and a spatio-temporal convolution module. The feature embedding module embeds external features such as weekend holidays, geographic POIs, and weather features into the time series, the spatio-temporal attention mechanism module captures the dynamic spatio-temporal correlation of parking traffic, and the spatio-temporal convolution module captures the spatio-temporal features by using graph convolution and gated recursion units. Finally, the outputs of adjacent time series, daily series, and weekly series are weighted and fused to obtain the final prediction results, thus predicting the parking lot traffic flow more accurately and effectively. Results on real datasets demonstrate that the proposed model enhances prediction performance.

Privacy-preserving top-[formula omitted] spatio-temporal keyword preference query

With the rapid advancement of location-based services and mobile devices, spatial keyword query attracts increasing attention. In this paper, we focus on a new query type known as top-k spatio-temporal keyword preference query. This kind of query considers both the spatial object itself and other spatial objects in the neighborhood to return k spatial objects with the highest score. These k spatial objects satisfy spatial and temporal constraints, while their scores are determined by the keyword similarity of the neighboring spatial objects. We propose a scheme to enable privacy-preserving top-k spatio-temporal keyword preference queries. To effectively represent temporal information, we employ time vectors to denote time periods, allowing us to assess whether the data satisfies temporal constraints based on the inner product of time vectors. Furthermore, we adopt a two-step strategy to execute the query. The first step is to find all Points of Interest (POIs) that meet the spatial and temporal constraints. The second step is to calculate the score of each POI and return the top-k POIs with the highest score. To enhance query efficiency, we build a tree index structure that can achieve sub-linear query complexity. Additionally, we utilize EASPE algorithm to encrypt both the index and the query, ensuring privacy-preserving capabilities. Security analysis proves that our scheme satisfies CQA2-security. At the same time, experimental evaluation validates the query performance of our scheme.

Collaborative trajectory representation for enhanced next POI recommendation

Point-of-Interest (POI) recommendation stands as the cornerstone within a variety of location-based applications and services that intend to anticipate upcoming movements that users may be interested in. The current state-of-the-art methods have effectively explored spatio-temporal contextual features and users’ long-term and short-term preference patterns. Nevertheless, most existing work lacks the ability to effectively capture group movement patterns from trajectory collaboration. Additionally, they pay close attention to the accuracy of personalized recommendations, neglecting recommendation diversity, which refers to offering broader location options that extend beyond a user’s typical preferences, thereby avoiding excessive homogeneity or repetition. To address these gaps, this study proposes a Collaborative Trajectory Representation model (CTRNext), which enhances the diversity of recommendations while maintaining the precision of personalized preferences. To be specific, we first design two trajectory embedding layers to extract joint semantic interactions and the explicit spatiotemporal context-aware representation. Then, a trajectory semantic similarity calculation module that captures collaborative signals from potentially similar-minded users and eliminates barriers caused by trajectory length is proposed. Next, the implicit correlation and further updated representation between different check-in records are achieved through a multi-head self-attention aggregation module. Finally, we put forward a dual-driven user preference matching module to generate the preference-based next POI recommendation while enhancing diversity. Our approach demonstrates its remarkable recommendation accuracy through extensive experimentation on four real-world datasets, surpassing the performance of state-of-the-art methodologies.

Selective clustered tourist trip design problem with time windows under intuitionistic fuzzy score and exponential travel times

Creating travel plans is a difficult task for tourists. Hence, this is due to planning trips for a selection of Points of Interest (POIs) that consider several constraints and preferences to identify POIs and optimize the attractiveness of routes in general. The literature addresses the problem as the Tourist Trip Design Problem (TTDP). This paper considers a particular variant of TTDP, where POIs are grouped into clusters corresponding to various destinations. Additionally, the visit to each POI must occur within a specific time interval. Minimum and maximum restrictions exist on how many POIs from each cluster may be visited on the same itinerary. Moreover, we proposed the Tourist Trip Design Problem with Clustered POIs under intuitionistic fuzzy scores and exponential travel times in uncertainty and randomness. This problem is finding a feasible route that maximizes the total score collected. The route must begin and end at a specified initial location, and its duration is limited to a specific maximum value. A mathematical model is proposed for this problem. Test problems are generated and solved by the proposed model. The solution to the test problems is obtained by using different parameters. The results show that the proposed methodology is an approach that allows tourists to get other travel plans and see how well these travel plans meet their demands and preferences.

