In the last decade, there has been great progress in the field of machine learning and deep learning. These models have been instrumental in addressing a great number of problems. However, they have struggled when it comes to dealing with high dimensional data. In recent years, representation learning models have proven to be quite efficient in addressing this problem as they are capable of capturing effective lower-dimensional representations of the data. However, most of the existing models are quite ineffective when it comes to dealing with high dimensional spatiotemporal data as they encapsulate complex spatial and temporal relationships that exist among real-world objects. High-dimensional spatiotemporal data of cities represent urban communities. By learning their social structure we can better quantitatively depict them and understand factors influencing rapid growth, expansion, and changes. In this paper, we propose a collective embedding framework that leverages the use of auto-encoders and Laplacian score to learn effective embeddings of spatiotemporal networks of urban communities. In addition, we also develop a weighted degree centrality measure for constructing spatiotemporal heterogeneous networks. To evaluate the performance of our proposed model, we implement it on real-world urban community data. Experimental results demonstrate the effectiveness of our model over state-of-the-art alternatives.