Number Of Points Of Interest Research Articles

What is the “best” way to measure the relative location variables in the market value assessment? An econometric method applied to an Italian case study

The present research is dedicated to investigating the explanatory power of relative location variables in assessing and forecasting market values. Here, relative location refers to the spatial position (geographical context) of a building or property in relation to a given Point Of Interest (POI). Specifically, a methodological approach is proposed for identifying the most suitable quantification modality based on statistical performance and consistency with the market mechanisms of the specific reference context. For a case study in Northern Italy, we collected data on 615 residential properties and 2673 POIs, including cultural facilities, school and education institutions, commercial services, sports, entertainment, and leisure facilities, health and care services, public transport systems, urban parks, and green areas. The relative location between the collected properties and the POIs is assessed using an automated calculation procedure developed in the Python programming language, in conjunction with Geographic Information Software (GIS). This automatism allows the assessment of relative location in terms of different Units Of Measure (UOM), such as straight-line distance, travel time by car, travel time on foot, travel time by public transport, and the number of POIs in a 400 m/1 km ring buffer. Since 615 residential buildings and 2673 POIs were analysed, with their relative locations measured using six different UOMs, a database of 9'865'215 data was produced. Furthermore, for each category of POI, a feature importance analysis guides the selection of the best UOM, i.e., the most statistically significant one. Considering the chosen UOM, an optimised econometric technique is finally implemented to analyse the functional relationships between the market values of residential properties and the set of identified relative location variables.

An Expectation-Maximization framework for Personalized Itinerary Recommendation with POI Categories and Must-see POIs

In this paper, we introduce a novel deterministic method based on Expectation Maximization (EM) to solve the rather complex problem of designing a tourist trip or Personalized Itinerary Recommendation (PIR). PIR objective is to recommend a personalized tour consisting of successive Points of Interest (POIs), which maximizes user satisfaction and respects user time-frame constraints. On top of that, the POIs are divided into categories, in order for travelers to be able to set limits on the maximum (and minimum) number of POIs that belong to one category and are included in the itinerary. In the proposed framework, emphasis is given on the POIs sequence selection, which exploits the customized POI recommendations offered by a recommender system. Additionally, the proposed methodology with POIs categories is able to solve the TourMustSee problem, so that the tour includes a set of POIs that must be visited. The proposed system has been successfully incorporated into a mobile app, offering a complete tourist trip design. The high performance, resilience, and computational efficiency of the proposed framework are demonstrated by experimental findings and comparisons to existing approaches on numerous synthetic and real datasets.

The distribution of shared parking use in time and space: A case study in Guangzhou, China

Shared parking enables private parking owners to share their parking spaces during idle time. In recent years, increasingly more research investigated people’s intentions to participate in shared parking schemes as well as optimization algorithms to match shared parking supplies and demands. However, little research has investigated the distribution of shared parking use in time and space in implementation. To fill this gap, this study uses the transaction records of 121 shared parking lots in Guangzhou, China, and applies a quasi-Poisson regression model to analyze the influence of a set of explanatory factors on the total number of transactions. The results show that the number of parking transactions is significantly influenced by implemented duration, parking lot capacity, land use of shared facility, number of POIs (point of interest), and transit stations within a range of 750 meters from the shared parking lot. This study also applies a linear regression model to analyze the effect of a set of explanatory variables on the average parking duration at shared parking lots. The results show that the average parking duration is significantly influenced by the land use of a shared facility, number of office buildings within 750 meters from the shared parking lot, and peak time of the shared parking lots.

Visit planner: A personalized mobile trip design application based on a hybrid recommendation model

The paper presents Visit Planner (ViP), a mobile application prototype that provides a solution to the challenging tourist trip design problem. ViP follows a holistic approach offering personalized recommendations for Points of Interest (POIs) based on preferences either explicitly collected by the application, or inferred by the users? ongoing interaction with the system. ViP proposes to the final user, a trajectory of POIs calculated using an Expectation Maximization method that maximizes user satisfaction taking into consideration a variety of time and spatial constraints for both users and POIs. Additionally, POIs are divided into categories, so that a certain number of POIs from each category to be included in the final itinerary. The application is implemented as a user-interactive system that allows the flexibility for easy content adaptation and facilitates management of content and services by the user. The prototype has been implemented for Android-based smartphones, on an open application environment, using standard communication protocols and open database technology. Currently, it is applied to the city of Agios Nikolaos in Crete, and is available for download from Google play.

