Spatiotemporal prediction of theft risk with deep inception-residual networks

Earth & Environmental Sciences

• crime 100%
• prediction 41%
• smart city 18%
• learning 11%
• safety 9%
• public 9%
• services 8%
• city 7%
• method 3%

Social Sciences

• larceny 90%
• offense 54%
• event 15%
• learning method 13%
• complaint 13%
• incident 11%
• time 4%

Engineering & Materials Science

• Crime 99%
• Smart city 15%
• Convolution 13%
• Deep learning 12%