Points Of Interest Research Articles

MvStHgL: Multi-view Hypergraph Learning with Spatial-temporal Periodic Interests for Next POI Recommendation

Providing potential next point-of-interest (POI) suggestions for users has become a prominent task in location-based social networks, which receives more and more attention from the industry and academia, and it remains challenging due to highly dynamic and personalized interactions in user movements. Currently, state-of-the-art works develop various graph- and sequential-based learning methods to model user-POI interactions and transition regularities. However, there are still two significant shortcomings in these works: (1) Ignoring personalized spatial- and temporal-aspect interactive characteristics capable of exhibiting periodic interests of users; (2) Insufficiently leveraging the sequential patterns of interactions for beyond-pairwise high-order collaborative signals among users’ sequences. To jointly address these challenges, we propose a novel multi-view hypergraph learning with spatial-temporal periodic interests for next POI recommendation (MvStHgL). In the local view, we attempt to learn the POI representation of each interaction via jointing periodic characteristics of spatial and temporal aspects. In the global view, we design a hypergraph by regarding interactive sequences as hyperedges to capture high-order collaborative signals across users, for further POI representations. More specifically, the output of POI representations in the local view is used for the initialized embedding, and the aggregation and propagation in the hypergraph are performed by a novel Node-to-Hypergraph-to-Node scheme. Furthermore, the captured POI embeddings are applied to achieve sequential dependency modeling for next POI prediction. Extensive experiments on three real-world datasets demonstrate that our proposed model outperforms the state-of-the-art models.

Deducing neighborhoods of classes from a fitted model

AbstractIn this article, a new kind of interpretable machine learning method is presented, which can help to understand the partition of the feature space into predicted classes in a classification model using quantile shifts, and this way make the underlying statistical or machine learning model more trustworthy. Basically, real data points (or specific points of interest) are used and the changes of the prediction after slightly raising or decreasing specific features are observed. By comparing the predictions before and after the shifts, under certain conditions the observed changes in the predictions can be interpreted as neighborhoods of the classes with regard to the shifted features. Chord diagrams are used to visualize the observed changes. For illustration, this quantile shift method (QSM) is applied to an artificial example with medical labels and a real data example.

Campus Navigation using Augmented Reality and Virtual Reality

With the rapid advancement of Augmented Reality (AR) and Virtual Reality (VR) technologies, there has been a growing interest in leveraging these immersive technologies for enhancing campus navigation experiences. This paper presents a research study focused on designing and implementing a campus navigation system for the Maharashtra Institute of Technology World Peace University (MIT WPU) using AR/VR technologies. The proposed system aims to provide students, faculty, and visitors with an intuitive and immersive wayfinding experience, facilitating navigation across the expansive campus grounds. The core features of the proposed campus navigation system include real-time location tracking, interactive campus maps, personalized route planning, and integration with campus services and facilities information. The AR component overlays digital navigation cues and points of interest onto the user's physical surroundings, enhancing spatial awareness and providing contextual information. The VR component offers an immersive 3D visualization of the campus environment, allowing users to explore virtual replicas of buildings, landmarks, and amenities. The research methodology involves a multi-stage process, beginning with requirements gathering through stakeholder interviews and user surveys to understand the navigation challenges faced by campus users. Based on the gathered requirements, a prototype AR/VR campus navigation application is developed, leveraging state-of-the-art AR/VR development tools and technologies

Identifying building function using multisource data: A case study of China's three major urban agglomerations

As crucial geographical components shaping urban landscapes, defining the functions of buildings plays one of the pivotal roles in planning urban development and facilitating socio-economic human activities. Existing studies in large-scale mapping mainly ignore the environment around buildings, leading to insufficient meeting the demands of large-scale building-level mapping in certain disciplines. To abridge this gap, this article calculates the geometric characteristics of building footprint, the distances from buildings to adjacent objects, and the kernel densities of Point-of-Interest (POIs) to identify building main functions in three Chinese urban agglomerations (Beijing-Tianjin-Hebei Urban Agglomeration, Yangtze River Delta Urban Agglomeration, and Pearl River Delta Urban Agglomeration). Leveraging XGBoost, our models attained accuracies of 0.936, 0.934, and 0.940 in the three urban agglomerations, accompanied by kappa coefficients of 0.883, 0.868, and 0.891 respectively, suggesting their capability of identifying building functions in large-scale mapping. Furthermore, the conducted experiments on varying feature combinations and transferability highlight the significance of the built environment in enhancing classification accuracy. Additionally, the analysis of the classification results of buildings in the three urban agglomerations shows harmonized development strategies for Yangtze River Delta Urban Agglomeration and Pearl River Delta Urban Agglomeration, while cities within the Beijing-Tianjin-Hebei Urban Agglomeration do not show the same development strategies. All results demonstrate the feasibility of employing multi-source accessible data to efficiently categorize building functions over large areas in a cost-effective manner. This methodology can be replicated in China and even on a larger scale to offer insights into complex urban spaces and to help urban planners provide policy support such as old city renovations and new city planning.

