A GIS – Based Approach for Catchment Area Analysis of Convenience Store

Widaningrum, Dyah Lestari

doi:10.1016/j.procs.2015.12.158

Cited by 30 publications

(10 citation statements)

References 8 publications

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“…Catchment area analysis has been extended to include the delineation of boundaries for more than just schools and hospitals. Recent studies have extended problems to include retail store locations [20], bike share access [21], and food store accessibility [22]. Researchers investigating catchment areas are often interested in determining who is served (in terms of socio-economic or demographic characteristics), the size of the catchment area, and relatedly, the ability of the service to meet the demand of the population in the catchment.…”

Section: Catchment Areasmentioning

confidence: 99%

“…One of the major goals of any transportation network is the ability to get people to where they need to go [29]. This has been evaluated with retail store locations [20], libraries, and supermarkets [30]. The determination of who has accessibility to certain amenities by way of public transportation (or other transportations methods) would in turn provide a reasonable estimation of the population served by that particular amenity.…”

Section: Catchment Areasmentioning

confidence: 99%

See 1 more Smart Citation

Information fusion to estimate resilience of dense urban neighborhoods

Palladino

Bienenstock

West

et al. 2019

Signal Processing, Sensor/Information Fusion, and Target Recognition XXVIII

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Diverse sociocultural influences in rapidly growing dense urban areas may induce strain on civil services and reduce the resilience of those areas to exogenous and endogenous shocks. We present a novel approach with foundations in computer and social sciences, to estimate the resilience of dense urban areas at finer spatiotemporal scales compared to the state-ofthe-art. We fuse multi-modal data sources to estimate resilience indicators from social science theory and leverage a structured ontology for factor combinations to enhance explainability. Estimates of destabilizing areas can improve the decision-making capabilities of civil governments by identifying critical areas needing increased social services.Keywords: Dense urban areas, behavior modeling, social/cultural modeling, social capital, situation/threat assessment INTRODUCTIONTwo-thirds of the world's population is expected to live in urban areas by the year 2050. This rapid growth of dense neighborhoods with diverse sociocultural influences may strain civil services, e.g., law enforcement, health care, and waste management, reducing the resilience of these areas to exogenous and endogenous shocks. Aggregate statistics for large, sprawling, urban areas are likely to miss critical indicators.To address these issues, we developed a novel approach that advances the state-of-the-art by (i) ascertaining the stability and resilience of densely populated areas at much finer geospatial and temporal scales, and (ii) basing that estimate on a solid social science foundation. We fuse multi-modal data sources to estimate the prevalence of indicators from social theories (e.g., social capital). Aggregate statistics from structured city-level data (censuses, surveys) provide a baseline for the entire city, while social media and news reports provide features at shorter time scales, and smaller geographic areas (i.e., neighborhoods, city blocks) introducing deviations from the baseline. This social science foundation, combined with our structured ontology, leads to enhanced explainability of the stability assessment, increasing confidence in downstream decisions. Knowledge of destabilizing areas informs the decisionmaking process in civil governments by identifying neighborhoods in need of social services and civil control. Our computational framework estimates the capacity of a dense urban area to cope with stress and volatility, allowing governments, soldiers, humanitarian organizations, and private companies to better understand and manage local disturbances. THEORETICAL FRAME: SOCIAL SCIENCE FOUNDATIONThere is a need to bridge the gap between the computational and social sciences. Modern decision-makers require support from computational tools with near real-time feedback, however social science theories are still validated/quantified after lengthy surveys and structured interviews. We performed comprehensive research and built methods using open-source data and text analytics to allow data science to quantify social science theories at a rapid pace. As a first step...