Spatial model for predictive recovery monitoring based on hazard, built environment, and population features and their spillover effects

The ability to proactively monitor the trajectory of post-disaster recovery is valuable for resource allocation prioritization. Existing knowledge, however, lacks models and insights for quantifying and proactively monitoring post-disaster community recovery. This study examines models that could predict population activity recovery at the scale of the census block group (CBG). Population activity recovery is measured by using location-based human mobility visitation patterns to essential points-of-interest (POIs) in the context of the 2017 Hurricane Harvey in Harris County, Texas. The study examined the association between the population activity recovery duration and 32 features split into four categories: (1) physical vulnerability and access, (2) hazard exposure and impact, (3) proactive actions and (4) population features. Several types of spatial regression models were evaluated to determine their ability to capture this relationship. The Spatial Durbin Model was identified as the best fit for assessing direct, spillover, and total effects of features on population activity recovery at the CBG level. The results show the extent of physical vulnerability, measured by road network density, prolongs the duration of population activity recovery by a combination of direct and spillover effects. Also, the extent of access to essential facilities, measured based on the number of POIs, shortens the duration of population activity recovery. Correspondingly, the extent of flooding is not a significant feature in explaining the population recovery duration in CBGs. The results show that better preparedness, measured by extent of POIs visitations prior to hurricane landing, is associated with faster population activity recovery. In terms of population attributes, the total number of people, the percentage of minorities, and the percentage of Black and Asian subpopulations are significant features in the model for predicting the duration of population activity recovery. The study outcome offers data-driven insights for understanding the determinants of population activity recovery and provides a new model tool for predictive recovery monitoring based on evaluating the direct, spillover, and total effects of features. These findings can identify areas with slower or more rapid recovery to inform emergency managers and public officials in ensuring equitable resource allocation prioritization.

A traffic big data analysis on relationships between urban planning and traffic noise level--taking Dongguan Demonstration Area, China as an example

With the promotion of smart city research, traffic big data has become a new way to study urban traffic noise. Taking Dongguan Demonstration Area, China as an example, this research discussed the relationships between traffic noise levels and urban plannings using geographic information scienceGIS, global positioning system (GPS) techniques and OpenITS Organization OpenData. The results showed that, for the whole area, some planning factors, say global integration, local integration (R=500m), global betweenness, local betweenness (R=500m) and number of points of interest (POIs) had significant positive correlations with the daytime traffic noise levels. Among them, the number of POIs had the strongest correlation with the traffic noise levels (r=0.560 p<0.01). However, the degree of influence of each variable on traffic noise levels can be changed with geographical locations. This research also identified specific areas where traffic noise levels were negatively correlated with local integration and local betweenness, which had great potential to provide a recreational and peaceful place for people to walk. Therefore, the urban areas' centers and fringes can be studied separately to effectively control traffic noise by changing the urban plannings.

Continuous Road Network-Based Skyline Query for Moving Objects

With the development of location-based services and smart terminals, skyline query technique has been used widely in intelligent transportation systems. In skyline queries, the areas and keywords queried by users have a great impact on the quality of the query results and users may only be interested in the closer results of the skyline query. However, current approaches for the continuous skyline query limit the area of the skyline query to a specific area in the road network, which leads to that many useful query results cannot be retrieved. To this end, an innovative continuous skyline query approach in city range is proposed in this paper, where a multi-scale area divisions of the urban road network are provided to find the optimize query scale and area. In our approach, first, the dominant area of each intersection node in the road network is established based on the Voronoi. Then, all Points of Interest (POIs) are divided into the dominant area of each intersection node. After that, the intersection node aggregation algorithm (INAA), link remolding algorithm (LMA) and link fitting algorithm (LFA) are proposed to reduce the number of intersection nodes in the road network, so as to increase the dominant area of the remaining intersection nodes and the number of POIs in these nodes. Finally, a better query scale by considering the efficiency and quality of the query is given through the studies.

Exploring five indicators for the quality of OpenStreetMap road networks: a case study of Québec, Canada

OpenStreetMap (OSM) is one of the most well-known volunteered geographic information (VGI) projects that aims to produce a free-world map. However, there are serious concerns about its quality. Numerous studies have assessed the quality of OSM by comparing the OSM database with a reference database. Several researchers have proposed the use of quality indicators as variables that can describe OSM quality in regions where no reference data are available. A quality indicator is a variable that has a significant monotonic relationship with quality measures. In this study, a literature review was conducted to identify and define the main quality measures proposed for assessing the quality of linear features. Owing to limited access to current data, only three quality elements—completeness, positional accuracy, and attribute accuracy—were evaluated in this study. These quality measures were then used to assess the quality of the OSM roads in the province of Quebec. Finally, Spearman’s rank correlation coefficient test was applied to determine whether there was a significant correlation between the quality measures related to the three quality elements and the five potential quality indicators: population, average income, density of OSM roads, density of OSM buildings, and number of points of interest (POI). The main contribution of this study is testing the following hypothesis: “There is a significant correlation between the five mentioned variables and the measures related to the three quality elements”. Statistical analysis showed that in terms of completeness, the density of OSM roads and population were the best indicators; in terms of positional accuracy, population and income were the best indicators; and in terms of attribute accuracy, completeness was the best indicator. All five variables have significant correlations with the measures of the three elements of quality, except for the following two pairs (attribute accuracy, density of OSM roads) and (attribute accuracy, density of OSM buildings). This study proposes the density of OSM roads and number of POI as two new quality indicators that have not been found in the literature review.