An Android Smart City Traveller App (NaviCity)

Discover the cutting-edge and adaptable "Smart City Traveler App" that elevates the journey of individuals discovering unfamiliar cities. Through the utilization of state-of-the-art technologies, this mobile application expertly evaluates user preferences, time limitations, and curiosities, supplying individuals with custom travel plans and suggestions. With the ultimate purpose of enhancing sightseeing for both new and experienced travelers, the app commences with registration where users are prompted to input crucial information for tailoring their exploration. By leveraging the comprehensive location data collected through the Google Places API, the app guarantees a wealth of information on countless destinations for users to access. The Smart City Traveler App, constructed with a sleek combination of Flutter and Firebase, takes the hassle out of trip planning by aggregating top FourSquare rankings. With an intuitive interface, users can easily peruse and utilize this wealth of information to make informed choices. Not only does the app boast detailed maps pinpointing points of interest, but it also offers up various routes to maximize time efficiency. In the case of longer stays, the app even suggests exciting adventure activities or refreshing water parks, based on highly rated reviews. Incorporating Google Maps, the Smart City Traveler App puts the power in users' hands to confidently explore the city. Transforming city exploration into an unforgettable journey, this app is a must-have for adventurous travelers. Seamlessly blending cutting-edge technology with the thrill of discovery, the Smart City Traveler App is a testament to the endless possibilities of merging travel and technology. With personalized features and efficient optimization, it empowers users to fully indulge in the urban landscape, tailored to their own preferences and constraints.

Saliency-Guided Point Cloud Compression for 3D Live Reconstruction

3D modeling and reconstruction are critical to creating immersive XR experiences, providing realistic virtual environments, objects, and interactions that increase user engagement and enable new forms of content manipulation. Today, 3D data can be easily captured using off-the-shelf, specialized headsets; very often, these tools provide real-time, albeit low-resolution, integration of continuously captured depth maps. This approach is generally suitable for basic AR and MR applications, where users can easily direct their attention to points of interest and benefit from a fully user-centric perspective. However, it proves to be less effective in more complex scenarios such as multi-user telepresence or telerobotics, where real-time transmission of local surroundings to remote users is essential. Two primary questions emerge: (i) what strategies are available for achieving real-time 3D reconstruction in such systems? and (ii) how can the effectiveness of real-time 3D reconstruction methods be assessed? This paper explores various approaches to the challenge of live 3D reconstruction from typical point cloud data. It first introduces some common data flow patterns that characterize virtual reality applications and shows that achieving high-speed data transmission and efficient data compression is critical to maintaining visual continuity and ensuring a satisfactory user experience. The paper thus introduces the concept of saliency-driven compression/reconstruction and compares it with alternative state-of-the-art approaches.

Latent Representation Learning for Geospatial Entities

Representation learning has been instrumental in the success of machine learning, offering compact and performant data representations for diverse downstream tasks. In the spatial domain, it has been pivotal in extracting latent patterns from various data types, including points, polylines, polygons, and networked structures. However, existing approaches often fall short of explicitly capturing both semantic and spatial information, relying on proxies and synthetic features. This paper presents GeoNN, a novel graph neural network-based model designed to learn spatially-aware embeddings for geospatial entities. GeoNN leverages edge features generated from geodesic functions, dynamically selecting relevant features based on relative locations. It introduces both transductive (GeoNN-T) and inductive (GeoNN-I) models, ensuring effective encoding of geospatial features and scalability with entity changes. Extensive experiments demonstrate GeoNN’s effectiveness in location-sensitive superpixel-based graphs and real-world points of interest, outperforming baselines across various evaluation measures.

