This paper brings a comprehensive systematic review of the application of geospatial artificial intelligence (GeoAI) in quantitative human geography studies, including the subdomains of cultural, economic, political, historical, urban, population, social, health, rural, regional, tourism, behavioural, environmental and transport geography. In this extensive review, we obtain 14,537 papers from the Web of Science in the relevant fields and select 1516 papers that we identify as human geography studies using GeoAI via human scanning conducted by several research groups around the world. We outline the GeoAI applications in human geography by systematically summarising the number of publications over the years, empirical studies across countries, the categories of data sources used in GeoAI applications, and their modelling tasks across different subdomains. We find out that existing human geography studies have limited capacity to monitor complex human behaviour and examine the non-linear relationship between human behaviour and its potential drivers—such limits can be overcome by GeoAI models with the capacity to handle complexity. We elaborate on the current progress and status of GeoAI applications within each subdomain of human geography, point out the issues and challenges, as well as propose the directions and research opportunities for using GeoAI in future human geography studies in the context of sustainable and open science, generative AI, and quantum revolution.

Analyzing spatial varying effect is pivotal in geographic analysis. Yet, accurately capturing and interpreting this variability is challenging due to the complexity and non-linearity of geospatial data. Herein, we introduce an integrated framework that merges local spatial weighting scheme, Explainable Artificial Intelligence (XAI), and cutting-edge machine learning technologies to bridge the gap between traditional geographic analysis models and general machine learning approaches. Through tests on synthetic datasets, this framework is verified to enhance the interpretability and accuracy of predictions in both geographic regression and classification by elucidating spatial variability. It significantly boosts prediction precision, offering a novel approach to understanding spatial phenomena.

Quantitative geographical methods have played an important role in COVID-19 research. To complement and extend previous review studies, we conduct a scoping review of COVID-19 studies employing quantitative geographical approaches by focusing on 331 papers published in 45 journals in geography, urban studies, and planning. We identify four major research themes (clusters): (1) how SARS-CoV-2 viruses spread in cities, (2) the COVID-19 mortality (death) rates and their association with socioeconomic variables, (3) how the COVID-19 pandemic changed people’s mobilities, and (4) how the COVID-19 pandemic affects air pollution. We conclude that spatial models play a key role in COVID-19 quantitative geographical approaches, and human mobility is an important and widely studied topic. We also reveal a lack of research focusing on environmental pollution (other than air pollution) that potentially worsened during the pandemic.

The Geospatial Analytics Extension for KNIME (GAEK) is an innovative tool designed to integrate visual programming with geospatial analytics, streamlining GIS education and research in social sciences. GAEK simplifies access for users with an intuitive, visual interface for complex spatial analysis tasks and contributes to the organization of the GIS Knowledge Tree through its geospatial analytics nodes. This paper discusses GAEK's architecture, functionalities, and its transformative impact on GIS applications. While GAEK significantly enhances user experience and research reproducibility, future updates aim to expand its functionality and optimize its bundled environment.

Firms may seek non-redundant information through inter-firm relations beyond their geographic and cognitive boundaries (i.e., relations with firms in other regions and active in different fields). Little is known about the conditions under which firms benefit from this high-risk/high-gain strategy. We created a digital layer of 600,000 German firms by using their websites’ textual and relational content. Our results suggest that strong relations (relations with common third partners) between firms from different fields and inter-regional relations are positively associated with a firm’s innovation level. We also found that a specific combination of weak and strong relations confers greater innovation benefits.

The properties of emerging, digital, general-purpose technologies make it hard to observe their adoption by firms and identify the salient determinants of adoption. However, these aspects are critical since the patterns related to early-stage diffusion establish path-dependencies which have implications for the distribution of the technological opportunities and socio-economic returns linked to these technologies. We focus on the case of artificial intelligence (AI) and train a transformer language model to identify firm-level AI adoption using textual data from over 1.1 million websites and constructing a hyperlink network that includes >380,000 firms in Germany, Austria, and Switzerland. We use these data to expand and test epidemic models of inter-firm technology diffusion by integrating the concepts of social capital and network embeddedness. We find that AI adoption is related to three epidemic effect mechanisms: 1) Indirect co-location in industrial and regional hot-spots associated to production of AI knowledge; 2) Direct exposure to sources transmitting deep AI knowledge; 3) Relational embeddedness in the AI knowledge network. The pattern of adoption identified is highly clustered and features a rather closed system of AI adopters which is likely to hinder its broader diffusion. This has implications for policy which should facilitate diffusion beyond localized clusters of expertise. Our findings also point to the need to employ a systemic perspective to investigate the relation between AI adoption and firm performance to identify whether appropriation of the benefits of AI depends on network position and social capital.