The Effect of High-Density Built Environments on Elderly Individuals' Physical Health: A Cross-Sectional Study in Guangzhou, China.

The built environment refers to the objective material environment built by humans in cities for living and production activities. Existing studies have proven that the built environment plays a significant role in human health, but little attention is paid to the elderly in this regard. At the same time, existing studies are mainly concentrated in Western developed countries, and there are few empirical studies in developing countries such as China. Based on POI (point of interest) data and 882 questionnaires collected from 20 neighborhoods in Guangzhou, we employ multilevel linear regression modeling, mediating effect modeling, to explore the path and mechanism of the impact of the built environment on elderly individuals’ physical health, especially the mediating effects of physical and social interaction activity. The results show that the number of POIs, the distance to the nearest park and square, and the number of parks and squares are significantly positively correlated with the physical health of the elderly, while the number of bus and subway stations and the distance to the nearest station are significantly negatively correlated. Secondly, physical activity and social networks play a separate role in mediating the effect of the built environment on elderly individuals’ physical health. The results enrich the research on the built environment and elderly individuals’ health in the context of high-density cities in China and provide some reference basis for actively promoting spatial intervention and cultivating a healthy aging society.

Querying Optimal Routes for Group Meetup

Motivated by location-based social networks which allow people to access location-based services as a group, we study a novel variant of optimal sequenced route (OSR) queries, optimal sequenced route for group meetup (OSR-G) queries. OSR-G query aims to find the optimal meeting POI (point of interest) such that the maximum users’ route distance to the meeting POI is minimized after each user visits a number of POIs of specific categories (e.g., gas stations, restaurants, and shopping malls) in a particular order. To process OSR-G queries, we first propose an OSR-Based (OSRB) algorithm as our baseline, which examines every POI in the meeting category and utilizes existing OSR (called E-OSR) algorithm to compute the optimal route for each user to the meeting POI. To address the shortcomings (i.e., requiring to examine every POI in the meeting category) of OSRB, we propose an upper bound based filtering algorithm, called circle filtering (CF) algorithm, which exploits the circle property to filter the unpromising meeting POIs. In addition, we propose a lower bound based pruning (LBP) algorithm, namely LBP-SP which exploits a shortest path lower bound to prune the unqualified meeting POIs to reduce the search space. Furthermore, we develop an approximate algorithm, namely APS, to accelerate OSR-G queries with a good approximation ratio. Finally the experimental results show that both CF and LBP-SP outperform the OSRB algorithm and have high pruning rates. Moreover, the proposed approximate algorithm runs faster than the exact OSR-G algorithms and has a good approximation ratio.

Transit-based accessibility and urban development: An exploratory study of Shenzhen based on big and/or open data

Transit-Oriented Development (TOD) degree, the correlation between transit-based accessibility and urban development, reflects to what extent TOD shapes urban form. This paper empirically examines the shifting patterns of TOD degree and its correlates by using publicly available big and/or open data (BOD) such as point of interest (POI) and OpenStreetMap data for Shenzhen, China, a city experiencing rapid growth/changes in recent decades. The BOD used contain up-to-date information of human activities in finer spatiotemporal resolutions than traditional data. The paper illustrates how BOD can be utilized to quantify TOD degree and its correlates in 2014 and 2017. The main findings are twofold. First, bus stops, a proxy for bus-based accessibility, were found to markedly influence urban development (measued by the number of POIs with social media check-ins) around a (planned) metro station in 2014, when the local metro system was still in devleopment. Second, the impacts of the metro-based accessibility (measured by the average time to the two central business districts) on urban development became larger than those of the accessibility to bus stops as the local metro system matured. It is recommended that decision-makers should systematically consider and harness those effects over time to better promote TOD degree.