Measuring exposure and contribution of different types of activity travels to traffic congestion using GPS trajectory data

This study proposes a data-driven framework for understanding the space-time patterns of exposure and contribution of different activities to traffic congestion in urban road networks by using GPS trajectory and Point-of-Interest (POI) big datasets. Taking taxi trips related to traffic congestion in Wuhan, China as a case study, we first infer the types of individual activities from GPS trajectories and POIs and identify traffic congestion on each road. Then we develop two indicators to measure the congestion exposure of different activity types. Further, we reveal the space-time patterns of activity-related congestion through spatiotemporal analysis of the indicators of traffic congestion associated with different activities. The findings of this study shed light on how different types of activities contribute to the space-time heterogeneity of traffic congestion, and highlight the significance of considering the space-time patterns of congestion related with different activity types in urban transportation management.

New challenges for sustainable urban mobility

Micromobility provide intelligent, fast, and sustainable trips for tourist from accommodation facilities to attraction locations. However, some points of interest may be located away from the accommodation i.e. in a different city of a metropolitan area. In this case, cycling to the attractions may not be possible and multimodal bike and public transport trips can be considered for more sustainable tourism. Therefore, it is essential to identify how adequate a city transport infrastructures and services are for these types of trips. In this paper, we propose a cycling indicator for tourism accessibility with the aim of defining the level of multimodal bike-public transport friendliness through numerical values related to a specific zone in the main city of a metropolitan area. The indicator was applied to the metropolitan area of Bari (Italy) and results showed that the presence of bike lanes and bike-sharing plays an important role.

A graph-based approach for traffic prediction using similarity and causal relations between nodes

Accurate traffic prediction is crucial for the development of intelligent transportation systems (ITS) and various smart applications. To achieve this, effectively capturing the complex interplay between spatial and temporal dependencies across traffic nodes is essential. However, existing research often focuses solely on fixed spatial dependencies between neighboring nodes, neglecting the potential influence of distant nodes. This paper addresses these limitations by exploring two key types of relationships among traffic nodes. First, we examine indirect relationships based on similarity, where nodes exhibit similar traffic patterns irrespective of their physical proximity. Second, we investigate direct causal relationships, where traffic conditions at one node are directly influenced by other nodes. Based on these findings, we propose a novel approach named TPSC (Traffic Prediction using nodes Similarity and Causal relations). TPSC incorporates a classifier that utilizes the K-Prototype algorithm to group nodes based on their traffic similarity and nearby points of interest (POIs). Separate models are then trained for each cluster. Our model leverages a spatial module that employs graph convolutional network representation learning alongside transfer entropy to capture causal relationships and dynamic spatial dependencies. Additionally, a temporal module is introduced to capture periodic temporal dependencies across three components: recent patterns, daily patterns, and weekly patterns. Extensive experiments are conducted to evaluate proposed model on four large-scale traffic datasets for multiple traffic characteristics including speed, flow, and travel time. The experimental results demonstrate that TPSC surpasses the performance of the compared baselines, highlighting its superior predictive capabilities in traffic prediction.

Spot scanning imaging calibration method based on deviation model for wafer inspection

The spot scanning defect detection system has good detection performance for wafer defects. However, due to the existence of system assembly errors, the wafer defect inspection system could not scan following the planned trace, which resulted in distortion of the reconstructed image and had a greater impact on defect feature extraction and accurate defect location. The system deviation calibration and adjustment can further facilitate the accuracy of the system position and image reconstruction. To address this issue, the causes and shapes of imaging distortion were analyzed and simulated, and a simple and effective calibration method based on a scanning deviation model for the spot scanning defect detection system was proposed. First, a scanning deviation model was established following the characteristics of the spot scanning system, and different degrees of reconstructed image distortion were simulated and analyzed as the system deviation changes. Then, an 8-inch wafer with a grid was used as the calibrator and scanned using the developed spot scanning system. Serveral grid corners in the reconstructed image are selected and used as the calibration feature points. The constraint function is constructed using the equidistant features between adjacent grid points, and then the quasi-Newton method is used to optimize the parameters of the function to obtain the best deviation estimation of the spot scanning system. To prove the validity of the estimated deviation parameters, another patterned wafer was scanned and the distortion image was used for reconstruction. The experiment demonstrated that an accurate and distortion-free wafer image can be reconstructed using the calibrated deviation parameters, and the root mean square error of the measured distance between two points of interest was only 0.61 pixels, which proved the effectiveness of the proposed calibration method.