Promoting well-being is one of the key targets of the Sustainable Development Goals at the United Nations. Many national and city governments worldwide are incorporating Subjective Well-Being (SWB) indicators into their agenda, to complement traditional objective development and economic metrics. In this study, we introduce the Twitter Sentiment Geographical Index (TSGI), a location-specific expressed sentiment database with SWB implications, derived through deep-learning-based natural language processing techniques applied to 4.3 billion geotagged tweets worldwide since 2019. Our open-source TSGI database represents the most extensive Twitter sentiment resource to date, encompassing multilingual sentiment measurements across 164 countries at the admin-2 (county/city) level and daily frequency. Based on the TSGI database, we have created a web platform allowing researchers to access the sentiment indices of selected regions in the given time period.

This textbook integrates GIS, spatial analysis, and computational methods for solving real-world problems in various policy-relevant social science applications. Thoroughly updated, the third edition showcases the best practices of computational spatial social science and includes numerous case studies with step-by-step instructions in ArcGIS Pro and open-source platform KNIME. Readers sharpen their GIS skills by applying GIS techniques in detecting crime hotspots, measuring accessibility of primary care physicians, forecasting the impact of hospital closures on local community, or siting the best locations for business.

FEATURES

• Fully updated using the latest version of ArcGIS Pro and open-source platform KNIME
• Features two brand-new chapters on agent-based modeling and big data analytics
• Provides newly automated tools for regionalization, functional region delineation, accessibility measures, planning for maximum equality in accessibility, and agent-based crime simulation
• Includes many compelling examples and real-world case studies related to social science, urban planning, and public policy
• Provides a website for downloading data and programs for implementing all case studies included in the book and the KNIME lab manual

Intended for students taking upper-level undergraduate and graduate-level courses in quantitative geography, spatial analysis, and GIS applications, as well as researchers and professionals in fields such as geography, city and regional planning, crime analysis, public health, and public administration.

Background: Stay-at-home orders were one of the controversial interventions to curb the spread of COVID-19 in the United States. The stay-at-home orders, implemented in 51 states and territories between March 7 and June 30, 2020, impacted the lives of individuals and communities and accelerated the heavy usage of web-based social networking sites. Twitter sentiment analysis can provide valuable insight into public health emergency response measures and allow for better formulation and timing of future public health measures to be released in response to future public health emergencies.

Objective: This study evaluated how stay-at-home orders affect Twitter sentiment in the United States. Furthermore, this study aimed to understand the feedback on stay-at-home orders from groups with different circumstances and backgrounds. In addition, we particularly focused on vulnerable groups, including older people groups with underlying medical conditions, small and medium enterprises, and low-income groups.

Methods: We constructed a multiperiod difference-in-differences regression model based on the Twitter sentiment geographical index quantified from 7.4 billion geo-tagged tweets data to analyze the dynamics of sentiment feedback on stay-at-home orders across the United States. In addition, we used moderated effects analysis to assess differential feedback from vulnerable groups.

Results: We combed through the implementation of stay-at-home orders, Twitter sentiment geographical index, and the number of confirmed cases and deaths in 51 US states and territories. We identified trend changes in public sentiment before and after the stay-at-home orders. Regression results showed that stay-at-home orders generated a positive response, contributing to a recovery in Twitter sentiment. However, vulnerable groups faced greater shocks and hardships during the COVID-19 pandemic. In addition, economic and demographic characteristics had a significant moderating effect.

Conclusions: This study showed a clear positive shift in public opinion about COVID-19, with this positive impact occurring primarily after stay-at-home orders. However, this positive sentiment is time-limited, with 14 days later allowing people to be more influenced by the status quo and trends, so feedback on the stay-at-home orders is no longer positively significant. In particular, negative sentiment is more likely to be generated in states with a large proportion of vulnerable groups, and the policy plays a limited role. The pandemic hit older people, those with underlying diseases, and small and medium enterprises directly but hurt states with cross-cutting economic situations and more complex demographics over time. Based on large-scale Twitter data, this sociological perspective allows us to monitor the evolution of public opinion more directly, assess the impact of social events on public opinion, and understand the heterogeneity in the face of pandemic shocks.

A lightning talk given at the 2023 Esri International Users Conference.

Background

Descriptive epidemiological data on incidence rates (IRs) of asthma with recurrent exacerbations (ARE) are sparse.

Objectives

This study hypothesized that IRs for ARE would vary by time, geography, age, and race and ethnicity, irrespective of parental asthma history.

