Ziqi Li

• PhD
• Assistant Professor at Florida State University

Geographically weighted regression (GWR) is a spatial statistical technique that recognizes that traditional ‘global’ regression models may be limited when spatial processes vary with spatial context. GWR captures process spatial heterogeneity by allowing effects to vary over space. To do this, GWR calibrates an ensemble of local linear models at a...

Geographically Weighted Regression (GWR) has been broadly used in various fields to model spatially non-stationary relationships. Multi-scale Geographically Weighted Regression (MGWR) is a recent advancement to the classic GWR model. MGWR is superior in capturing multi-scale processes over the traditional single-scale GWR model by using different b...

Bandwidth, a key parameter in geographically weighted regression models, is closely related to the spatial scale at which the underlying spatially heterogeneous processes being examined take place. Generally, a single optimal bandwidth (geographically weighted regression) or a set of covariate-specific optimal bandwidths (multiscale geographically...

Machine learning and artificial intelligence (ML/AI), previously considered black box approaches, are becoming more interpretable, as a result of the recent advances in eXplainable AI (XAI). In particular, local interpretation methods such as SHAP (SHapley Additive exPlanations) offer the opportunity to flexibly model, interpret and visualise compl...

This article introduces GeoShapley, a game theory approach to measuring spatial effects in machine learning models. GeoShapley extends the Nobel Prize–winning Shapley value framework in game theory by conceptualizing location as a player in a model prediction game, which enables the quantification of the importance of location and the synergies bet...

Moran eigenvector spatial filtering (ESF) approaches have shown promise in accounting for spatial effects in statistical models. Can this extend to machine learning? This article examines the effectiveness of using Moran Eigenvectors as additional spatial features in machine learning models. We generate synthetic datasets with known processes invol...

Hydrological model design is critical for accurate simulation of overland flow accumulation. It affects the measurement of drainage networks, levels of soil moisture, and odds of urban flooding. This article outlines the design of a new hydrological algorithm that simulates divergent and convergent flow scenarios with respect to the major topograph...

This chapter discusses the opportunities of eXplainable Artificial Intelligence (XAI) within the realm of spatial analysis. A key objective in spatial analysis is to model spatial relationships and infer spatial processes to generate knowledge from spatial data, which has been largely based on spatial statistical methods. More recently, machine lea...

Modeling geospatial tabular data with deep learning has become a promising alternative to traditional statistical and machine learning approaches. However, existing deep learning models often face challenges related to scalability and flexibility as datasets grow. To this end, this paper introduces GeoAggregator, an efficient and lightweight algori...

Propose an efficient transformer model for geo-spatial tabular data.

This study uses building footprint data from the Ordnance Survey MasterMap to analyze construction and demolition activities across England from 2017 to 2023. By comparing the Topographic Object Identifiers (TOIDs) of each building between years, we identified newly constructed and demolished buildings, quantified changes, and used the bivariate co...

Spatial autoregressive (SAR) models are often used to explicitly account for the spatial dependence underlying geographic phenomena. However, traditional SAR models are specified using a single SAR coefficient, assuming constant spatial dependence over space. This assumption oversimplifies the situation where the true spatial autoregressive process...

Scale, context, and heterogeneity have been central issues in geography. From a quantitative standpoint, accurately identifying the scale and context at which geographical processes operate and capturing their spatial heterogeneity have been challenging tasks. Despite various prominent developments in spatial modeling literature, there is a lack of...

Glasgow, Scotland, United Kingdom, has long-term issues with inequalities in health and food security, as well as large areas of vacant and derelict land. Urban agriculture projects can increase access to fresh food, improve mental health and nutrition, and empower and bring communities together. We investigated the distribution of urban agricultur...

Geographic regression models of various descriptions are often applied to identify patterns and anomalies in the determinants of spatially distributed observations. These types of analyses focus on answering why questions about underlying spatial phenomena, e.g., why is crime higher in this locale, why do children in one school district outperform...

Geographic regression models of various descriptions are often applied to identify patterns and anomalies in the determinants of spatially distributed observations. These types of analyses focus on answering why questions about underlying spatial phenomena, e.g., why is crime higher in this locale, why do children in one school district outperform...

As individual's cognitions and behaviours are affected by where they live, the reliability of responses to tests or scales may vary with location. In this paper, we develop a local version of Cronbach's alpha, geographically weighted Cronbach's alpha, to investigate how the reliability of the measure varies spatially. Two demonstrations of explorat...

Carpool-style ridesharing, compared to traditional solo ride-hailing, can reduce traffic congestion, cut per-passenger carbon emissions, reduce parking infrastructure, and provide a more cost-effective way to travel. Despite these benefits, ridesharing only occupies a small percentage of the total ride-hailing trips in cities. This study integrates...

People’s attitudes towards hydraulic fracturing (fracking) can be shaped by socio-demographics, economic development, social equity and politics, environmental impacts, and fracking-related information. Existing research typically conducts surveys and interviews to study public attitudes towards fracking among a small group of individuals in a spec...

Models designed to capture spatially varying processes are now employed extensively in the social and environmental sciences. The main strength of such models is their ability to represent relationships that vary across locations through locally varying parameter estimates. However, local models of spatial processes also provide information on the...

Real estate market analysis has long been an active area of inquiry and one that reveals much about people’s preferences regarding housing attributes. It is well-known that house prices tend to exhibit strong spatial dependency and that they vary across space due to differences in structural and neighborhood characteristics. It is perhaps less well...