Characterizing urban GHG emissions based on land-use change—A case of Airport New City

Measuring greenhouse gas (GHG) emissions across different types of land is essential for urban planning during the transition to low-carbon cities. Urban land identification, based on remote sensing images and points of interest (POIs), has the potential to narrow down the statistical unit, helping achieve spatial visualization of GHG emissions below the city level and distinguish emission characteristics of different land-use types. Considering land type as a classification standard for GHG emissions, we constructed the modified STRIPAT-PLS model by identifying land-use types, reconstructing land-use GHG emissions (LUGEs) inventory, and extracting driving indexes, which calculated driving force and LUGEs that were missed in historical years. It deepened the depth and precision of GHG emissions research and provided a reference for LUGE reduction and land management below the city level where missing historical data on GHG inventories. The results showed that: 1) the modified STRIPAT-PLS model achieved a simulation accuracy of 94.9%, demonstrating the effectiveness of our research approach in depicting GHG emissions across different land-use types. 2) Land scale, socio-economic development, and industrial development were key factors that impacted agricultural land, residential land, and airport respectively. 3) Airport was the most significant carbon contributor, while industrial witnessed the highest growth in emission intensity; 4) the spatial analysis indicated decreasing differences in GHG emissions and an overall increase in emission intensity. In addition, the research proposed emission reduction priorities and strategies for each land type to promote low-carbon goals in airport cities.

Recency-based spatio-temporal similarity exploration for POI recommendation in location-based social networks

Point-of-interest (POI) recommendation is one of the primary tasks of location-based social networks (LBSNs). With user data in bulk, extracting useful information and addressing issues such as data sparsity and cold-start problems looming large in collaborative filtering become difficult. One of the plausible solutions is to incorporate contextual information into the recommendation process. In this article, we propose a Recency-based Spatio-Temporal Similarity Exploration (RSTSE) for POI recommendation that utilizes the recency-based trust estimation among the prospective neighbors of the target user. The trust level is categorized into two heads: direct trust, which can be extracted from the peer group information of the user, and indirect trust, which is measured based on venue popularity, temporal recency, radial proximity, and transitivity. The approach consists of two phases. In the incipient phase, POIs are extracted based on the preferences of potential neighbors, including the users who are recognized peers, the users with similar visiting histories in the spatial and temporal context, and the users with friend-of-friend relations. The telic phase involves Neural Collaborative Filtering (NCF) to capture the linear and non-linear user–POI interactions better. RSTSE has been evaluated on three real-world datasets, namely, Gowalla, Foursquare, and Weeplaces, and the results suggest efficacy over other state-of-the-art approaches.

Discrimination of ivory from extant and extinct elephant species using Raman spectroscopy: A potential non-destructive technique for combating illegal wildlife trade.

The use of elephant ivory as a commodity is a factor in declining elephant populations. Despite recent worldwide elephant ivory trade bans, mammoth ivory trade remains unregulated. This complicates law enforcement efforts, as distinguishing between ivory from extant and extinct species requires costly, destructive and time consuming methods. Elephant and mammoth ivory mainly consists of dentine, a mineralized connective tissue that contains an organic collagenous component and an inorganic component of calcium phosphate minerals, similar in structure to hydroxyapatite crystals. Raman spectroscopy is a non-invasive laser-based technique that has previously been used for the study of bone and mineral chemistry. Ivory and bone have similar biochemical properties, making Raman spectroscopy a promising method for species identification based on ivory. This study aimed to test the hypothesis that it is possible to identify differences in the chemistry of mammoth and elephant ivory using Raman spectroscopy. Mammoth and elephant tusks were obtained from the Natural History Museum in London, UK. Included in this study were eight samples of ivory from Mammuthus primigenius, two samples of carved ivory bangles from Africa (Loxodonta species), and one cross section of a tusk from Elephas maximus. The ivory was scanned using an inVia Raman micro spectrometer equipped with a x50 objective lens and a 785nm laser. Spectra were acquired using line maps and individual spectral points were acquired randomly or at points of interest on all samples. The data was then analysed using principal component analysis (PCA) with use of an in-house MATLAB script. Univariate analysis of peak intensity ratios of phosphate to amide I and III peaks, and carbonate to phosphate peaks showed statistical differences (p<0.0001) in the average peak intensity ratios between Mammuthus primigenius, Loxodonta spp. and Elephas maximus. Full width at half maximum hight (FWHM)analysis of the phosphate peak demonstrated higher crystal maturity of Mammuthus primigenius compared to living elephant species. The results of the study have established that spectra acquired by Raman spectroscopy can be separated into distinct classes through PCA. In conclusion, this study has shown that well-preserved mammoth and elephant ivory has the potential to be characterized using Raman spectroscopy, providing a promising method for species identification. The results of this study will be valuable in developing quick and non-destructive methods for the identification of ivory, which will have direct applications in archaeology and the regulation of international trade.