Methods

The investigators leveraged data from 17,246 children born after 1990 enrolled in 59 US with 1 Puerto Rican cohort in the Environmental Influences on Child Health Outcomes (ECHO) consortium to estimate IRs for ARE.

Results

The overall crude IR for ARE was 6.07 per 1000 person-years (95% CI: 5.63-6.51) and was highest for children aged 2-4 years, for Hispanic Black and non-Hispanic Black children, and for those with a parental history of asthma. ARE IRs were higher for 2- to 4-year-olds in each race and ethnicity category and for both sexes. Multivariable analysis confirmed higher adjusted ARE IRs (aIRRs) for children born 2000-2009 compared with those born 1990-1999 and 2010-2017, 2-4 versus 10-19 years old (aIRR = 15.36; 95% CI: 12.09-19.52), and for males versus females (aIRR = 1.34; 95% CI 1.16-1.55). Black children (non-Hispanic and Hispanic) had higher rates than non-Hispanic White children (aIRR = 2.51; 95% CI 2.10-2.99; and aIRR = 2.04; 95% CI: 1.22-3.39, respectively). Children born in the Midwest, Northeast and South had higher rates than those born in the West (P < .01 for each comparison). Children with a parental history of asthma had rates nearly 3 times higher than those without such history (aIRR = 2.90; 95% CI: 2.43-3.46).

Conclusions

Factors associated with time, geography, age, race and ethnicity, sex, and parental history appear to influence the inception of ARE among children and adolescents.

Travel time estimation is crucial for several geospatial research studies, particularly healthcare accessibility studies. This paper presents a comparative study of six methods for drive time estimation on geospatial big data in the USA. The comparison is done with respect to the cost, accuracy, and scalability of these methods. The six methods examined are Google Maps API, Bing Maps API, Esri Routing Web Service, ArcGIS Pro Desktop, OpenStreetMap NetworkX (OSMnx), and Open Source Routing Machine (OSRM). Our case study involves calculating driving times of 10,000 origin-destination (OD) pairs between ZIP code population centroids and pediatric hospitals in the USA. We found that OSRM provides a low-cost, accurate, and efficient solution for calculating travel time on geospatial big data. Our study provides valuable insight into selecting the most appropriate drive time estimation method and is a benchmark for comparing the six different methods. Our open-source scripts are published on GitHub (https://github.com/wybert/Comparative-Study-of-Methods-for-Drive-Time-Es...) to facilitate further usage and research by the wider academic community. 

Travel time estimation is crucial for several geospatial research studies, particularly healthcare accessibility studies. This paper presents a comparative study of six methods for drive time estimation on geospatial big data in the USA. The comparison is done with respect to the cost, accuracy, and scalability of these methods. The six methods examined are Google Maps API, Bing Maps API, Esri Routing Web Service, ArcGIS Pro Desktop, OpenStreetMap NetworkX (OSMnx), and Open Source Routing Machine (OSRM). Our case study involves calculating driving times of 10,000 origin-destination (OD) pairs between ZIP code population centroids and pediatric hospitals in the USA. We found that OSRM provides a low-cost, accurate, and efficient solution for calculating travel time on geospatial big data. Our study provides valuable insight into selecting the most appropriate drive time estimation method and is a benchmark for comparing the six different methods. Our open-source scripts are published on GitHub (https://github.com/wybert/Comparative-Study-of-Methods-for-Drive-Time-Es...) to facilitate further usage and research by the wider academic community.

Equity in health care delivery is a longstanding concern of public health policy. Telehealth is considered an important way to level the playing field by broadening health services access and improving quality of care and health outcomes. This study refines the recently developed “2-Step Virtual Catchment Area (2SVCA) method” to assess the telehealth accessibility of primary care in the Baton Rouge Metropolitan Statistical Area, Louisiana. The result is compared to that of spatial accessibility via physical visits to care providers based on the popular 2-Step Floating Catchment Area (2SFCA) method. The study shows that both spatial and telehealth accessibilities decline from urban to low-density and then rural areas. Moreover, disproportionally higher percentages of African Americans are in areas with higher spatial accessibility scores; but such an advantage is not realized in telehealth accessibility. In the study area, absence of broadband availability is mainly a rural problem and leads to a lower average telehealth accessibility than physical accessibility in rural areas. On the other side, lack of broadband affordability is a challenge across the rural-urban continuum and is disproportionally associated with high concentrations of disadvantaged population groups such as households under the poverty level and Blacks.

