Fig 1.
The overview of the GLSTM framework.
Which contains the Data Input Block, Tweet Extraction Block, Graph Convolution with LSTM Block, and Output Visualization Block (on open map background: OpenStreetMap).
Fig 2.
A schema database relationship.
Which contains the description of T2D data types and POI data types.
Fig 3.
Example of temporal attributes and effective visit frequency for a given POI from a tweet record.
Fig 4.
Feature splicing process of Tweets and POIs.
LatandLng: coordinates, : category, X: node feature, T: time (T-th week), Sn: similarity, Dn: distance, f: visit frequency.
Fig 5.
The graph structure on the map tile with different categories (A1 and A2).
Where the POI denotes the node set and case categories are the edge information set of the GNN training.
Fig 6.
The structure of the LSTM module.
Which contains the forget gate Ft, input gate It, cell sate Ct, and output gate Ot.
Fig 7.
The T2D food access spatial weight matrix.
Where the value of each grid is utilized to evaluate the matching degree of POI feature and category cases on the map tile.
Table 1.
The spatial-temporal T2D food access category associated POI attributes prediction result with CNN-LSTM, CRNN, RNN-TS3WR, GNN-BiLSTM, and GLSTM training models.
Table 2.
The prediction results of our proposed GLSTM training model with the T2D food access categories in 7 States.
Fig 8.
A T2D risk prediction based on a case study of the New York State map visualization.