Discovering Station Patterns of Urban Transit Network with Multisource Data: Empirical Evidence in Jinan, China

The various performances of buses at stations bring lots of difficulties for operators to manage them to improve the service quality. This paper proposes a data-driven framework to analyze the patterns of stations with network structure data, points of interest (POI) data and vehicle global positioning system (GPS) trajectory data. First, we build six indicators based on these data to measure the performance from station perspective. The results show that the number of POI around stations within 1 kilometer follows an exponential distribution. Moreover, the average headway and headway deviation of stations follow lognormal distributions. Second, we use agglomerative hierarchical clustering method to divided bus stations into different groups. Results indicate that the bus stations of Jinan could be divided into four groups with obvious characteristics. The findings could help operators to make exclusive strategies to manage bus systems.

Transposition of Location-based Games: Using Procedural Content Generation to deploy balanced game maps to multiple locations

Location-Based Games (LBGs) rely on the player’s location to change its game state. Developing worldwide LBGs is a challenging task due to the need to deploy game instances in multiple locations, while maintaining the same game balancing, features, and even correlations between locations of the game and the real world. Hence, it is virtually impossible to manually design interactions, challenges, and game scenarios for every place a player is at. As a result, even modern LBGs still present huge balancing differences between regions and avoid exploring the competition between players like other game genres. This work uses Procedural Content Generation to transpose maps of LBGs while focusing on maintaining their game balancing. We convert LBGs into a game model based on a directed weighted graph using information about Points-of-Interest (POIs) around the players’ location. This game model is simplistic and lightweight, which makes it suitable to wearable and IoT devices that are becoming popular among LBGs. Moreover, we use a Genetic Algorithm to generate a corresponding LBG instance with similar game balancing. To validate the proposed approach, we designed four LBGs with distinct features, gameplay, and mechanics, and conducted an experiment that required samples to compare maps generated by the algorithm in different locations. Results indicate that games with similar game balancing score higher and that the number of POIs has a significant impact on the performance of the approach. Finally, we can conclude that this work contributes to improving the development of LBGs by helping to mitigate the challenge of transposing LBGs while maintaining game balancing.

A Data-Driven Approach to Trip Generation Modeling for Urban Residents and Non-local Travelers

Trip generation modeling is essential in transportation planning activities. Previous modeling methods that depend on traditional data collection methods are inefficient and expensive. This paper proposed a novel data-driven trip generation modeling method for urban residents and non-local travelers utilizing location-based social network (LBSN) data and cellular phone data and conducted a case study in Nanjing, China. First, the point of interest (POI) data of the LBSN were classified into various categories by the service type, then, four features of each category including the number of users, number of POIs, number of check-ins, and number of photos were aggregated by traffic analysis zones to be used as explanatory variables for the trip generation models. We used a random tree regression method to select the most important features as the model inputs, and the trip models were established based on the ordinary least square model. Then, an exploratory approach was used to test the performance of each combination of the variables with various test methods to identify the best model for residents’ and travelers’ trip generation functions. The results suggest land use compositions have significant impact on trip generations, and the trip generation patterns are different between urban residents and non-local travelers.

Spatial price dependencies between the traditional accommodation sector and the sharing economy

The emergence of peer-to-peer (P2P) accommodation (e.g. Airbnb) has steadily increased the pressure on the traditional accommodation sector. Although Airbnb listings are perceived as being more affordable than hotels, this has not yet been conclusively demonstrated. Therefore, the aim of this study is to investigate whether significant price dependencies exist between the Airbnb and traditional accommodation sectors and to analyze the underlying pricing strategies. For this purpose, the Estonian capital city of Tallinn is used as a case example. Airbnb data, prices and locations of hotels in Tallinn, as well as spatial information such as distance to points of interest (POIs), and so on, are used in hedonic price regression models. The results show that Airbnb pricing positively depends on characteristics of the listing and the number of POIs within an optimal 650 m radius, which is obtained from a simulation study. Also, prices of hotels and of other Airbnb listings within the same radius positively impact Airbnb listing prices. Finally, Airbnb accommodations are shown to indeed be the more affordable alternative.

Game-theoretic methods for locating camera towers and scheduling surveillance

We develop techniques to optimise the locations and surveillance scheduling of tower-mounted camera systems used by a military force in an urban setting. Using a game-theoretic foundation, we seek to minimise expected damage from attacks or other adversarial events (e.g., emplacements of improvised explosive devices). Assuming that at most one camera may surveil a single point of interest (POI) at any time, a mixed-integer program uses an additive-probability model to optimise the placement of towers, while allocating 'aggregate, normalised surveillance time' between cameras and POIs. Linear-programming-based column generation then creates a probability distribution for camera-to-POI assignments to define implementable schedules. We prove that such schedules must exist, making the additive probability model exact. Computational examples on realistically sized problems produce high-quality solutions quickly, with quality suffering only when the number of cameras available nears the number of POIs to be surveilled. We show that an alternative game-theoretic model may produce better solutions when such a situation arises.