Perioperative chemotherapy and immunotherapy to optimize cystectomy outcomes in the curative intent of non-metastatic muscle-invasive bladder cancer: A systematic review and meta-analysis

IntroductionPerioperative systemic treatments play a crucial role in the comprehensive management of muscle-invasive bladder cancer. While neoadjuvant platinum-based chemotherapy has a proven efficacy, administering adjuvant chemotherapy can be challenging in patients with multiple comorbidities. Although immunotherapy has shown success in metastatic settings, its effectiveness in both neoadjuvant and adjuvant settings remains under investigation. This study aims to conduct a systematic literature review and meta-analysis to assess the impact of chemotherapy and/or immunotherapy in neoadjuvant and/or adjuvant settings. MethodsA systematic review and meta-analysis were conducted by consulting the PubMed, Scopus, and ClinicalTrial.gov databases for the period from 1994 to 2023. The analysis utilized Forest Plots for key points of interest: overall survival/pathologic response for neoadjuvant chemotherapy (NAC) and overall survival/disease-free survival for adjuvant chemotherapy (AC), employing random or fixed models. ResultsThe systematic review included 26 articles, and 14 were incorporated into the meta-analysis. For NAC, five studies assessed overall survival, yielding an overall hazard ratio (HR) of 0.84 [0.75–0.94]; P=0.002. Pathologic response under NAC was evaluated in five studies, resulting in an overall odds ratio (OR) of 0.3 [0.2–0.4]; P<0.001 compared to cystectomy and 0.86 [0.65–1.13]; P=0.28 for MVAC vs. GC. Regarding AC, six studies investigated overall survival, revealing an overall HR of 0.93 [0.77–1.12]; P=0.46. Disease-free survival under AC was examined in seven studies, with an overall OR of 0.58 [0.44–0.78]; P<0.001. Meta-analysis was not conducted for immunotherapy due to limited phase II studies in the neoadjuvant setting and only two available studies in the adjuvant setting. ConclusionThis study reaffirms the efficacy of platinum-based chemotherapy in neoadjuvant and adjuvant scenarios, enhancing overall survival in muscle-invasive bladder cancer patients. Immunotherapy exhibits promising outcomes in tumor downstaging in the neoadjuvant setting and in disease-free survival, in the adjuvant setting.

Stochastic Computation of Barycentric Coordinates

This paper presents a practical and general approach for computing barycentric coordinates through stochastic sampling. Our key insight is a reformulation of the kernel integral defining barycentric coordinates into a weighted least-squares minimization that enables Monte Carlo integration without sacrificing linear precision. Our method can thus compute barycentric coordinates directly at the points of interest, both inside and outside the cage, using just proximity queries to the cage such as closest points and ray intersections. As a result, we can evaluate barycentric coordinates for a large variety of cage representations (from quadrangulated surface meshes to parametric curves) seamlessly, bypassing any volumetric discretization or custom solves. To address the archetypal noise induced by sample-based estimates, we also introduce a denoising scheme tailored to barycentric coordinates. We demonstrate the efficiency and flexibility of our formulation by implementing a stochastic generation of harmonic coordinates, mean-value coordinates, and positive mean-value coordinates.

