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Distance constraint is a major concern in many spatial analyses. Buffering is one of the proximity techniques in GIS most commonly used to address this constraint. I introduce shape-based point buffering, an anisotropic and variable-distance buffer generation method conformal to the original polygons. In contrast with isotropic fixed-distance buffering, shape-based buffering is defined using a relative distance (percentage) instead of a real unit (for example, meters), and it allows all buffered boundaries to be formed at the same time. The construction and implementation of the buffering method are described below. Three emergency-response scenarios are designed to demonstrate potential applications of this buffering method, including a shape-based fixed-percentage buffer calculation and space distribution, a shape-based variable-percentage buffer region, and a reverse calculation of the task schedule from a probability surface constructed from shape-based buffers. Limitations of the method are discussed. The method has potential applications in emergency preparedness and planning to better address fairness issues when a geographic area must be zoned arbitrarily.

The built environment plays an important role in shaping physical activities and furthering a healthy lifestyle. An obesogenic environment, which promotes obesity through uninviting neighborhood design, can cause sedentary living and environmentally induced inactivity, particularly by reducing walking. Existing measures of walkability consider the distribution and features of potential destinations, but fail to account for key aspects of the built environment design, pedestrian preferences, or various reasons for walking. In this paper, we propose a new assessment method, the Perceived importance and Objective measurement of Walkability in the built Environment Rating (POWER), incorporating pedestrian preferences derived from a walking preference survey. By conducting a university campus-based case study, we developed a quantitative method, the customized analytic hierarchy process, to analyze data from the survey. The customized analytic hierarchy process was used to calculate the perceived importance of various factors. In addition, objective measurements were collected and processed from Geographic Information System (GIS) data and fieldwork. Using both perceived importance and objective measurements, we created a POWER map showing the most walkable and unwalkable places on campus. The outcome of this case study reveals the nuanced spatial variations with regard to walkability. The proposed integral measure creates an improved method for rating the walkability of the built environment. Future applications and limitations are also discussed.

Spatial demand representation is critical for applying location models to planning processes and the efficiency of modeling solutions. Current research has focused primarily on assessing and mitigating demand representation error but ignored the computational complexity of implementing demand representations and solving the associated models. We first use set theory to formulize Cromley et al's (Institutional Journal of Geographical Information Science 26 495-512) demand representation with the least common demand coverage unit (LCDCU). Then, in the application of using the maximal covering location problem (MCLP) to site base stations optimally for a cellular network, we compare the LCDCU-based representation with widely used point-lattice-based and polygon-lattice-based demand representations in terms of both computational complexity and representation error. The LCDCU-based representation creates demand objects by partitioning a demand space into potential service areas, and has several advantages including offering solutions that provide real 100% demand coverage and eliminating some errors associated with other demand representations. However, the computational complexity of implementing the LCDCU-based representation could easily become extremely high as the number of potential facility sites increases, which could be a challenge to current geographic information systems. In addition, unlike point-based and polygon-based demand representations, the LCDCU-based representations cannot be applied to the planar covering location problems where a facility can be sited anywhere. The results of our study suggest that point-lattice-based demand representations with fine granularity are a good alternative to the LCDCU-based representations due to their effective modeling solutions without extensive computation. Polygon-lattice-based demand representations are not recommended owing to both high computational complexity and relatively large representation error. This study provides some indicators on how to choose an appropriate spatial demand representation in practical applications.

Outdoor activities confer mental, physical, and social health benefits to children. In urban areas, parks are vital for children to engage in outdoor activities. Understanding public perceptions and expectations of how urban park environments influence children's activity could attenuate social and physical inequities and promote park use. Previous studies have used surveys and observational data to analyze preferences for children's activity in parks. Crowdsourced online data and volunteered geographic information (VGI) could offer a valuable addition to or substitute for traditional approaches. Our study uses social media as a passive data collection method to ascertain public perceptions and expectations about children's activity in Atlanta parks with a shorter timeframe, compared to a regular survey analysis. We collected 4026 Google Map reviews from 2017 to 2022 that related to children and applied text mining analysis to understand how people perceive children's activity in Atlanta parks and how perceptions change across different environments. Ten topics were extracted that related to children's activity in Atlanta parks: attitude, amenity, safety, social, pet, nature, recreational, water sports, water recreational, and sports. The attitude, social, and water recreational topics were the most widely discussed topics in all settings. However, in racially diverse and low-crime environments, discussions centered on pet, recreational, and sports, suggest the importance of tailored strategies to promote children's activities in parks. Park planners and policymakers can use this approach and findings to evaluate and advocate for children's activities in urban parks. Also, our work helps to expand survey analysis with passive data collection methods from small geographic scales to larger areas and to apply geoanalytics of big data and social media data in investigation research.

Recognizing urban functions is crucial for understanding urban spatial structures and urban planning. Previous work has investigated urban functions based on human activities that were derived from mobile phone positioning data, check-in data, taxi data, etc. However, urban functions can only be comprehensively sensed from both human activities and the physical environment together. To do so, a deep learning method was proposed to predict urban functions by integrating social media data and street-level imagery. The verbs extracted from social media posts were taken as the proxy for human activities, and we identified urban physical environmental information from street-level imagery. Then urban functions were uncovered from both the verbs in terms of human activities and street-level imagery from the perspective of the physical environment. Twelve types of urban function were recognized by verbs in social media posts, which were then improved by integrating street-level imagery within the 5th Ring Road of Beijing, China. The experiment demonstrated that verbs as direct proxies for human activities can avoid noise, and the multi-source data integration eliminated biases caused by a single data source. This work provides a comprehensive understanding of urban structure and dynamics for urban management and planning.