CONTEMPORARY POST-INDUSTRIAL URBAN AREAS FACE OPPOSING TRANSFORMATION TRENDS: ON ONE HAND, ABANDONMENT OR UNDERUTILIZATION, AND ITS REPLACEMENT BY NEW CONSTRUCTIONS AND USES, ON THE OTHER HAND, THE REVALUATION OF THE HISTORICAL FABRIC AND THE IMPLEMENTATION OF INITIATIVES TO REHABILITATE THIS LEGACY AS INDUSTRIAL HERITAGE. THIS STUDY AIMED TO UNDERSTAND THE FACTORS THAT INFLUENCE TRENDS, AND SIMULATE LAND-USE SCENARIOS. A METHODOLOGY BASED ON THREE PHASES IS PROPOSED: DIGITIZATION, EXPLORATORY SPATIAL DATA ANALYSIS AND SIMULATION. USING THE FORMER TEXTILE DISTRICT OF BELLAVISTA IN TOMÉ (CHILE), THIS STUDY CREATED AND USED HISTORICAL LAND-USE MAPS FROM 1970, 1992 AND 2019. MEANWHILE THE MAIN CHANGE OBSERVED FROM 1970 TO 1992 WAS A 59.4% REDUCTION IN HISTORICAL INFORMAL OPEN SPACES. THE MAJOR CHANGE FROM 1992 TO 2019 WAS THE HISTORICAL INFORMAL OPEN SPACE LOSS TREND CONTINUING; 65% OF THE LAND DEDICATED TO THIS USE CHANGED TO NEW USAGES. CONSEQUENTLY, THE INFLUENCE OF TWO MORPHOLOGICAL FACTORS AND THREE URBAN MANAGEMENT INSTRUMENTS ON LAND-USE CHANGES BETWEEN 1992 AND 2019 WAS STUDIED. THE PROJECTION TO 2030 SHOWED A CONTINUED TREND OF EXPANSION OF NEW HOUSING USES OVER HISTORIC URBAN GREEN SPACES AND INDUSTRIAL AREAS ON THE WATERFRONT, ALTHOUGH RESTRAINED BY THE PRESERVATION OF THE CENTRAL AREAS OF HISTORIC HOUSING AND THE TEXTILE FACTORY.

IN LINE WITH UNESCO HISTORIC URBAN LANDSCAPE APPROACH, THIS STUDY HIGHLIGHTS THE NEED FOR INTEGRATIVE TOOLS THAT CONNECT HERITAGE CONSERVATION WITH BROADER URBAN DEVELOPMENT DYNAMICS, BALANCING PRESERVATION AND GROWTH. WHILE SEVERAL MACHINE-LEARNING MODELS HAVE BEEN APPLIED TO ANALYSE THE DRIVERS OF URBAN CHANGE, THERE REMAINS A NEED FOR COMPARATIVE ANALYSES THAT ASSESS THEIR STRENGTHS, LIMITATIONS, AND POTENTIAL FOR COMBINED APPLICATIONS TAILORED TO SPECIFIC CONTEXTS. THIS STUDY AIMS TO COMPARE THE PREDICTIVE ACCURACY OF THREE LAND-USE CHANGE MODELS (RANDOM FOREST, LOGISTIC REGRESSION, AND RECURSIVE PARTITIONING REGRESSION TREES) IN ESTIMATING THE PROBABILITY OF LAND-USE TRANSITIONS, AS WELL AS THEIR INTERPRETATIVE CAPACITY TO IDENTIFY THE MAIN FACTORS DRIVING THESE CHANGES. USING DATA FROM THE BELLAVISTA NEIGHBORHOOD IN TOMÉ, CHILE, THE MODELS WERE ASSESSED THROUGH PREDICTION AND PERFORMANCE METRICS, PROBABILITY MAPS, AND AN ANALYSIS OF KEY DRIVING FACTORS. THE RESULTS UNDERSCORE THE POTENTIAL OF INTEGRATING PREDICTIVE (RANDOM FOREST) AND INTERPRETATIVE (LOGISTIC REGRESSION AND RECURSIVE PARTITIONING REGRESSION TREES) APPROACHES TO SUPPORT HERITAGE PLANNING. SPECIFICALLY, THE RESEARCH DEMONSTRATES HOW THESE MODELS CAN BE EFFECTIVELY COMBINED BY LEVERAGING THEIR RESPECTIVE STRENGTHS: EMPLOYING RANDOM FOREST FOR SPATIAL SIMULATIONS, LOGISTIC REGRESSION FOR IDENTIFYING ASSOCIATIVE FACTORS, AND RECURSIVE PARTITIONING REGRESSION TREES FOR GENERATING INTUITIVE DECISION RULES. OVERALL, THE STUDY SHOWS THAT LAND-USE CHANGE MODELS CONSTITUTE VALUABLE TOOLS FOR MANAGING URBAN TRANSFORMATION IN HERITAGE URBAN AREAS OF INTERMEDIATE CITIES.