Are heterogeinity and conflicting preferences no longer a problem? Personality-based dynamic clustering for group recommender systems

The complexity associated with groups of tourists led to the emergence of Group Recommender Systems (GRS) for tourism. But if generating recommendations for small groups is a complex task, to provide them to large and occasional groups is even more. This complexity is especially due to the group’s heterogeinity, conflicting preferences, the information overload found on the internet and the tourists’ different ways of coping with the information, hindering the recommendation process from the users’ profile construction to the final recommendation of a list of points of interest to visit. In this work, we show how we tackled the identified issues in a GRS prototype, Grouplanner, including the cold-start problem, by predicting the tourists’ preferences based only on their personality and dividing the main group into subgroups of similar personality; by using a Multi-Agent Microservice; a novel dynamic clustering algorithm, d-means, adapted from the k-means algorithm, that does not need to know the number of clusters a priori; and association rules. Using a personality dataset of n=100k users, the proposed d-means algorithm was tested against two baselines (k-means and k-means++), showing better results in the clustering quality and scalability. We were also able to determine a large set of association rules to refine the recommendations, although further improvements are needed. To test the Grouplanner prototype, a simulation with real users (n=35) was conducted. The results showed the subgroups formed were very compact, revealing a very good clustering quality, with an average silhouette of s = 0.91. 11 of the 15 proposed tourist preferences were successfully predicted and used for the preliminary recommendation lists, being 92 % of the participants satisfied with the individual recommendations and 96 % with the group recommendations.

Landscape of scientific research on the problems of decarbonization and achieving carbon neutrality of national economies

Purpose: is to form a comprehensive understanding of the patterns of publication activity in the field of decarbonization that have developed in the global and Russian scientific space, in order to identify promising scientific areas on this basis that can define new research horizons in achieving carbon neutrality.Methods: the research methods involved bibliographic and bibliometric analyses of scholarly publications, utilizing data from the Scopus and eLibrary databases. The study encompasses the timeframe from 2012 to 2022. The research methodology is structured to address three key objectives. Firstly, it aims to identify the quantitative dynamics of global publication activity in the realm of decarbonization. Secondly, the investigation seeks to analyze publication patterns at the levels of research organizations, countries, keywords, related topics, scientific publications, subject areas, and thematic groups, including clusters of thematic groups. Thirdly, the research involves semantic analysis of publications and the identification of points of research interest within the contexts of both global and Russian scientific landscapes.Results: it has been established that bibliographic clustering of studies on decarbonization problems occurs not so much around technical and technological solutions, but in conceptually broader coordinates. It has been identified that the aspiration for carbon neutrality is predominantly observed in economies characterized by a high standard of living, where citizens exhibit a heightened sense of social responsibility and a greater commitment to enhancing the quality of life for future generations. It was revealed that if the global problems of decarbonization are the center of gravity of the most powerful researchers and scientific teams, but in Russia this topic is not yet characterized by a comparable positioning.Conclusions and Relevance: systematization of decarbonization research, which is rapidly developing both in terms of volume indicators and in terms of increasing its interdisciplinarity and social relevance, is the most important condition for effective scientific orientation and research routing in a highly fragmented and contradictory development space on the problems of the global energy transition.

Geospatial indexing for sea–land navigation based on machine learning

The exponential growth of geospatial data has created a need for efficient data processing and indexing, especially for acquiring the required data around while traveling on land and sea. Machine learning based learned index has brought a new phase in the field of spatial data indexing on land and sea for fast and accurate acquisition of points of interest. Recently proposed spatial learned indexes and traditional spatial indexes are difficult to maintain their performance while traveling on land and sea due to high memory cost and time consumption. Considering the above issue, we propose SLIP, a novel in-memory spatial learned index. SLIP uses an optimized mapping function and a piecewise linear model to handle data updates (e.g., insertion and deletion) efficiently. Based on SLIP, we design a range query algorithm and a faster KNN(K-NearestNeighbor) query algorithm using the optimized radius expansion model to get the results more quickly. Experiments on real geospatial datasets and synthetic datasets used the electronic navigation chart software show that SLIP achieved an average improvement of 10x in query performance over traditional spatial indexes and state-of-the-art learned indexes. Therefore, SLIP provides an innovative idea for high-efficiency geospatial data processing and indexing, and offers an efficient method for acquiring the surrounding geographic data for future navigation on land and sea.

