## 1 Introduction
Simulations that address the effect of urban change on the conservation of the built heritage fabric are very scarce. Some examples are the research of [PERSON] and [PERSON] regarding Chinese and Ethiopian historical cities, respectively [27; 28]. However, both studies focus on urban expansion rather than intra-urban land-use changes. From a different standpoint, this study deals with intra-urban shifts from \"historical\" to new land uses. In this way the research explores the applicability and usefulness of this type of study to urban heritage studies. Simulation models consider two key phases. \begin{table}
\begin{tabular}{c l l l l} \hline \hline
**Approach** & **Article** & **Model** & **Scale** & **Resolution** \\ \hline \multirow{6}{*}{Land-Use Change} & [12] & WoE-MCDA & Region & 5 m \\  & [13] & RF-CA & Intra-urban & 10 m \\  & [14] & RF-CA & Intra-urban & 30 m \\  & [15] & ANN-CA & Intra-urban & 50 m \\  & [16] & RF-MCDA-Fuzzy & Region & 30 m \\  & [17] & CA-ANN-Fuzzy & Region & 30 m \\  & [18] & Patch-LR-CA & Region & 30 m \\ \hline \multirow{6}{*}{Urban Growth} & [19] & AIS-CA & Region & 30 m \\  & [20] & LR-MC-CA & Region & 30 m \\  & [21] & LR-CA & Region & 25 m \\  & [22] & GRADIANTE-CA & National & 100 m \\ \hline \multirow{6}{*}{Both} & [23] & CA-Sleuth & Region & 45 m \\  & [24] & MLC-MC-CA-MPNNMC & Intra-urban & 10 m \\  & [25] & MC-CA & Intra-urban & 30 m \\  & [26] & MC-CA & Region & 10 m \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Urban simulation models. The first phase is the calculation of a probability matrix using methods such as weights of evidence (WoE) [12], Random Forest (RF) [13; 14], Logistic Regression (LR) [18; 21; 29] and Artificial Neural Networks (ANNs) [17; 18; 30]. The weights of evidence are used to analyze the impact of the factors through hypothesis testing [31]. The second phase is running the simulation using cellular automata (CA) [13; 14; 15; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26], Markov Chains (MCs) [25] and fuzzy [16; 17]. This study aims to analyze the dynamics of land-use change and conservation of historical heritage trends and their drivers in an industrial district by using exploratory spatial data analysis techniques and the Dinamica EGO model [32]. In the case study of the Bellavista neighborhood of Tome (Chile), we propose a methodology based on three phases. In the first phase, the changes in land use were digitally mapped: between 1970 and 1992, and between 1993 and 2019. (The first period reflects the changes after the industry's decline. The second period reflects the moment when the first tensions emerged between the objectives of heritage protection and those of urban renewal.) In the second phase, an exploratory analysis of the rates of change was made, to obtain the main trends. Finally, a simulation model was created and validated in the third phase to project the neighborhood's land uses for 2030. The main contributions of the work are as follows:  
* Historical study of the neighborhood and identification of the main milestones behind land-use change that affect heritage conservation. * Creation of digital maps of the Bellavista neighborhood, categorized by lots and for different years (1970, 1992 and 2019). * Exploratory analysis of the changes between the said periods. * Spatio-temporal prediction model to identify the drivers of change, and particularly their effect on the loss or conservation of the neighborhood's historical fabric. * Projection of the neighborhood's future land uses through the model to identify the change trends in the coming years. The rest of the article is organized as follows: Section 2 explains the methodology used, focusing primarily on the creation of the digital cartography, the exploratory data analysis techniques used and the main characteristics of the simulation model before defining the study case. Section 3 presents, in detail, the results obtained from each phase. Finally, the conclusions section summarizes the work and proposes relevant future lines of work.