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Section: Catchment Areasmentioning

confidence: 99%

Section: Catchment Areasmentioning

confidence: 99%

Information fusion to estimate resilience of dense urban neighborhoods

Palladino

Bienenstock

West

et al. 2019

Signal Processing, Sensor/Information Fusion, and Target Recognition XXVIII

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“…This section focuses on the analysis of transfer characteristics, access and egress distance, and catchment area of metro stations. Notably, in the following part, bikeshare users are categorized into five age groups: Age Group 1 (below 18), Age Group 2 (19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35), Age Group 3 (36-45), Age Group 4 (46-60 for males and 46-55 for females), and Age Group 5 (above 60 for males and above 55 for females). This categorization takes into consideration Chinese citizens' statutory age of retirement-very often 55 for female and 60 for male.…”

Section: Resultsmentioning

confidence: 99%

“…The delineation of the catchment area of a station is possible when access distances are decided [17]. In general, three methods were applied in previous studies: The simple buffer with the Euclidean distance [23], the combination of the Thiessen polygon and buffer [24], and the network-distance-based method [25]. The first two methods always overestimated the catchment area; therefore, in many studies the network-distance-based method was applied and developed on the basis that travelers pick the shortest path from the road network and cycle to metro stations.…”

Section: Metro-bicycle Transferring Distance and Catchment Areamentioning

confidence: 99%

Measuring Bikeshare Access/Egress Transferring Distance and Catchment Area around Metro Stations from Smartcard Data

Jin

2018

Information

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Metro–bikeshare integration is considered a green and efficient travel model. To better develop such integration, it is necessary to monitor and analyze metro–bikeshare transfer characteristics. This paper measures access and egress transferring distances and catchment areas based on smartcard data. A cubic regression model is conducted for the exploration of the 85th access and egress network-based transferring distance around metro stations. Then, the independent samples t-test and one-way analysis of variance (ANOVA) are used to explore access and egress transfer characteristics in demographic groups and spatial and temporal dimension. Additionally, the catchment area is delineated by applying both the network-based distance method and Euclidean distance method. The result reveals that males outcompete females both in access and egress distances and urban dwellers ride a shorter distance than those in suburban areas. Access and egress distances are both shorter in morning peak hours than those in evening peak hours and access distance on weekdays is longer than that on weekends. In addition, network-based catchment area accounts for over 90% of Euclidean catchment area in urban areas, while most of the ratios are less than 85% in suburban. The paper uses data from Nanjing, China as a case study. This study serves as a scientific basis for policy makers and bikeshare companies to improve metro–bikeshare integration.

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“…These bisectors join together and create the thiessen polygon (Brassel and Reif, 1979). It is a popular method used in many market analysis and meteorological studies like Derakhshan and Talebbeydokhti, 2011;Halls et al, 2001;Lewis, 2008 andWidaningrum, 2015. In the given map, for any location of the user, the nearest school can be easily identified by point in polygon analysis.…”

Section: Geosptaial Analysis Of Datamentioning

confidence: 99%

School Mapping and Geospatial Analysis of the Schools in Jasra Development Block of India

Agrawal¹,

Gupta²

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:GIS is a collection of tools and techniques that works on the geospatial data and is used in the analysis and decision making. Education is an inherent part of any civil society. Proper educational facilities generate the high quality human resource for any nation. Therefore, government needs an efficient system that can help in analysing the current state of education and its progress. Government also needs a system that can support in decision making and policy framing. GIS can serve the mentioned requirements not only for government but also for the general public. In order to meet the standards of human development, it is necessary for the government and decision makers to have a close watch on the existing education policy and its implementation condition. School mapping plays an important role in this aspect. School mapping consists of building the geospatial database of schools that supports in the infrastructure development, policy analysis and decision making. The present research work is an attempt for supporting Right to Education (RTE) and Sarv Sikha Abhiyaan (SSA) programmes run by Government of India through the use of GIS. School mapping of the study area is performed which is followed by the geospatial analysis. This research work will help in assessing the present status of educational infrastructure in Jasra block of Allahabad district, India.

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A GIS – Based Approach for Catchment Area Analysis of Convenience Store

Cited by 30 publications

References 8 publications

Information fusion to estimate resilience of dense urban neighborhoods

Information fusion to estimate resilience of dense urban neighborhoods

Measuring Bikeshare Access/Egress Transferring Distance and Catchment Area around Metro Stations from Smartcard Data

School Mapping and Geospatial Analysis of the Schools in Jasra Development Block of India

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