A Kinematic Synthesis Methodology for P-Drivable Spatial Single Degree-of-Freedom Mechanisms to Avoid Singularities

Abstract For a single degree-of-freedom spatial mechanism, a reference frame attached to any of its links produces a continuous motion of this frame. Given the progression of this frame from the start through the end of the mechanism’s motion, this paper seeks to identify specific points relative to this moving reference frame. The points of interest are those that can be coupled with a second point determined in the fixed frame to act as the end joint locations for a spherical–prismatic–spherical (SPS) driving chain. If the selection of the point pair is made such that the change in distance between them as the mechanism moves is strictly monotonic, then the SPS chain they define is potentially capable of driving the mechanism over the desired range of motion. This motion is referred to as locally P-drivable because a global solution is not ensured by the process proposed herein. This synthesis process can avoid singularities encountered by actuating the mechanism at one of its original joints. The proposed approach enables the dimensional synthesis of a single degree-of-freedom mechanism to focus on creating circuit-defect-free solutions without concern for potential singular positions. The actuating chain can then be determined as a separate step in the synthesis process. This paper also considers motions that are not P-drivable and the specialization to planar systems with the synthesis of a P-drivable revolute–prismatic–revolute (RPR) chain.

Strategy of Russian Military Cooperation with the Countries of the Arab Region

The article undertakes an analysis of the prospective domains of Russian military collaboration with Arab countries against the backdrop of geopolitical uncertainties. Despite maintaining its position as the second-largest arms exporter globally, Russia has experienced a proportional decline in its share of the global arms market. Nevertheless, a discernible uptick in the trade volume of conventional weaponry, coupled with cyclical enhancements in the tactical and technical attributes of military assets, reflects positive momentum in this sector. Amid prevailing external constraints and a noticeable scarcity of contemporary Russian weaponry in burgeoning segments of the arms market, Russia necessitates resolute and equitable measures to uphold and fortify collaborative ties with partner nations. Within this context, particular emphasis is placed on Russia's strategic military cooperation with Arab states, given the region's status as a focal point of geopolitical interests for major economies and a primary destination for arms imports.The article scrutinizes Russia's competitive standing in the arms and military equipment markets of the Arab world, acknowledging the region's significance as a pivotal importer of such goods. Employing a multifaceted evaluation encompassing absolute and relative statistical metrics and derived indices, the study assesses the region's role in global arms procurement. Moreover, it offers an appraisal of the existing state and potential avenues for enhancing the efficacy of the Russian-Arab military-technical collaboration framework. The discourse delves into the transformative prospects of this partnership within the framework of networkcentric warfare concepts. Furthermore, the article elucidates the principal risks inherent in military cooperation among nations at present, highlighting concerns regarding the possible manipulation of military-technical cooperation mechanisms to serve as leverage in furthering the geopolitical aspirations of specific stakeholders.In conclusion, the study posits forward-looking trajectories for bolstering bilateral relations, both sectorally and geographically. These include initiatives such as expanding licensing, leasing, offset transactions, and technology transfer initiatives; advocating for fifth-generation fighter aircraft and long-range radar detection and control systems, leveraging Industry 4.0 technologies; and prioritizing the training of foreign specialists.

Visualizing the Influence of New Public Transport Infrastructure on Travel Times

AbstractDue to the global climate change and increasing traffic volumes in cities, a shift from individual to public and multimodal transport is aspired. Travel time is one of the most important aspects for many people when choosing their mode of transportation. This leads to the requirement that changes in travel times have to be considered when planning new public transport infrastructure. This research paper presents and compares different techniques for visualizing the impact of new lines in existing public transport networks on travel times. The general approach of simulating timetable data and calculating intermodal travel times considering public transport and walking is being applied to two current infrastructure projects in the city of Bonn and the surrounding region. The created maps generally aim to visualize the spread in travel times between existing and extended transportation networks discretized by different spatial units such as rectangles or postal code areas. In comparison to other common methods which typically require two maps for two different scenarios (e.g. in case of isochrones), our approach gives the opportunity to combine all relevant information within one map. It is also shown how to apply bivariate choropleth maps for displaying travel times and how to visualize improvements in the accessibility of multiple target points of interest at once.