Political and social processes that shape people's voting preferences might be linked to geographical location, varying from place to place, and operating at local, regional, and national scales. Here, we use a local modeling technique, multiscale geographically weighted regression (MGWR), to examine spatial and temporal variations in the influence...

Long-term community resilience, which privileges a long view look at chronic issues influencing communities, has begun to draw more attention from city planners, researchers and policymakers. In Phoenix, resilience to heat is both a necessity and a way of life. In this paper, we attempt to understand how residents living in Phoenix experience and b...

Studies of spatially varying parameter estimates obtained in the calibration of various types of local statistical models are commonplace. The variation in such estimates is typically explained in terms of spatially varying processes. This paper highlights that an alternative explanation for spatially varying parameter estimates, in terms of non-li...

PySAL is a library for geocomputation and spatial data science. Written in Python, the library has a long history of supporting novel scholarship and broadening methodological impacts far afield of academic work. Recently, many new techniques, methods of analyses, and development modes have been implemented, making the library much larger and more...

Background: Persisting within-country disparities in maternal health service access are significant barriers to attaining the Sustainable Development Goals aimed at reducing inequalities and ensuring good health for all. Sub-national decision-makers mandated to deliver health services play a central role in advancing equity but require appropriate...

Long-term community resilience, which privileges a long view look at chronic issues influencing communities, has begun to draw more attention from city planners, researchers and policymakers. In Phoenix, resilience to heat is both a necessity and a way of life. In this paper, we attempt to understand how residents living in Phoenix experience and b...

This article attempts to identify and separate the role of spatial “context” in shaping voter preferences from the role of other socioeconomic determinants. It does this by calibrating a multiscale geographically weighted regression (MGWR) model of county-level data on percentages voting for the Democratic Party in the 2016 U.S. presidential electi...

This study aims to examine the spatially varying relationships between social vulnerability factors and COVID-19 cases and deaths in the contiguous United States. County-level COVID-19 data and the Centers for Disease Control and Prevention social vulnerability index (SVI) dataset were analyzed using local Spearman's rank correlation coefficient. R...

Geographically Weighted Regression (GWR) has been broadly used in various fields to model spatially non-stationary relationships. Classic GWR is considered as a single-scale model that is based on one bandwidth parameter which controls the amount of distance-decay in weighting neighboring data around each location. The single bandwidth in GWR assum...

Under the realization that Geographically Weighted Regression (GWR) is a data-borrowing technique, this paper derives expressions for the amount of bias introduced to local parameter estimates by borrowing data from locations where the processes might be different from those at the regression location. This is done for both GWR and Multiscale GWR (...

Geographically Weighted Regression (GWR) has been broadly used in various fields to model spatially non-stationary relationships. Multi-scale Geographically Weighted Regression (MGWR) is a recent advancement to the classic GWR model. MGWR is superior in capturing multi-scale processes over the traditional single-scale GWR model by using different b...

This study evaluates the influences of air pollution in China using a recently proposed model—multi‐scale geographically weighted regression (MGWR). First, we review previous research on the determinants of air quality. Then, we explain the MGWR model, together with two global models: ordinary least squares (OLS) and OLS containing a spatial lag va...

Bandwidth, a key parameter in geographically weighted regression models, is closely related to the spatial scale at which the underlying spatially heterogeneous processes being examined take place. Generally, a single optimal bandwidth (geographically weighted regression) or a set of covariate-specific optimal bandwidths (multiscale geographically...

Under the realization that Geographically Weighted Regression (GWR) is a data-borrowing technique, this paper derives expressions for the amount of bias introduced to local parameter estimates by borrowing data from locations where the processes might be different from those at the regression location. This is done for both GWR and Multiscale GWR (...

A recent paper in this journal proposed a form of geographically weighted regression (GWR) that is termed parameter-specific distance metric geographically weighted regression (PSDM GWR). The central focus of the PSDM generalization of the GWR framework is that it allows the kernel function that weights nearby data to be specified with a distinct d...

A recent paper expands the well‐known geographically weighted regression (GWR) framework significantly by allowing the bandwidth or smoothing factor in GWR to be derived separately for each covariate in the model—a framework referred to as multiscale GWR (MGWR). However, one limitation of the MGWR framework is that, until now, no inference about th...

Geographically Weighted Regression (GWR) is a widely-used tool for exploring spatial heterogeneity of processes over geographic space. GWR computes location-specific parameter estimates, which makes its calibration process computationally intensive. The maximum number of data points that can be handled by current open-source GWR software is approxi...

Geographically weighted regression (GWR) is a spatial statistical technique that recognizes traditional 'global' regression models may be limited when spatial processes vary with spatial context. GWR captures process spatial heterogeneity via an operationalization of Tobler's first law of geography: "everything is related to everything else, but ne...

A recent paper (Fotheringham et al. 2017) expands the well-known Geographically Weighted Regression (GWR) framework significantly by allowing the bandwidth or smoothing factor in GWR to be derived separately for each covariate in the model – a framework referred to as Multiscale GWR (MGWR). However, one limitation of the MGWR framework is that, unt...

NoSQL databases are open-source, schema-less, horizontally scalable and high-performance databases. These characteristics make them very different from relational databases, the traditional choice for spatial data. The four types of data stores in NoSQL databases (key-value store, document store, column store, and graph store) contribute to signifi...

Sustainable solar energy is of the interest for the city of San Francisco to meet their renewable energy initiative. Buildings in the downtown area are expected to have great photovoltaic (PV) potential for future solar panel installation. This study presents a comprehensive method for estimating geographical PV potential using remote sensed LiDAR...