Geographic space is not neutral or lifeless, but an intricate living structure composed of numerous small features and a few large ones across all scales. The living structure is crucial for comprehending how geographic space shapes human activities. With the emerging geospatial big data, researchers now have unprecedented opportunities to study the relationship between geographic space and human behaviour at a finer spatial resolution. This thesis leverages multisource geospatial big data, including Twitter check-in locations, street networks from OpenStreetMap, building footprints, and night-time light images, to explore the fundamental mechanisms of human activities that underlie geographic space. To overcome the limitations of conventional analytics in this era of big data, we propose the topological representation and living structure based on Christopher Alexander's conception of space.

We utilize scaling and topological analyses to reveal the underlying living structure of geographic space with various big datasets. Our results demonstrate that tweet locations or human activities at different scales can be accurately predicted by the underlying living structure of street nodes. We also capture and characterize human activities using big data and find that building footprints and tweets show similar scaling patterns in terms of sizes of their spatial clusters. We also propose an improved spatial clustering method to increase the processing speed of geospatial big data. Finally, we adopt topological representation to identify urban centres by the fusion of multi-source geospatial big data. The living structure, together with its topological representation can help us better understand human activities patterns in the geographic space at both city and country levels.

The complexity of geographic space can be better understood through the concept of living structure, which consists of numerous interconnected smaller features and a few larger ones distributed across multiple spatial scales. The rise of geospatial big data presents unprecedented opportunities to explore this complexity, offering finer spatial resolution for analyzing urban structures and human activities. This thesis leverages diverse geospatial datasets – including street nodes, building footprints, points of interest, social media check-ins, andnighttime light imagery – to examine urban forms and the intricate patterns of geographic features and their underlying human activities across scales. To address key challenges in geographic analysis in the era of big data, this research adopts a topological representation of living structure, inspired by Christopher Alexander’s organic view of space to characterize the complexity of space. Under this perspective, geographic space is not neutral or lifeless but rather an organized complexity that can be mathematically quantified.

The findings reveal a strong correlation between human activities, as indicated by social media check-ins, and the spatial distribution of street nodes and building footprints at various scales. To account for the heterogeneous spatial distribution of geographic features, an enhanced spatial clustering method is introduced. This study also quantifies the structural complexity of intra-urban spaces and identifies urban centers by integrating multi-source geospatial data. Furthermore, the complexity of geographic space is analyzed globally usingnight time light data, leveraging recursively generated substructures. By adopting a holistic, multi-scale perspective on geographic space, the concept of living structure provides a novel framework for understanding complex human activity patterns in the era of big data. This research offers new insights into the spatial organization of cities and contributes to the development of sustainable urban environments.

Det geografiska rummets komplexitet kan förstås bättre genom konceptet living structure, som består av många sammanlänkade mindre element och ett fåtal större, fördelade över flera rumsliga skalor. Framväxten av geospatiala big data erbjuder enastående möjligheter att utforska denna komplexitet och möjliggör analyser av urbana strukturer och mänskliga aktiviteter med högre rumslig upplösning. Denna avhandling använder olika geospatiala datakällor– inklusive gatunoder, byggnadsfotavtryck, intressepunkter, incheckningar på sociala medier och nattljusbilder – för att undersöka stadsformers och geografiska företeelsers komplexa mönster samt de bakomliggande mänskliga aktiviteterna över skalor. För att hantera centrala utmaningar inom geografisk analys i big data-eran tillämpar denna studie en topologisk representation av living structure, inspirerad av Christopher Alexanders organiska syn på rummet. Ur detta perspektiv är det geografiska rummet inte neutralt eller livlöst, utan en intrikat organiserad komplexitet som kan kvantifieras.

Resultaten visar ett starkt samband mellan mänskliga aktiviteter, representerade genom incheckningar på sociala medier, och den rumsliga fördelningen av gatunoder och byggnadsfotavtryck på olika skalor. För att hantera den heterogena rumsliga fördelningen av geografiska företeelser introduceras en förbättrad metod för rumslig klustring. Studien kvantifierar även den strukturella komplexiteten – eller livfullheten – hos intra-urbana rum och identifierar stadscentra genom att integrera geospatial data från flera källor. Vidare analyseras det geografiska rummets komplexitet globalt med hjälp av nattljusdata och rekursivt genererade delstrukturer. Genom att anta ett holistiskt perspektiv på geografiskt rum över alla skalor erbjuder konceptet living structure en ny ram för att förstå mänskliga aktivitetmönster i big data-eran. Denna forskning bidrar med nya insikter i städers rumsliga organisering och ger vägledning för utvecklingen av hållbara urbana miljöer.