Combining travel behavior in metro passenger flow prediction: A smart explainable Stacking-Catboost algorithm

Accurately predicting short-term passenger flow is essential to optimize operation resources and improve transportation services in urban metro systems. However, it has become a challenging problem due to spatial-temporal demand fluctuations and heterogeneous passengers’ travel behavior, i.e., the interaction between the departure and arrival passengers. In this paper, we develop an explainable Stacking-Catboost model for passenger flow prediction combining the passenger’s return probability computation. The model explores several basic ensemble learning models and the best stacking strategy. To better characterize the macroscopic spatiotemporal travel patterns other than the micro individual travel behavior, several relevant variables such as train operation characteristics, the nearby bus stations, and points of interest are considered. Ablation studies are conducted to investigate the utility of each component of the proposed model. An explainable analysis is performed to interpret information from “black box” models and quantify the contribution of each feature. We present a real-world case study conducted in the Beijing metro, demonstrating that our proposed method achieves significant improvements over existing techniques in hourly prediction tasks. Specifically, our approach outperforms the CategoricalBoosting (CatBoost) by up to 11.11 % and the Random Forest (RF) by up to 12.90 %, showcasing the effectiveness of incorporating macroscopic spatiotemporal travel patterns and micro individual travel behavior to enhance short-term forecasting accuracy. The global interpretability of models reveals key factors that impact performance, such as returning passenger flow, historical travel demand, and timetable data. Our results offer valuable insights into short-term forecasting challenges and provide a framework for leveraging explainable artificial intelligence to improve transportation services in urban metro systems.

Exploring spatio-temporal impact of COVID-19 on citywide taxi demand: A case study of New York City.

Coronavirus disease 2019 (COVID-19) has brought dramatic changes in our daily life, especially in human mobility since 2020. As the major component of the integrated transport system in most cities, taxi trips represent a large portion of residents' urban mobility. Thus, quantifying the impacts of COVID-19 on city-wide taxi demand can help to better understand the reshaped travel patterns, optimize public-transport operational strategies, and gather emergency experience under the pressure of this pandemic. To achieve the objectives, the Geographically and Temporally Weighted Regression (GTWR) model is used to analyze the impact mechanism of COVID-19 on taxi demand in this study. City-wide taxi trip data from August 1st, 2020 to July 31st, 2021 in New York City was collected as model's dependent variables, and COVID-19 case rate, population density, road density, station density, points of interest (POI) were selected as the independent variables. By comparing GTWR model with traditional ordinary least square (OLS) model, temporally weighted regression model (TWR) and geographically weighted regression (GWR) model, a significantly better goodness of fit on spatial-temporal taxi data was observed for GTWR. Furthermore, temporal analysis, spatial analysis and the epidemic marginal effect were developed on the GTWR model results. The conclusions of this research are shown as follows: (1) The virus and health care become the major restraining and stimulative factors of taxi demand in post epidemic era. (2) The restraining level of COVID-19 on taxi demand is higher in cold weather. (3) The restraining level of COVID-19 on taxi demand is severely influenced by the curfew policy. (4) Although this virus decreases taxi demand in most of time and places, it can still increase taxi demand in some specific time and places. (5) Along with COVID-19, sports facilities and tourism become obstacles on increasing taxi demand in most of places and time in post epidemic era. The findings can provide useful insights for policymakers and stakeholders to improve the taxi operational efficiency during the remainder of the COVID-19 pandemic.

Point Protection with Transfluthrin against Musca domestica L. in a Semi-Field Enclosure.

House flies are notoriously difficult to control, owing to their tendency to live in close relationships with humans and their livestock, and their rapid development of resistance to chemical controls. With this in mind, we explored an alternative chemical control, a spatial repellent to deter Musca domestica L. from points we wanted to protect (i.e., a baited trap). Our results demonstrated that the synthetic spatial repellent, transfluthrin, is effective in preventing M. domestica adults from entering protected traps for both a susceptible strain (CAR21) and a field-acquired permethrin-resistant strain (WHF; 24 h LD50 resistance ratio of 150), comprising 22% and 28% of the total number of flies collected, respectively. These results are promising and demonstrate that transfluthrin can be an effective spatial repellent to protect points of interest where needed.