The lack of large-scale data revealing the interactions among firms has constrained empirical studies. Utilizing relational web data has remained unexplored as a remedy for this data problem. We constructed a Digital Layer by scraping the inter-firm hyperlinks of 600,000 German firms and linked the Digital Layer with several traditional indicators. We showcase the use of this developed dataset by testing whether the Digital Layer data can replicate several theoretically motivated and empirically supported stylized facts. The results show that the intensity and quality of firms’ hyperlinks are strongly associated with the innovation capabilities of firms and, to a lesser extent, with hyperlink relations to geographically distant and cognitively close firms. Finally, we discuss the implications of the Digital Layer approach for an evidence-based assessment of sectoral and place-based innovation policies.

This paper seeks to unveil how (geospatial) connection strategies associated with business innovation, differ between geolocated social media and hyperlink company networks. Thereby, we provide a first step towards understanding connection strategies of innovative companies on social media platforms. For this purpose, we build a hyperlink and Twitter follower network for 11,892 companies in the information technology (IT) sector and compare them along four dimensions. First, underlying network structures were assessed. Second, we asserted information flow patterns between companies with the help of centrality measures. Third, geographic and cognitive proximities of companies were compared. Fourth, the influence of company characteristics was examined through linear and logistic regression models. This comparison revealed, that on a general level the basic connection patterns of the hyperlink and Twitter network differ. Nevertheless, the geospatial dimension (geographic proximity) and the knowledge base of a company (cognitive proximity) appear to have a similar influence on the decision to connect with other companies on Twitter and via hyperlinks. Further, the results suggest that innovative companies most likely align their connection strategies across hyperlink and Twitter networks. Thus, business innovation might effect connection strategies across online company networks in a comparable manner.

Coronavirus disease 2019 (COVID-19) continues to affect the world, and the design of strategies to curb disease outbreaks requires close monitoring of their trajectories. We present machine learning methods that leverage internet-based digital traces to anticipate sharp increases in COVID-19 activity in U.S. counties. In a complementary direction to the efforts led by the Centers for Disease Control and Prevention (CDC), our models are designed to detect the time when an uptrend in COVID-19 activity will occur. Motivated by the need for finer spatial resolution epidemiological insights, we build upon previous efforts conceived at the state level. Our methods—tested in an out-of-sample manner, as events were unfolding, in 97 counties representative of multiple population sizes across the United States—frequently anticipated increases in COVID-19 activity 1 to 6 weeks before local outbreaks, defined when the effective reproduction number Rt becomes larger than 1 for a period of 2 weeks.

This study examined transit-based accessibility to hospitals in Ulaanbaatar, the capital city of Mongolia, which is one of the low- and middle-income countries (LMICs). Promoting transit-based accessibility to hospitals is an important public health policy goal because limited accessibility can lead to adverse health outcomes. Public transit is especially crucial for people living in LMICs because many of them lack private vehicles. However, transit-based hospital accessibility has not been widely studied in LMICs. With the recent development of open-source transit analysis tools and standardization of schedule-based transit network data protocols, we could build Ulaanbaatar's schedule-based transit network dataset in great detail. We computed transit-based accessibility to hospitals from 128,032 residential parcels in Ulaanbaatar. Overall, transit-based accessibility to hospitals was higher in the central area than in the peripheral areas of Ulaanbaatar. Specifically, transit-based accessibility to hospitals was significantly lower in the ger area (settlements without central infrastructure connection to heat, water, and sewage) than in the non-ger area (apartment area). The results revealed that about 10% of people living in the study area have inadequate transit-based accessibility to hospitals. Our research is one of the first studies attempting to create a detailed schedule-based transit network and measure transit-based accessibility to hospitals in a rapidly growing, under-examined city in LMICs.

Cycling mobility contributes to better livability in cites, helps societies to reduce greenhouse gas emissions and their dependency on fossil fuels, and shows positive health effects. However, unattractive conditions, primarily inadequate infrastructure, hinder the further growth of cycling mobility. As interactions of cyclists with the (built) environment are complex, assessing potential impacts of an intervention aimed at improving physical conditions is not trivial. Despite a growing body of literature on various facets of cycling mobility, assessments are widely limited to a single method and thereby either focus on one detailed aspect or on one perspective. While multi-method and mixed methods studies are emerging, they are not embedded into a structured, integrated framework for assessing systemic effects of interventions yet. Therefore, we propose a conceptual integration of several relevant methods such as questionnaires, interviews, GIS analyses and human sensing. In this paper, we present a generic, extensible framework that offers guidance for developing and implementing case-specific mixed methods designs for multifaceted assessments of interventions. The framework supports domain experts and researchers across different stages of conducting a study. Results from this research further indicate the added value of mixed methods studies compared to single-method approaches.

Editorial of the Special Issue on Spatiotemporal Analysis of the Impact of COVID-19