Predicting Crime Hotspots using Machine Learning Techniques

This research delves into the application of machine learning algorithms for forecasting crime hotspots by leveraging historical data of public property crime in a major coastal city in southeast China. The study conducts a comparative analysis, emphasizing the predictive efficacy of various machine learning models. Results indicate that the LSTM model surpasses other methods including KNN, random forest, support vector machine, naive Bayes, and convolutional neural networks when utilizing solely historical crime data. Moreover, integrating built environment data such as points of interest (POIs) and urban road network density as covariates into the LSTM model enhances predictive accuracy. These findings bear significance for shaping policing strategies and implementing measures for crime prevention and control.

Emerging Evils in Post-Colonies

The colonial and postcolonial are two different ideological eras in the colonized peoples’ history, which are taken up by post-colonial literature. Without rehashing the ex-colonized beings’ painful past from a rebellious and revanchist perspective, postcolonial writers’ literary projects aimed at revitalizing the ex-colonized beings’ experience in the form of collective memory. In such a creative art, the narrative devices in force decry retrograde and dehumanizing practices. In terms of vision, the call for improving the relationship between ex-colonizers and ex-colonized subjects is highly prescribed. All neocolonial policies and related influences are systematically proscribed, thus favouring the creation of a global village free from inequalities, exclusions, and other injustices. In essence, the poetization of “Bournehills” in The Chosen Place, The Timeless People and “Kosawa” in How Beautiful We Were is part of those narrative techniques. Today, in a disguised form, Westerners, with their seducing offers or projects, corrupt tiny groups of undeveloped people won over to their cause and keep the masses in misery. In this context of political paradigm shift and sociocultural mutation, the study of the forms of life in the post-colonies remains a challenge. This helps to disclose on the one hand how the former colonial maintains their ex-colonies in perpetual dependence and on the other hand, highlight how the ex-colonized beings or heirs react and overcome neo-colonial policies. To account for the features of neo-colonialism, the use of Perussetian semiotic approach will be helpful. This will contribute to looking into the prevailing forms of life in both fictional imaginaries. Two points of interest will be scrutinized: “post-colonial order features” and “ex-colonized beings’ resilience”

Recognizing the rights of children with disabilities in Malawi

Little is known about the realization of the rights of children with disabilities in Malawi, including which rights are being met or violated, where protection of rights must be reinforced, and ways that society can support the rights. This study explored the realization of the rights of children with disabilities in Malawi. Data generation included interviews with 30 children with disabilities (15 boys, 15 girls). Data were analyzed using content analysis and Taguette software, with codes generated relating to the UN CRPD articles. Findings indicate that children expressed discrimination and inequities toward the realization of multiple rights. In particular, there remain significant gaps in policy and programs addressing freedom from violence and abuse in community and school settings. The findings of this study will enable duty bearers, decision-makers, and service providers to prioritize initiatives for children with disabilities in Malawi to ensure their comprehensive breadth of rights is met. Points of interest This study examined how the rights of children with disabilities in Malawi are being met. The United Nations Convention on the Rights of Persons with Disabilities (UN CRPD) provides a framework for developing policy, designing services, and implementing programs for children with disabilities based on their rights. Children with disabilities said they get support, feel respected by family, and don’t feel neglected or abandoned by their parents. The government, community, teachers, NGOs, and families are working together to support the rights of children with disabilities. Significant issues exist in providing inclusive education and preventing violence for children with disabilities, both in terms of services and policies. This study’s results will help those responsible for actions, decision-makers, and service providers in Malawi prioritize policies and programs for children with disabilities, ensuring that their full range of rights is addressed.